Drawdown (economics)

The drawdown is the measure of the decline from a historical peak in some variable (typically the cumulative profit or total open equity of a financial trading strategy).[1]

Somewhat more formally, if {\textstyle X(t),\;t\geq 0} is a stochastic process with {\textstyle X(0)=0}, the drawdown at time {\displaystyle T}, denoted {\textstyle D(T)}, is defined as:

{\displaystyle D(T)=\max \left[\max _{t\in (0,T)}X(t)-X(T),0\right]\equiv \left[\max _{t\in (0,T)}X(t)-X(T)\right]_{+}}

The average drawdown (AvDD) up to time {\displaystyle T} is the time average of drawdowns that have occurred up to time {\displaystyle T}:

{\displaystyle \operatorname {AvDD} (T)={1 \over T}\int _{0}^{T}D(t)\,dt}

The maximum drawdown (MDD) up to time {\displaystyle T} is the maximum of the drawdown over the history of the variable. More formally, the MDD is defined as:

{\displaystyle \operatorname {MDD} (T)=\max _{\tau \in (0,T)}D(\tau )=\max _{\tau \in (0,T)}\left[\max _{t\in (0,\tau )}X(t)-X(\tau )\right]}

Pseudocode

The following pseudocode computes the Drawdown (“DD”) and Max Drawdown (“MDD”) of the variable “NAV”, the Net Asset Value of an investment. Drawdown and Max Drawdown are calculated as percentages:

MDD = 0
peak = -99999
for i = 1 to N step 1 do
    # peak will be the maximum value seen so far (0 to i), only get updated when higher NAV is seen
    if (NAV[i] > peak) then
        peak = NAV[i]
    end if
    DD[i] = 100.0 × (peak - NAV[i]) / peak
    # Same idea as peak variable, MDD keeps track of the maximum drawdown so far. Only get updated when higher DD is seen.
    if (DD[i] > MDD) then
        MDD = DD[i]
    end if
end for

Trading definitions

There are two main definitions of a drawdown:

1. How low it goes (the magnitude)

Putting it plainly, a drawdown is the “pain” period experienced by an investor between a peak (new highs) and subsequent valley (a low point before moving higher).[citation needed]
Next, the Maximum Drawdown, or more commonly referred to as Max DD. This is pretty much self explanatory, as the Max DD is the worst (the maximum) peak to valley loss since the investment’s inception.[citation needed]

In finance, the use of the maximum drawdown as an indicator of risk is particularly popular in the world of commodity trading advisors through the widespread use of three performance measures: the Calmar ratio, the Sterling ratio and the Burke ratio. These measures can be considered as a modification of the Sharpe ratio in the sense that the numerator is always the excess of mean returns over the risk-free rate while the standard deviation of returns in the denominator is replaced by some function of the drawdown.

2. How long it lasts (the duration)

The drawdown duration is the length of any peak to peak period, or the time between new equity highs.
The max drawdown duration is the worst (the maximum/longest) amount of time an investment has seen between peaks (equity highs).

Many assume Max DD Duration is the length of time between new highs during which the Max DD (magnitude) occurred. But that isn’t always the case. The Max DD duration is the longest time between peaks, period. So it could be the time when the program also had its biggest peak to valley loss (and usually is, because the program needs a long time to recover from the largest loss), but it doesn’t have to be.[citation needed]

When {\displaystyle X} is Brownian motion with drift, the expected behavior of the MDD as a function of time is known. If {\displaystyle X} is represented as:

{\displaystyle X(t)=\mu t+\sigma W(t)}

Where {\displaystyle W(t)} is a standard Wiener process, then there are three possible outcomes based on the behavior of the drift {\displaystyle \mu }:[2] 

  • {\displaystyle \mu >0} implies that the MDD grows logarithmically with time
  • {\displaystyle \mu =0} implies that the MDD grows as the square root of time
  • {\displaystyle \mu <0} implies that the MDD grows linearly with time

Banking or other finance definitions

Credit offered

Where an amount of credit is offered, a drawdown against the line of credit results in a debt (which may have associated interest terms if the debt is not cleared according to an agreement.)

Funds offered

Where funds are made available, such as for a specific purpose, drawdowns occur if the funds – or a portion of the funds – are released when conditions are met.

Optimization of drawdown

A passing glance at the mathematical definition of drawdown suggests significant difficulty in using an optimization framework to minimize the quantity, subject to other constraints; this is due to the non-convex nature of the problem. However, there is a way to turn the drawdown minimization problem into a linear program.[3][4]

The authors start by proposing an auxiliary function {\displaystyle \Delta _{\alpha }(x)}, where {\displaystyle x\in \mathbb {R} ^{p}} is a vector of portfolio returns, that is defined by:

{\displaystyle \Delta _{\alpha }(x)=\min _{\zeta }\left\{\zeta +{1 \over {(1-\alpha )T}}\int _{0}^{T}[D(x,t)-\zeta ]_{+}\,dt\right\}}

They call this the conditional drawdown-at-risk (CDaR); this is a nod to conditional value-at-risk (CVaR), which may also be optimized using linear programming. There are two limiting cases to be aware of: 

  • {\textstyle \lim _{\alpha \rightarrow 0}\Delta _{\alpha }(x)} is the average drawdown
  • {\textstyle \lim _{\alpha \rightarrow 1}\Delta _{\alpha }(x)} is the maximum drawdown

See also

  • Linear programming
  • Risk measure
  • Risk return ratio

References

  1. ^ “What Is A Drawdown? – Fidelity”. www.fidelity.com. Retrieved 2019-08-04.
  2. ^ Magdon-Ismail, Malik; Atiya, Amir F.; Pratap, Amrit; Abu-Mostafa, Yaser S. (2004). “On the Maximum Drawdown of a Brownian Motion” (PDF)Journal of Applied Probability41 (1): 147–161. doi:10.1239/jap/1077134674.
  3. ^ Chekhlov, Alexei; Uryasev, Stanislav; Zabarankin, Michael (2003). “Portfolio Optimization with Drawdown Constraints” (PDF).
  4. ^ Chekhlov, Alexei; Uryasev, Stanislav; Zabarankin, Michael (2005). “Drawdown Measure in Portfolio Optimization” (PDF)International Journal of Theoretical and Applied Finance8 (1): 13–58. doi:10.1142/S0219024905002767.

Arrow–Debreu model

In mathematical economics, the Arrow–Debreu model suggests that under certain economic assumptions (convex preferences, perfect competition, and demand independence) there must be a set of prices such that aggregate supplies will equal aggregate demands for every commodity in the economy.[1]

The model is central to the theory of general (economic) equilibrium and it is often used as a general reference for other microeconomic models. It is named after Kenneth Arrow, Gérard Debreu,[2] and sometimes also Lionel W. McKenzie for his independent proof of equilibrium existence in 1954[3] as well as his later improvements in 1959.[4][5]

The A-D model is one of the most general models of competitive economy and is a crucial part of general equilibrium theory, as it can be used to prove the existence of general equilibrium (or Walrasian equilibrium) of an economy. In general, there may be many equilibria; however, with extra assumptions on consumer preferences, namely that their utility functions be strongly concave and twice continuously differentiable, a unique equilibrium exists. With weaker conditions, uniqueness can fail, according to the Sonnenschein–Mantel–Debreu theorem.

Convex sets and fixed points

Picture of the unit circle

A quarter turn of the convex unit disk leaves the point (0,0) fixed but moves every point on the non–convex unit circle.

In 1954, McKenzie and the pair Arrow and Debreu independently proved the existence of general equilibria by invoking the Kakutani fixed-point theorem on the fixed points of a continuous function from a compact, convex set into itself. In the Arrow–Debreu approach, convexity is essential, because such fixed-point theorems are inapplicable to non-convex sets. For example, the rotation of the unit circle by 90 degrees lacks fixed points, although this rotation is a continuous transformation of a compact set into itself; although compact, the unit circle is non-convex. In contrast, the same rotation applied to the convex hull of the unit circle leaves the point (0,0) fixed. Notice that the Kakutani theorem does not assert that there exists exactly one fixed point. Reflecting the unit disk across the y-axis leaves a vertical segment fixed, so that this reflection has an infinite number of fixed points.

Non-convexity in large economies

The assumption of convexity precluded many applications, which were discussed in the Journal of Political Economy from 1959 to 1961 by Francis M. Bator, M. J. Farrell, Tjalling Koopmans, and Thomas J. Rothenberg.[6] Ross M. Starr (1969) proved the existence of economic equilibria when some consumer preferences need not be convex.[6] In his paper, Starr proved that a “convexified” economy has general equilibria that are closely approximated by “quasi-equilbria” of the original economy; Starr’s proof used the Shapley–Folkman theorem.[7]

Economics of uncertainty: insurance and finance

Compared to earlier models, the Arrow–Debreu model radically generalized the notion of a commodity, differentiating commodities by time and place of delivery. So, for example, “apples in New York in September” and “apples in Chicago in June” are regarded as distinct commodities. The Arrow–Debreu model applies to economies with maximally complete markets, in which there exists a market for every time period and forward prices for every commodity at all time periods and in all places.[citation needed]

The Arrow–Debreu model specifies the conditions of perfectly competitive markets.

In financial economics the term “Arrow–Debreu” is most commonly used with reference to an Arrow–Debreu security. A canonical Arrow–Debreu security is a security that pays one unit of numeraire if a particular state of the world is reached and zero otherwise (the price of such a security being a so-called “state price”). As such, any derivatives contract whose settlement value is a function on an underlying whose value is uncertain at contract date can be decomposed as linear combination of Arrow–Debreu securities.

Since the work of Breeden and Lizenberger in 1978,[8] a large number of researchers have used options to extract Arrow–Debreu prices for a variety of applications in financial economics.[9]

See also

  • Model (economics)
  • Incomplete markets
  • Fisher market – a simpler market model, in which the total quantity of each product is given, and each buyer comes only with a monetary budget.

References

  1. ^ Arrow, K. J.; Debreu, G. (1954). “Existence of an equilibrium for a competitive economy”. Econometrica22 (3): 265–290. doi:10.2307/1907353. JSTOR 1907353.
  2. ^ EconomyProfessor.com Archived 2010-01-31 at the Wayback Machine, Retrieved 2010-05-23
  3. ^ McKenzie, Lionel W. (1954). “On Equilibrium in Graham’s Model of World Trade and Other Competitive Systems”. Econometrica22(2): 147–161. doi:10.2307/1907539. JSTOR 1907539.
  4. ^ McKenzie, Lionel W. (1959). “On the Existence of General Equilibrium for a Competitive Economy”. Econometrica27 (1): 54–71. doi:10.2307/1907777. JSTOR 1907777.
  5. ^ For an exposition of the proof, see Takayama, Akira (1985). Mathematical Economics (2nd ed.). London: Cambridge University Press. pp. 265–274. ISBN 978-0-521-31498-5.
  6. Jump up to:a b Starr, Ross M. (1969), “Quasi–equilibria in markets with non–convex preferences (Appendix 2: The Shapley–Folkman theorem, pp. 35–37)”, Econometrica37 (1): 25–38, CiteSeerX 10.1.1.297.8498, doi:10.2307/1909201, JSTOR 1909201.
  7. ^ Starr, Ross M. (2008). “Shapley–Folkman theorem”. In Durlauf, Steven N.; Blume, Lawrence E. (eds.). The New Palgrave Dictionary of Economics4 (Second ed.). Palgrave Macmillan. pp. 317–318. doi:10.1057/9780230226203.1518. ISBN 978-0-333-78676-5.
  8. ^ Breeden, Douglas T.; Litzenberger, Robert H. (1978). “Prices of State-Contingent Claims Implicit in Option Prices”. Journal of Business51 (4): 621–651. doi:10.1086/296025. JSTOR 2352653.
  9. ^ Almeida, Caio; Vicente, José (2008). “Are interest rate options important for the assessment of interest risk?” (PDF)Working Papers Series n. 179, Central Bank of Brazil.

Short-rate model

short-rate model, in the context of interest rate derivatives, is a mathematical model that describes the future evolution of interest rates by describing the future evolution of the short rate, usually written {\displaystyle r_{t}\,}.

The short rate

Under a short rate model, the stochastic state variable is taken to be the instantaneous spot rate.[1] The short rate, {\displaystyle r_{t}\,}, then, is the (continuously compounded, annualized) interest rate at which an entity can borrow money for an infinitesimally short period of time from time {\displaystyle t}. Specifying the current short rate does not specify the entire yield curve. However, no-arbitrage arguments show that, under some fairly relaxed technical conditions, if we model the evolution of {\displaystyle r_{t}\,} as a stochastic process under a risk-neutral measure {\displaystyle Q}, then the price at time {\displaystyle t} of a zero-coupon bond maturing at time {\displaystyle T} with a payoff of 1 is given by

{\displaystyle P(t,T)=\operatorname {E} ^{Q}\left[\left.\exp {\left(-\int _{t}^{T}r_{s}\,ds\right)}\right|{\mathcal {F}}_{t}\right],}

where {\displaystyle {\mathcal {F}}} is the natural filtration for the process. The interest rates implied by the zero coupon bonds form a yield curve, or more precisely, a zero curve. Thus, specifying a model for the short rate specifies future bond prices. This means that instantaneous forward rates are also specified by the usual formula

{\displaystyle f(t,T)=-{\frac {\partial }{\partial T}}\ln(P(t,T)).}

Particular short-rate models

Throughout this section {\displaystyle W_{t}\,} represents a standard Brownian motion under a risk-neutral probability measure and {\displaystyle dW_{t}\,} its differential. Where the model is lognormal, a variable {\displaystyle X_{t}} is assumed to follow an Ornstein–Uhlenbeck process and {\displaystyle r_{t}\,} is assumed to follow {\displaystyle r_{t}=\exp {X_{t}}\,}.

One-factor short-rate models

Following are the one-factor models, where a single stochastic factor – the short rate – determines the future evolution of all interest rates. Other than Rendleman–Bartter and Ho–Lee, which do not capture the mean reversion of interest rates, these models can be thought of as specific cases of Ornstein–Uhlenbeck processes. The Vasicek, Rendleman–Bartter and CIR models have only a finite number of free parameters and so it is not possible to specify these parameter values in such a way that the model coincides with observed market prices (“calibration”). This problem is overcome by allowing the parameters to vary deterministically with time.[2][3] In this way, Ho-Lee and subsequent models can be calibrated to market data, meaning that these can exactly return the price of bonds comprising the yield curve. The implementation is usually via a (binomial) short rate tree [4] or simulation; see Lattice model (finance) § Interest rate derivatives and Monte Carlo methods for option pricing.

  1. Merton’s model (1973) explains the short rate as {\displaystyle r_{t}=r_{0}+at+\sigma W_{t}^{*}}: where {\displaystyle W_{t}^{*}} is a one-dimensional Brownian motion under the spot martingale measure.[5]
  2. The Vasicek model (1977) models the short rate as {\displaystyle dr_{t}=(\theta -\alpha r_{t})\,dt+\sigma \,dW_{t}}; it is often written {\displaystyle dr_{t}=a(b-r_{t})\,dt+\sigma \,dW_{t}}.[6]
  3. The Rendleman–Bartter model (1980) explains the short rate as {\displaystyle dr_{t}=\theta r_{t}\,dt+\sigma r_{t}\,dW_{t}}.[7]
  4. The Cox–Ingersoll–Ross model (1985) supposes {\displaystyle dr_{t}=(\theta -\alpha r_{t})\,dt+{\sqrt {r_{t}}}\,\sigma \,dW_{t}}, it is often written {\displaystyle dr_{t}=a(b-r_{t})\,dt+{\sqrt {r_{t}}}\,\sigma \,dW_{t}}. The {\displaystyle \sigma {\sqrt {r_{t}}}} factor precludes (generally) the possibility of negative interest rates.[8]
  5. The Ho–Lee model (1986) models the short rate as {\displaystyle dr_{t}=\theta _{t}\,dt+\sigma \,dW_{t}}.[9]
  6. The Hull–White model (1990)—also called the extended Vasicek model—posits {\displaystyle dr_{t}=(\theta _{t}-\alpha r_{t})\,dt+\sigma _{t}\,dW_{t}}. In many presentations one or more of the parameters {\displaystyle \theta ,\alpha } and {\displaystyle \sigma } are not time-dependent. The model may also be applied as lognormal. Lattice-based implementation is usually trinomial.[10][11]
  7. The Black–Derman–Toy model (1990) has {\displaystyle d\ln(r)=[\theta _{t}+{\frac {\sigma ‘_{t}}{\sigma _{t}}}\ln(r)]dt+\sigma _{t}\,dW_{t}} for time-dependent short rate volatility and {\displaystyle d\ln(r)=\theta _{t}\,dt+\sigma \,dW_{t}} otherwise; the model is lognormal.[12]
  8. The Black–Karasinski model (1991), which is lognormal, has {\displaystyle d\ln(r)=[\theta _{t}-\phi _{t}\ln(r)]\,dt+\sigma _{t}\,dW_{t}}.[13] The model may be seen as the lognormal application of Hull–White;[14] its lattice-based implementation is similarly trinomial (binomial requiring varying time-steps).[4]
  9. The Kalotay–Williams–Fabozzi model (1993) has the short rate as {\displaystyle d\ln(r_{t})=\theta _{t}\,dt+\sigma \,dW_{t}}, a lognormal analogue to the Ho–Lee model, and a special case of the Black–Derman–Toy model.[15] This approach is effectively similar to “the original Salomon Brothers model” (1987),[16] also a lognormal variant on Ho-Lee.[17]

Multi-factor short-rate models

Besides the above one-factor models, there are also multi-factor models of the short rate, among them the best known are the Longstaff and Schwartz two factor model and the Chen three factor model (also called “stochastic mean and stochastic volatility model”). Note that for the purposes of risk management, “to create realistic interest rate simulations”, these multi-factor short-rate models are sometimes preferred over One-factor models, as they produce scenarios which are, in general, better “consistent with actual yield curve movements”.[18]

  • The Longstaff–Schwartz model (1992) supposes the short rate dynamics are given by
{\displaystyle {\begin{aligned}dX_{t}&=(a_{t}-bX_{t})\,dt+{\sqrt {X_{t}}}\,c_{t}\,dW_{1t},\\[3pt]dY_{t}&=(d_{t}-eY_{t})\,dt+{\sqrt {Y_{t}}}\,f_{t}\,dW_{2t},\end{aligned}}}
where the short rate is defined as

{\displaystyle dr_{t}=(\mu X+\theta Y)\,dt+\sigma _{t}{\sqrt {Y}}\,dW_{3t}.}[19]
  • The Chen model (1996) which has a stochastic mean and volatility of the short rate, is given by
{\displaystyle {\begin{aligned}dr_{t}&=(\theta _{t}-\alpha _{t})\,dt+{\sqrt {r_{t}}}\,\sigma _{t}\,dW_{t},\\[3pt]d\alpha _{t}&=(\zeta _{t}-\alpha _{t})\,dt+{\sqrt {\alpha _{t}}}\,\sigma _{t}\,dW_{t},\\[3pt]d\sigma _{t}&=(\beta _{t}-\sigma _{t})\,dt+{\sqrt {\sigma _{t}}}\,\eta _{t}\,dW_{t}.\end{aligned}}}[20]

Other interest rate models

The other major framework for interest rate modelling is the Heath–Jarrow–Morton framework (HJM). Unlike the short rate models described above, this class of models is generally non-Markovian. This makes general HJM models computationally intractable for most purposes. The great advantage of HJM models is that they give an analytical description of the entire yield curve, rather than just the short rate. For some purposes (e.g., valuation of mortgage backed securities), this can be a big simplification. The Cox–Ingersoll–Ross and Hull–White models in one or more dimensions can both be straightforwardly expressed in the HJM framework. Other short rate models do not have any simple dual HJM representation.

The HJM framework with multiple sources of randomness, including as it does the Brace–Gatarek–Musiela model and market models, is often preferred for models of higher dimension.

See also

  • Fixed-income attribution

References

  1. ^ Short rate models, Prof. Andrew Lesniewski, NYU
  2. ^ An Overview of Interest-Rate Option Models Archived2012-04-06 at the Wayback Machine, Prof. Farshid Jamshidian, University of Twente
  3. ^ Continuous-Time Short Rate Models Archived 2012-01-23 at the Wayback Machine, Prof Martin Haugh, Columbia University
  4. Jump up to:a b Binomial Term Structure Models, Mathematica in Education and Research, Vol. 7 No. 3 1998. Simon Benninga and Zvi Wiener.
  5. ^ Merton, Robert C. (1973). “Theory of Rational Option Pricing”. Bell Journal of Economics and Management Science4 (1): 141–183. doi:10.2307/3003143. hdl:1721.1/49331. JSTOR 3003143.
  6. ^ Vasicek, Oldrich (1977). “An Equilibrium Characterisation of the Term Structure”. Journal of Financial Economics5 (2): 177–188. CiteSeerX 10.1.1.456.1407. doi:10.1016/0304-405X(77)90016-2.
  7. ^ Rendleman, R.; Bartter, B. (1980). “The Pricing of Options on Debt Securities”. Journal of Financial and Quantitative Analysis15(1): 11–24. doi:10.2307/2979016. JSTOR 2979016.
  8. ^ Cox, J.C., J.E. Ingersoll and S.A. Ross (1985). “A Theory of the Term Structure of Interest Rates”. Econometrica53 (2): 385–407. doi:10.2307/1911242. JSTOR 1911242.
  9. ^ T.S.Y. Ho and S.B. Lee (1986). “Term structure movements and pricing interest rate contingent claims”. Journal of Finance41 (5): 1011–1029. doi:10.2307/2328161. JSTOR 2328161.
  10. ^ John Hull and Alan White (1990). “Pricing interest-rate derivative securities”. Review of Financial Studies3 (4): 573–592. doi:10.1093/rfs/3.4.573.
  11. ^ Markus Leippold and Zvi Wiener (2004). “Efficient Calibration of Trinomial Trees for One-Factor Short Rate Models” (PDF)Review of Derivatives Research7 (3): 213–239. CiteSeerX 10.1.1.203.4729. doi:10.1007/s11147-004-4810-8.
  12. ^ Black, F.; Derman, E.; Toy, W. (1990). “A One-Factor Model of Interest Rates and Its Application to Treasury Bond Options”(PDF)Financial Analysts Journal: 24–32. Archived from the original (PDF) on 2008-09-10.
  13. ^ Black, F.; Karasinski, P. (1991). “Bond and Option pricing when Short rates are Lognormal”. Financial Analysts Journal47 (4): 52–59. doi:10.2469/faj.v47.n4.52.
  14. ^ Short Rate Models[permanent dead link], Professor Ser-Huang Poon, Manchester Business School
  15. ^ Kalotay, Andrew J.; Williams, George O.; Fabozzi, Frank J.(1993). “A Model for Valuing Bonds and Embedded Options”. Financial Analysts Journal49 (3): 35–46. doi:10.2469/faj.v49.n3.35.
  16. ^ Kopprasch, Robert (1987). “Effective duration of callable bonds: the Salomon Brothers term structure-based option pricing model”. Salomon Bros. OCLC 16187107.
  17. ^ See pg 218 in Tuckman, Bruce & Angel Serrat (2011). Fixed Income Securities: Tools for Today’s Markets. Hoboken, NJ: Wiley. ISBN 978-0470891698.
  18. ^ Pitfalls in Asset and Liability Management: One Factor Term Structure Models, Dr. Donald R. van Deventer, Kamakura Corporation
  19. ^ Longstaff, F.A. and Schwartz, E.S. (1992). “Interest Rate Volatility and the Term Structure: A Two-Factor General Equilibrium Model” (PDF)Journal of Finance47 (4): 1259–82. doi:10.1111/j.1540-6261.1992.tb04657.x.
  20. ^ Lin Chen (1996). “Stochastic Mean and Stochastic Volatility — A Three-Factor Model of the Term Structure of Interest Rates and Its Application to the Pricing of Interest Rate Derivatives”. Financial Markets, Institutions & Instruments5: 1–88.

Monetary Policy Committee (United Kingdom)

Monetary Policy Committee
UK interest rates, May 1997 to present.svg

Interest rates since the Committee’s inception
Formation May 1997
Purpose Determining monetary policy
Chairman
Andrew Bailey (ex officio)
Parent organization
Bank of England
Staff
9
Website Monetary Policy Committee

The Monetary Policy Committee (MPC) is a committee of the Bank of England, which meets for three and a half days, eight times a year, to decide the official interest rate in the United Kingdom (the Bank of England Base Rate).

It is also responsible for directing other aspects of the government’s monetary policy framework, such as quantitative easing and forward guidance. The Committee comprises nine members, including the Governor of the Bank of England, and is responsible primarily for keeping the Consumer Price Index (CPI) measure of inflation close to a target set by the government, currently 2% per year (as of 2019).[1] Its secondary aim – to support growth and employment – was reinforced in March 2013.

Announced on 6 May 1997, only five days after that year’s General Election, and officially given operational responsibility for setting interest rates in the Bank of England Act 1998, the committee was designed to be independent of political interference and thus to add credibility to interest rate decisions. Each member has one vote, for which they are held to account: full minutes of each meeting are published alongside the committee’s monetary policy decisions, and members are regularly called before the Treasury Select Committee, as well as speaking to wider audiences at events during the year.

Purpose

The MPC are asked to keep the Consumer Price Index at 2% per year, a task it was successful in from its creation until 2007.

The committee is responsible for formulating the United Kingdom’s monetary policy,[2] most commonly via the setting of the rate at it which it lends to banks (officially the Bank of England Base Rate or BOEBR for short).[3] As laid out in law, decisions are made with a primary aim of price stability, defined by the government’s inflation target (2% per year on the Consumer Price Index as of 2016).[2] The target takes the form of a “point”, rather than the “band” used by the Treasury prior to 1997.[4] The secondary aim of the committee is to support the government’s economic policies, and help it meet its targets for growth and employment.[2] That secondary aim was reinforced by then Chancellor of the Exchequer George Osborne in his March 2013 budget, with the MPC given more discretion to more openly “trade off” above-rate inflation in the medium run to boost other economic indicators.[5] The MPC is not responsible for fiscal policy, which is handled by the Treasury itself,[4] but is briefed by the Treasury about fiscal policy developments at meetings.[3]

Under the Bank of England Act 1998 the Bank’s Governor must write an open letter of explanation to the Chancellor of the Exchequer if inflation exceeds the target by more than one percentage point in either direction, and once every three months thereafter until prices are back within the allowed range. It should also set out what plans the Bank has for rectifying the problem, and how long it is expected to remain at those levels in the meantime.[2]

In January 2009 the Chancellor announced an Asset Purchase Facility (APF), to be administered by the MPC, aimed at ensuring greater liquidity in financial markets.[6] The committee had already started to cut rates the previous autumn, but the effect of such changes can take up to two years and rates cannot go below zero. By March 2009, faced with very low levels on inflation and interest rates already at 0.5%, the MPC voted to start the process of quantitative easing (QE) – the injection of money directly into the economy – via the APF. It had the Bank buy government bonds (gilts), along with a smaller amount of high-quality debt issued by private companies.[7] Although non-gilts initially made up a non-negligible part of the APF portfolio, as of May 2015 the entirety of the APF was held as gilts.[8] On 7 August 2013, Governor Mark Carney issued the committee’s first forward guidance as a third tool for controlling future inflation.[9]

Criticism of the MPC has centred on its predominant focus on inflation to the detriment of growth and employment,[2] although that criticism may have been mitigated by the March 2013 revisions to the committee’s remit. There have also been complaints about the reluctance of lenders to pass on rate changes,[10] and about the extent to which the introduction and management of QE have risked politicising the committee.[11]

History

Traditionally, the Treasury set interest rates. After reforms in 1992, officials held regular meetings and published minutes, but were not independent of government.[4] The result was a feeling that political factors were clouding what should be purely economic judgements on monetary policy.[10]

On 6 May 1997, operational responsibility to set interest rates was granted to the independent Bank of England by the Chancellor of the Exchequer, Gordon Brown.[10] Guidelines for the creation of a new “Monetary Policy Committee” were laid out in the Bank of England Act 1998. The Act also set out the responsibilities of the MPC: it would meet monthly; its membership comprise the Governor, two Deputy Governors, two of the Bank’s Executive Directors and four members appointed by the Chancellor. It should publish minutes of all meetings within six weeks (in October 1998 the committee announced plans to publish far more quickly, after only one[12]). The Act gave the government responsibility for specifying its price stability target and growth and employment objectives at least annually.[13] The original inflation target the government set for the MPC was 2.5% per year on the RPI-X measure of inflation, but in 2003 this was changed to 2% on CPI.[4] The government reserved the right to instruct the Bank on what rate to set in times of emergency.[14]

The years 1998 to 2006 witnessed an unprecedented period of price stability – during which inflation stayed within a percentage point of the target – despite earlier predictions that it could sit outside the range forty or more percent of the time. A 2007 report produced for the Treasury Committee noted that the MPC’s independence of government “has reduced the scope for short-term political considerations to enter into the determination of interest rates”. The creation of the MPC, it said, brought with it “an immediate credibility gain”.[4] During this time, the MPC kept interest rates relatively stable between 3.5% and 7.5%.[15]

However, the financial crisis of 2007–08 ended this period of stability, and, on 16 April 2007, the governor (at that time Mervyn King), was obliged to write the first MPC open letter to the chancellor (Gordon Brown), explaining why the inflation had deviated from the target of 2% per year by more than one percentage point (3.1%).[16] By February 2013, he had had to write 14 such letters to chancellors.[17] Between October 2008 and March 2009 the base rate was cut six times to an all-time low of 0.5% in order to avoid deflation and spur growth. In March 2009, the MPC launched a programme of quantitative easing, initially injecting £75 billion into the economy.[18] By March 2010, it had also increased the amount of money set aside for quantitative easing to £200 billion,[19] a figure later increased by a further £75 billion in the months following October 2011.[20] The MPC announced two further £50 billion rounds of quantitative easing in February[21] and July 2012,[22] bringing the total to £375 billion whilst simultaneously keeping the base rate at 0.5%.[22] In March 2013, the Chancellor of the Exchequer, George Osborne, called on the MPC to follow its American counterpart (the Federal Reserve Board) in committing itself to keeping interest rates low for a prolonged period of time via appropriate forward guidance,[5] which it did on 7 August.[9]

These measures eventually proved insufficient to avoid deflation. Having taken over in August 2013, Governor Mark Carney wrote his first open letter in February 2015 to explain why inflation had fallen below 1% for the first time in the MPC’s history.[23] This was followed by deflation of 0.1% in April 2015, the first month of negative CPI growth since the 1960s, and triggering a second letter.[24] As of February 2015, Carney has written five such letters.[25] Following the UK’s vote to leave the European Union in June 2016, the MPC cut the base rate from 0.5% to 0.25%, the first change since March 2009.[26] At the same time, it announced a further round of quantitative easing, valued at £60 billion, bringing the total to £435 billion.[26]

In December 2014, the Bank adopted the recommendations of a report prepared by Kevin Warsh aimed at improving the transparency of the committee’s decision making processes.[27]

Composition

Following a reshuffle in April 2014, the committee currently comprises:[3]

  • The Governor of the Bank
  • The three Deputy Governors for Monetary Policy, Financial Stability and Markets and Banking
  • The Bank’s Chief Economist
  • Four external members, appointed by the Chancellor of the Exchequer for a renewable three-year term

Each member has one vote of equal weight,[3] for which they can be held publicly accountable.[4] The Governor chairs the meeting and is the last to cast a vote, acting as a casting vote in event of a tie.[28] Representatives from the Treasury may attend the meeting, but only as non-voting observers.[3]

Meetings

The MPC meets eight times a year,[3] including four joint meetings with the Financial Policy Committee.[27] After a half-day “pre-MPC meeting”, usually the Wednesday before, meetings are held over three days, typically a Thursday, Monday and Wednesday.[3] Prior to the implementation of the reforms recommended by Kevin Warsh, meetings were generally held monthly on the Wednesday and Thursday following the first Monday of the month, although this was sometimes deviated from. In 2010, for example, the meeting was postponed from the 5/6 to the 7/10 May in order to avoid conflicting with the general election schedule for the 6th.[29] The May 2015 meeting was similarly delayed.[30]

On the first day of the three, the Committee studies data relating to the UK economy, as well as the worldwide economy, presented by the Bank’s economists and regional representatives, and topics for discussion are identified and addressed.[3] The second day consists of a main policy discussion during which MPC members explain their personal views and debate the correct course of action.[3] The Governor chooses the policy most likely to command a majority and, on the third day of the meeting, a vote is taken; each member gets one vote.[3] Those in the minority are asked to give the action they would have preferred.[3] The committee’s decisions are announced at noon the day after the meeting has concluded.[28] Following a procedural change in 2015, minutes of each meeting (including the policy preference of each member) are published on the Bank’s website at the same time as any decision is announced, resulting in a “Super Thursday” effect.[31] Prior to August 2015, the committee’s decisions were published at noon on the final day of the meeting, but there was a two-week delay before any minutes were published.[32] Starting with the March 2015 meeting, full transcripts of meetings will also be published, albeit after an eight-year delay.[27]

Outside of meetings, members of the MPC can be called upon by Parliament to answer questions regarding their decisions, via parliamentary committee meetings, often those of the Treasury Committee. MPC members also speak to audiences throughout the country, with the same aim. Their views and expectations for inflation are also republished in the Bank’s quarterly inflation report.[3]

Membership

As of January 2020, the current Committee comprises:[33]

  • Andrew Bailey (16 March 2020 to 15 March 2028, Governor)
  • Ben Broadbent (1 July 2014 – 30 June 2024, Deputy Governor for Monetary Policy)
  • Dave Ramsden (1 September 2017 – 31 August 2022, Deputy Governor for Markets and Banking)
  • Jon Cunliffe (1 November 2013 – 31 October 2023, Deputy Governor for Financial Stability)
  • Andy Haldane (1 June 2014 – 11 June 2023, Chief Economist and Executive Director, Monetary Analysis & Research)
  • Jonathan Haskel (1 September 2018 – 31 August 2021, external member)
  • Michael Saunders (9 August 2016 – 9 August 2022, external member)
  • Gertjan Vlieghe (1 September 2015 – 31 August 2021, external member)
  • Silvana Tenreyro (5 July 2017 – 4 July 2023, external member)

Other, former members of the committee by date of appointment are:

  • Sir Edward George (June 1997 – June 2003)
  • Howard Davies (June – July 1997)
  • Willem Buiter (June 1997 – May 2000)
  • Charles Goodhart (June 1997 – May 2000)
  • Ian Plenderleith (June 1997 – May 2002)
  • Mervyn King (June 1997 – 30 June 2013)
  • DeAnne Julius (September 1997 – May 2001)
  • David Clementi (September 1997 – August 2002)
  • Sir Alan Budd (December 1997 – May 1999)
  • Sir John Vickers (June 1998 – September 2000)
  • Sushil Wadhwani (June 1999 – May 2002)
  • Christopher Allsopp (June 2000 – May 2003)
  • Stephen Nickell (June 2000 – 31 May 2006)
  • Charles Bean (October 2000 – 30 June 2014)
  • Kate Barker (June 2001 – 31 May 2010)
  • Marian Bell (June 2002 – June 2005)
  • Paul Tucker (June 2002 – 20 October 2013)
  • Sir Andrew Large (September 2002 – January 2006)
  • Richard Lambert (June 2003 – March 2006)
  • Rachel Lomax (1 July 2003 – 30 June 2008)
  • David Walton (1 July 2005 – 21 June 2006)
  • Sir John Gieve (16 January 2006 – March 2009)
  • David Blanchflower (1 June 2006 – 31 May 2009)
  • Tim Besley (1 September 2006 – 31 August 2009)
  • Andrew Sentance (1 October 2006 – 31 May 2011)
  • Spencer Dale (1 July 2008 – 31 May 2014)
  • Paul Fisher (1 March 2009 – 31 July 2014)
  • David Miles (1 June 2009 – 31 August 2015)
  • Adam Posen (1 September 2009 – 31 August 2012)
  • Martin Weale (1 August 2010 – 31 July 2016)
  • Kristin Forbes (1 July 2014 – 30 June 2017)
  • Nemat Shafik (1 August 2014 – 28 February 2017)
  • Charlotte Hogg (1 March 2017 – 28 April 2017)
  • Ian McCafferty (1 September 2012 to 31 August 2018)

The dates listed show when their current terms of appointment are due to, or did, end.

As of January 2008, Mervyn King, the Bank of England’s then Governor, was the only MPC member to have taken part in every meeting since 1997.[34] As a result, after the MPC meeting in July 2013, the first after King retired, no single member had attended every meeting. As of 2016, Kate Barker is the only external member to date to have been appointed for three terms, each lasting three years.[35]

See also

  • Federal Open Market Committee, the equivalent structure in the United States’ Federal Reserve System

References

  1. ^ Bank of England, Inflation and the 2% Target, website updated 10 May 2019. Retrieved 27 December 2019
  2. Jump up to:a b c d e Parkin, Michael; Powell, Melanie; Matthews, Kent (2007). Economics. Addison-Wesley. pp. 642–44. ISBN 978-0-13-204122-5.
  3. Jump up to:a b c d e f g h i j k l “Monetary Policy Committee”. Bank of England. Retrieved 20 August 2017.
  4. Jump up to:a b c d e f Bank of England (2007). “Treasury Committee Inquiry into the Monetary Policy Committee of the Bank of England: Ten Years On” (PDF). The Stationery Office. Retrieved 17 April 2010.
  5. Jump up to:a b Monaghan, Angela (20 March 2013). “Budget 2013: Bank of England’s monetary policy remit changed”. Retrieved 1 April2013.
  6. ^ “Asset Purchase Facility”. Bank of England. Retrieved 17 April2010.
  7. ^ Quantitative Easing explained (PDF). Bank of England. ISBN 1-85730-114-5. Retrieved 17 April 2010.
  8. ^ “Asset Purchase Facility Results”. Bank of England. 21 February 2013. Retrieved 27 February 2013.
  9. Jump up to:a b “UK interest rates held until unemployment falls”. BBC News. 7 August 2013. Retrieved 7 August 2013.
  10. Jump up to:a b c “Monetary Policy Committee”. politics.co.uk. November 2008. Retrieved 17 April 2010.
  11. ^ “IEA’s Shadow Monetary Policy Committee votes to hold Bank Rate”. Institute for Economic Affairs. December 2012. Archived from the original on 23 July 2013. Retrieved 1 August 2013.
  12. ^ “Monetary Policy Committee Minutes”. Bank of England. Archived from the original on 24 March 2010. Retrieved 17 April2010.
  13. ^ “Governance”. Bank of England. Retrieved 17 April 2010.
  14. ^ Keegan, William (2003). The prudence of Mr Gordon Brown. Wiley. ISBN 978-0-470-84697-1.
  15. ^ “Bank of England Statistical Interactive Database”. Bank of England. Retrieved 12 September 2016.
  16. ^ “Rate hike fear as inflation jumps”. BBC News. 17 April 2007. Retrieved 17 April 2010.
  17. ^ Wallace, Tim (15 February 2012). “High inflation forces King to explain again”. City AM. Retrieved 27 February 2013.
  18. ^ “UK interest rates lowered to 0.5%”. BBC News. 5 March 2009. Retrieved 18 April 2010.
  19. ^ “UK interest rates remain at record low”. BBC News. 4 November 2010. Retrieved 19 November 2010.
  20. ^ “Bank of England Adds 75 billion to Quantitative Easing Program”. Central Bank News. 6 October 2011. Retrieved 31 January 2012.
  21. ^ Katie Allen (9 February 2012). “Bank of England injects £50bn into ailing economy”. The Guardian. Retrieved 27 March 2012.
  22. Jump up to:a b “Bank of England pumps £50bn more into economy”. BBC News. 5 July 2012. Retrieved 27 February 2013.
  23. ^ Szu Ping Chan and Denise Roland (12 February 2015). “UK heading for deflation says Bank of England Governor Mark Carney”. The Daily Telegraph. Retrieved 30 March 2015.
  24. ^ “UK inflation rate turns negative”. BBC News. 19 May 2015. Retrieved 19 May 2015.
  25. ^ “Bank of England cuts growth forecast”. BBC News. 4 February 2016. Retrieved 4 February 2016.
  26. Jump up to:a b “UK interest rates cut to 0.25%”. BBC News. 4 August 2016. Retrieved 12 September 2016.
  27. Jump up to:a b c “Bank of England announces measures to bolster transparency and accountability”. Monetary Policy Committee. 11 December 2014. Retrieved 6 August 2015.
  28. Jump up to:a b “Monetary Policy Committee Decisions”. Bank of England. April 2010. Retrieved 17 April 2010.
  29. ^ “Monetary Policy Committee dates for 2015 and provisional dates for 2016”. Bank of England. December 2014. Retrieved 6 August2015.
  30. ^ “UK interest rates kept at record low”. BBC News. 11 May 2015. Retrieved 19 May 2015.
  31. ^ “Bank votes 8-1 to keep UK rates at record low”. BBC News. 6 August 2015. Retrieved 6 August 2015.
  32. ^ Giles, Chris (26 May 2015). “Bank of England shake-up aims to boost transparency”. Financial Times. Retrieved 13 June 2015.
  33. ^ “Members of the Monetary Policy Committee (MPC)”. Bank of England. Retrieved 12 January 2021.
  34. ^ Litterick, David (31 January 2008). “Mervyn King faces a rocky road to stability”. The Daily Telegraph. Retrieved 17 April 2010.
  35. ^ House of Commons Treasury Committee (2007). The Monetary Policy Committee of the Bank of England: re-appointment hearing for Ms Kate Barker and Mr Charlie Bean (volume 2). The Stationery Office. ISBN 978-0-215-03607-0.

Forward curve

The forward curve is a function graph in finance that defines the prices at which a contract for future delivery or payment can be concluded today. For example, a futures contract forward curve is prices being plotted as a function of the amount of time between now and the expiry date of the futures contract (with the spot price being the price at time zero). The forward curve represents a term structure of prices.[1]

Forward interest rate

forward interest rate is a type of interest rate that is specified for a loan that will occur at a specified future date. As with current interest rates, forward interest rates include a term structure which shows the different forward rates offered to loans of different maturities. According to the unbiased expectations hypothesis, forward interest rates predict spot interest rates at the time the loan is actually made, but many analysts dispute whether this is true, as it ignores durational risk.[2]

This figure is part of the lending & credit industry and is related as well to the “expectations theory” which states that forward interest rates can be used as forecasts for future interest rates. Investors expecting higher short-term interest rates are more likely to buy bonds maturing in the short term. If they were to park money into a long term debt they might not be able to make as much interest.

Finance analysts can refer to a graph of forward interest rate values over different time periods, the forward curve, to evaluate the time value of money.

Price forward curve

Price forward curve (short PFC) reflects specialties of the commodity market such as:

  • Transporting commodities is costly and time-consuming.
  • It is costly to store commodities – power storage is often prohibitively expensive.
  • Many commodities show a strong seasonality, e.g. there is more natural gas demanded (for heating) in winter than in summer.

In order to fairly value and manage the profitability of energy products it is thus necessary to capture these seasonal price dynamics in a forward curve term-structure.

The contract duration of a futures contract is limited by definition and investors have to change their contract during the contract term. Price forward curves help to determine when to do that, two scenarios are possible:[3]

  1. If the PFC is ascending, i.e. future commodity-contracts will be more expensive than at the moment, this is called contango. The investor will have additional costs as they have to sell their futures to a lower price than what they have to invest for their new futures.
  2. If the PFC is descending, it is a so-called backwardation and investors will make money by exchanging (rolling) their old futures contracts to new ones.

Hourly price forward curve

An hourly price forward curve (HPFC) is the construction of a forward curve at a resolution exceeding that known to the market and is as such able to capture the seasonalities of the electricity spot prices. The construction of an HPFC can be based on the combination of two approaches. A statistical approach examines how spot prices have moved in the past. A fundamental model suggests that the price is set purely by supply and demand (respectively, fuel prices on the merit order curve, and load).[4]

References

  1. ^ von Schwintowski, Hans-Peter; Berlinghof, Britta, eds. (2010). Handbuch Energiehandel (in German) (2 ed.). Berlin: Erich Schmidt Verlag. p. 751. ISBN 978-3503154388.
  2. ^ “All Together Now: The Forward Curve is Not a Forecast”.
  3. ^ “Was sind Forward Curves?”. finanzen.net (in German). Karlsruhe, Germany: Axel Springer. Retrieved 13 January 2016.
  4. ^ Hildman, Marcus; Jeroen Cornel; Florian Herzog; Dejan Stokic; Göran Andersson. “Robust Calculation and Parameter Estimation of the Hourly Price Forward Curve”.

Corporate debt bubble

The corporate debt bubble is the large increase in corporate bonds, excluding that of financial institutions, following the financial crisis of 2007–08. Global corporate debt rose from 84% of gross world product in 2009 to 92% in 2019, or about $72 trillion.[1][2] In the world’s eight largest economies—the United States, China, Japan, the United Kingdom, France, Spain, Italy, and Germany—total corporate debt was about $51 trillion in 2019, compared to $34 trillion in 2009.[3] Excluding debt held by financial institutions—which trade debt as mortgages, student loans, and other instruments—the debt owed by non-financial companies in early March 2020 was $13 trillion worldwide, of which about $9.6 trillion was in the U.S.[4]

The corporate bond market historically centered in the United States.[5] The U.S. Federal Reserve noted in November 2019 that leveraged loans, corporate bonds made to companies with poor credit histories or large amounts of existing debt, were the fastest growing asset class, increasing in size by 14.6% in 2018 alone.[6] Total U.S. corporate debt in November 2019 reached a record 47% of the entire U.S. economy.[7][5] However, corporate borrowing expanded worldwide under the low interest rates of the Great Recession. Two-thirds of global growth in corporate debt occurred in developing countries, in particular China. The value of outstanding Chinese non-financial corporate bonds increased from $69 billion in 2007 to $2 trillion in 2017.[5] In December 2019, Moody’s Analytics described Chinese corporate debt as the “biggest threat” to the global economy.[8]

Regulators and investors have raised concern that large amounts of risky corporate debt have created a critical vulnerability for financial markets, in particular mutual funds, during the next recession.[7] Former Fed Chair Janet Yellen has warned that the large amount of corporate debt could “prolong” the next recession and cause corporate bankruptcies.[9] The Institute of International Finance forecast that, in an economic downturn half as severe as the 2008 crisis, $19 trillion in debt would be owed by non-financial firms without the earnings to cover the interest payments, referred to as zombie firms.[3] The McKinsey Global Institute warned in 2018 that the greatest risks would be to emerging markets such as China, India, and Brazil, where 25-30% of bonds had been issued by high-risk companies.[5]

Low bond yields led to purchase of riskier bonds

While issuance of new high-yield (junk) bonds in the US market has been declining since 2012, the issuance of BAA (the lowest rating of “investment-grade”, including BBB) matched or exceeded high-yield issuance from 2016 through 2019.

Following the financial crisis of 2007–08, the Federal Reserve Board lowered short- and long-term interest rates in order to convince investors to move out of interest-bearing assets and match with borrowers seeking capital. The resulting market liquidity was accomplished through two steps: cutting the Fed Funds rate, the rate that the Fed charges institutional investors to borrow money; and quantitative easing, whereby the Fed buying trillions of dollars of toxic assets, effectively creating functioning markets for these assets and reassuring investors. The success of the U.S. Fed in dropping interest rates to historically low levels and preventing illiquid markets from worsening the financial crisis prompted central banks around the world to copy these techniques.[10][11]

However, the effect of quantitative easing was not limited to the toxic mortgage bonds targeted by central banks, as it effectively reduced the supply of bonds as a class, causing prices for bonds generally to rise and bond yields to lower. For over a decade, the artificially low interest rates and artificially low bond yields, have caused a “mispricing of risk” as investors continually seek out higher yields. As an example, high-yield debt, colloquially known as “junk bonds”, has historically yielded 10% or more to compensate investors for the increased risk; in February 2020, the U.S. yield on these bonds dipped to nearly 5%.[10][12] This indicates that investors flocking to higher yield have bought so much high-yield debt that it has driven the yield below the level needed to compensate for the risk.[10] U.S. corporate bonds held by mutual funds had tripled over the previous decade.[13]

In June 2018, 22% of outstanding U.S. nonfinancial corporate debt was rated “junk”, and a further 40% was rated one step above junk at “BBB”, so that approximately two-thirds of all corporate debt was from companies at the highest risk of default, in particular retailers who were losing business to online services.[5] The U.S. Fed noted in November 2019 that mutual funds held about one-sixth of outstanding corporate debt, but were acquiring one-fifth of new leveraged corporate loans. The size of high-yield corporate bond mutual funds, which specialize in riskier bonds, had doubled in the decade prior to 2019.[13]

While trade in corporate bonds typically centered in the U.S., two-thirds of corporate debt growth since 2007 was in developing countries. China became one of the largest corporate bond markets in the world, with the value of Chinese corporate bonds increasing from $69 billion in 2007 to $2 trillion at the end of 2017.[5] By mid-2018, total outstanding U.S. corporate debt reached 45% of GDP, which was larger than that seen during the dot-com bubble and subprime mortgage crisis.[14] Noting negative bond yields in Switzerland, the United Kingdom, and the US in August 2019, Bloomberg News stated that effectively paying borrowers to borrow is distorting incentives and misallocating resources, concluding that bonds are on a bubble.[15]

Low interest rates led to increasingly leveraged companies

U.S. companies, in industry in particular, issued increasing amounts of bonds at the lowest tier of investment grade (known as BAA or BBB) after the 2008 financial crisis.

Companies that do not make enough profit to pay off their debts and are only able to survive by repeatedly refinancing their loans, known as “zombie firms”, have been able to turn over their debt because low interest rates increase the willingness of lenders to buy higher yield corporate debt, while the yield they offer on their bonds remains at near-historical lows. In a 2018 study of 14 rich countries, the Bank of International Settlements stated that zombie firms increased from 2% of all firms in the 1980s to 12% in 2016.[16] By March 2020, one-sixth of all publicly-traded companies in the U.S. did not make enough profit to cover the interest on their issued debt.[16] In developing countries, high-risk bonds were concentrated in particular industries. In China, one-third of bonds issued by industrial companies and 28% of those issued by real-estate companies are at a higher risk of default, defined as having a times interest earned of 1.5 or less. In Brazil, one-quarter of all corporate bonds at a higher risk of default are in the industrial sector.[5] Fitch stated in December 2019 that the majority of Chinese companies listed on A-share markets, namely the Shanghai Stock Exchange and Shenzhen Stock Exchange, were unable to repay their debt with their operational cash flow and required refinancing.[17]

Bond investing in Europe closely followed the actions of the European Central Bank, in particular the quantitative easing implemented in response to the European debt crisis. In June 2016, the ECB began using its corporate sector purchase programme (CSPP), acquiring 10.4 billion euro in non-financial corporate bonds in the first month of operation, with the explicit purpose of ensuring liquidity in the corporate bond market.[18] News in mid-2019 that the ECB would restart its asset purchase program pushed the iBoxx euro corporate bond index, valued at $1.92 trillion, to record highs.[19] The increased purchases resulted in 42% of European investment-grade corporate debt having a negative yield, as investors effectively paid less risky companies to borrow money.[20]

The Federal Reserve Bank of New York noted in January 2020 that only two U.S. firms had the highest rating of AAA, Johnson & Johnson and Microsoft, while there was an increased number of firms at the lowest-end, called BAA (on the Moody’s rating scale) or BBB (on the S&P rating scale). Investment-grade firms, those with a rating between AAA and BAA, were more highly leveraged than the high-yield (“junk”) firms. Observing that investors tend to divest bonds that are downgraded to high-yield, the New York Fed stated, “In the current corporate debt landscape, with a greater amount outstanding of BAA-rated corporate debt and higher net leverage of investment-grade debt overall, the possibility of a large volume of corporate bond downgrades poses a financial stability concern.”[21]

Examples of leveraged corporate debt transactions include:

  • Halliburton doubled its corporate debt to $11.5 billion between 2012 and 2020; it sold $1 billion in debt in early March 2020 with the explicit purpose of paying off existing debt and has $3.8 billion in debt payments due through 2026.[16]
  • AT&T debt ballooned to $180 billion following its acquisition of Time Warner in 2016. In 2018, Moody’s declared AT&T to be “beholden to the health of the capital markets” because of its reliance on continued credit to service its debt load.[10]
  • KKR sold about $1.3 billion of cov-lite debt in 2017 to pay for its buyout of Unilever, despite Moody’s rating the offer 4.99 on a scale of 1 to 5, with 5 being the riskiest.[10]
  • Kraft Heinz had its credit rating downgraded to BBB- or “junk” in February 2020 due to low earnings expectations and the firm’s determination to use available capital to provide stock dividends rather than pay down debt.[22] That month, Kraft Heinz had $22.9 billion in total debt with only $2.3 billion in cash assets.[23]

The S&P 500 cyclically adjusted price-to-earnings ratio (CAPE) suggest that the index reached an overvaluation in the late 2010s not seen since the collapse of the dot-com bubble.

Corporations in the United States have used the debt to finance share buybacks, dividends, and mergers & acquisitions that boost share price to a greater extent than before. This has been done in place of long-term business investments and expansions.[14] The U.S. Tax Cuts and Jobs Act of December 2017 offered a tax holiday under the logic that firms would use the extra profits to increase investments. Instead, it vastly increased an existing trend towards share buybacks, which increase the value of the remaining publicly traded shares and contributed to the rise of stock market indexes generally.[24] While the S&P 500 has risen by over 300% from its low in the Great Recession, this rise is driven partly by the selling of corporate debt to purchase stock that becomes more expensive due to the purchases. The cyclically adjusted price-to-earnings ratio for the S&P 500 indicates it is the most overvalued it has been since the dot-com bubble and is around Wall Street Crash of 1929 valuations.[14]

The McKinsey Global Institute cautioned in 2018 against excessive alarm, noting that if interest rates rose by 2%, less than 10% of bonds issued in all advanced economies would be at higher risk of default, with the percentage falling to less than 5% of European debt, which is largely issued by AAA-rated companies.[5]

Search for yield results in growth in covenant light bonds

Most leveraged corporate bonds are “cov-lite”, or covenant light, that do not contain the usual protections for purchasers of the debt. In some cases, cov-lite terms may force the purchaser of the debt to buy more debt.[25] By mid-2018, 77.4% of U.S. leveraged corporate loans were cov-lite.[26] Cov-lite loans as a percentage of outstanding leveraged loans in European markets reached 78% in 2018, compared to under 10% in 2013.[27] Investors seeking stronger covenants lost the struggle with companies and private equity firms seeking to offload risk to the buyers of their debt. A writer for Bloomberg News opined in February 2020, “If and when the credit cycle turns, the aggressive push toward weakening protections virtually ensures that recovery rates will be worse than in 2008. But there’s no going back now: The risky debt markets are full of cov-lite deals. Investors either have to acclimate to that reality or get out of high-yield and leveraged loans.”[28]

Chinese debt

The Chinese government’s reaction to the 2009 financial crisis was to direct banks to loan to Chinese state-owned enterprises (SOEs), which then built factories and equipment to stimulate the economy despite the lack of demand for the products created. The economic activity of SOEs in 2017 was 22% of China’s total GDP, though SOEs accounted for over half of China’s corporate debt. It is often not clear the degree to which Chinese SOEs are owned by the state, making it difficult to differentiate corporate and sovereign debt. Government-directed lending gradually shifted from large banks offering loans to smaller local and provincial banks offering lightly regulated wealth management products. This “shadow banking” sector grew from $80 billion in 2006 to almost $9 trillion in 2018.[29]

Vice-Premier Liu He is the chair of the Chinese Financial Stability and Development Committee

In 2017, the International Monetary Fund estimated that 15.5% of all commercial bank loans in China were made to firms that did not have an operational cash flow sufficient to cover the interest on the loans. A 60% default rate of these loans could result in losses equal to 7% of Chinese GDP. In 2017, both Moody’s and Standard & Poor’s Financial Services LLC downgraded China’s sovereign debt rating because of concerns about the health of the financial system.[29]

The Chinese government recognized the risk posed by corporate debt. The 13th Five-Year Plan, unveiled in 2015, included financial reforms to reduce capacity in highly leveraged sectors. There were a wide variety of other policies and restrictions implemented to reduce debt burdens and manage the failure of zombie firms. In 2017, the government established the Financial Stability and Development Committee, chaired by Vice-Premier Liu He, to coordinate financial regulation, with the full impact of new regulations expected in 2021.[29]

The China–United States trade war that began in 2018 forced the government to pause debt reduction efforts in order to emphasize stimulus as both domestic and global demand for Chinese products fell.[8] Government attempts to crack down on risky debt combined with the economic slowdown to quadruple the size of defaults on yuan-denominated bonds from 2017 to 2018.[30] The government subsequently encouraged banks to increase lending, in particular to small struggling firms. In the first half of 2019, local governments issued $316.5 billion in bonds.[31] In December 2019, both Moody’s Analytics and Fitch warned that Chinese debt was the biggest threat within the “fault line in the financial system and the broader economy” posed by overall corporate debt.[8] Fitch noted that 4.9% of Chinese private companies had defaulted on bond payments in first 11 months of 2019, compared to 0.6% in all of 2014.[17]

Potential role of corporate debt in a future recession

The amount of US corporate bonds held by mutual funds tripled in the decade following the financial crisis of 2007–08.

The Organisation for Economic Co-operation and Development noted in February 2020 that “today’s stock of outstanding corporate bonds has lower overall credit quality, higher payback requirements, longer maturities and inferior covenant protection” that “may amplify the negative effects that an economic downturn would have on the non-financial corporate sector and the overall economy”.[32] If the corporate debt bubble bursts, the bonds would be repriced, resulting in a massive loss by the mutual funds, high-yield funds, pension funds, and endowments with corporate bond assets. As with the 2008 crisis, this may result in increased caution by lenders and the shrinking of the entire bond market, resulting in higher rates for individual consumers for mortgages, car loans, and small-business loans.[10] The International Monetary Fund conducted a stress test for a hypothetical shock half as large as the 2008 crisis and found that $19 trillion of corporate debt from eight countries—China, the United States, Japan, the United Kingdom, France, Spain, Italy, and Germany—representing roughly 40% of all corporate debt would be at risk of default because it would be difficult for companies to raise cash to repay loans that come due.[33]

In contrast, other observers believed that a crisis could be averted, noting that banks are better capitalized and central banks more responsive than in the 2007-08 financial crisis. In 2019, the McKinsey Global Institute expressed doubt that defaults in the corporate debt market would result in systemic collapses like that caused by the subprime mortgage crisis.[5] On 12 March 2020, Kenneth Rogoff of Harvard University stated, “I don’t think we have anything shaping up like 2008 or 1929, particularly in the United States.”[4] Though he later revised as the situation worsened, stating on 30 March, “there is a good chance it will look as bad as anything over the last century and half.”[34]

Concern about COVID-19 related economic turmoil

Social distancing and other responses to the COVID-19 pandemic caused drops in economic activity and corporate revenues. Pictured: New York City’s Theater District largely deserted on a Saturday night in March 2020.

Several financial commentators expressed alarm at the economic fallout of the COVID-19 pandemic and related collapse of the agreement between OPEC and non-OPEC producers, particularly Russia, to prop up crude oil prices and resulting stock market crash during the week of 9 March 2020. The concern is that this economic instability may initiate the collapse of the corporate debt bubble.[16][35][36][37] The total economic debt owed by non-financial companies in early March was $13 trillion worldwide, of which about $9.6 trillion was in the U.S.[4] The Chief Investment Officer of Guggenheim Partners noted on 9 March 2020, “the overleveraged corporate sector [is] about to face the prospect that new-issue bond markets may seize up, as they did last week, and that even seemingly sound companies will find credit expensive or difficult to obtain … Our estimate is that there is potentially as much as a trillion dollars of high-grade bonds heading to junk. That supply would swamp the high yield market as it would double the size of the below investment grade bond market. That alone would widen [yield] spreads even without the effect of increasing defaults.”[38] At end of the trading day on 9 March the yield spread for junk bonds reached 6.68% from a low of 3.49% on 6 January, as sellers attempted to lure cautious traders with higher yields. The bonds of firms in the energy sector, who make up about 10% of the total junk bond market and were particularly exposed to the Saudi-Russian oil price war, suffered large yield spreads.[39][40][41] A debt default by energy companies would harm the regional banks of Texas and Oklahoma, potentially causing a chain reaction through the corporate bond market.[42]

On 12 March, the spread on junk bonds over U.S. Treasuries increased to 7.42% in U.S. markets, the highest level since December 2015, indicating less willingness to buy corporate debt. As the airline and oil industries faced dire consequences from the economic slowdown and the Russia–Saudi Arabia oil price war, investors became increasingly concerned that corporate bond fund managers dealing with redemption requests from clients would be forced to engage in forced liquidation, potentially prompting other investors to try to sell first, driving down the value of the bonds, and increasing the cash crunch on investors.[43] A concern is that companies, unable to cover their debt, will draw down their credit lines to banks, thereby reducing bank liquidity.[44] An example is Boeing, which declared on 11 March that it would draw down the entirety of a $13.825 billion line of credit meant to cover costs related to the Boeing 737 MAX groundings to “preserve cash”, resulting in an 18% drop in its stock.[45][46] While U.S. banks should have capacity to supply liquidity to companies due to post-2008 crisis regulations, analysts are concerned about funds holding bonds, which were also seeking to build cash reserves in anticipation of imminent client withdrawals during the economic turmoil.[44] In the week of 9 March, investors pulled a record $15.9 billion from investment-grade bond funds and $11.2 billion from high-yield bond funds, the second-highest on record.[43] As of 13 March, the market was pricing in about a 50% chance of recession, indicating future strain if a recession actually came to pass.[44]

European Central Bank President Christine Lagarde oversaw the creation of a Pandemic Emergency Purchase Programme on 19 March.

From 20 February to 16 March 2020, the yield of the iBoxx euro liquid high-yield index doubled. The market for new European junk-rated corporate debt, including leveraged debt, had effectively disappeared. Around 38 billion euros of debt is due by junk-rated corporate and financial issuers in European currencies by the end of 2021. Analysts were concerned that Eurozone companies vulnerable to the COVID-19 economic downturn, and with debt coming due over the next two years, would be unable to refinance their debt and would be forced to restructure.[47] One U.S. analyst on 16 March opined, “The longer the pandemic lasts, the greater the risk that the sharp downturn morphs into a financial crisis with zombie companies starting a chain of defaults just like subprime mortgages did in 2008.”[48] On 19 March, the European Central Bank announced a 750 billion euro ($820 billion) bond-buying program, called the Pandemic Emergency Purchase Programme, to calm European debt markets.[49][50] The PEPP and corporate sector purchase programme were authorized to buy non-financial commercial paper.[51]

Fed action soothe U.S. markets at the end of March

In the week of 23 March, investment-grade firms in the US issued $73 billion in debt, about 21% higher than the previous record set in 2013. These firms sought to build cash reserves prior to the full impact of the recession. For example, the retail-focused businesses Nike, Inc. and The Home Depot began the week borrowing $6 billion and $5 billion, respectively. Unusually, about 25% of the debt was being bought by investors who typically trade stocks.[52] Investment-grade yields had increased as mutual fund and money-market funds sold their short-term bonds to meet client redemptions in previous weeks.[53] The high yields and the announcement by the Fed that it would purchase investment-grade bonds to ensure market liquidity attracted hedge funds and other non-traditional buyers seeking a refuge from market volatility.[52][54][55] The Fed’s attempts to maintain corporate liquidity, including with $687 billion in support on 26 March, were primarily focused on companies with higher credit ratings. The Council on Foreign Relations opined that due to the dependence of riskier companies on commercial paper to meet short-term liabilities, there would be a large increase in corporate defaults, unless aid was extended to lower-rated borrowers.[56]

Also on 23 March, the People’s Bank of China (PBOC) began open market operations to inject liquidity for the first time since 17 February, and also lowered interest rates. Chinese firms had sold $445 billion in onshore (yuan-denominated) bonds in 2020, a 12% increase from the first quarter of 2019. This followed Chinese government efforts to increase liquidity, which drove interest rates to a 14-year low. Chinese debt yields remained stable.[57] The amount of Chinese corporate bond defaults fell 30% in the first quarter, year on year, to less than 24 billion yuan ($3.4 billion) as banks rushed loans to stabilize businesses. While bankruptcies and job losses have been avoided in the short term, unless demand for Chinese goods and services increases, the increased loans may turn into more corporate nonperforming debt.[58]

Jerome Powell, Chair of the Federal Reserve, oversaw aggressive measures to stabilize turmoil in bond markets.

On 30 March, Moody’s downgraded the outlook on U.S. corporate debt from stable to negative. It mentioned in particular firms in global air travel, lodging and cruise ships, automobiles, oil and gas, and the banking sector. Moody’s also noted that $169 billion in corporate debt is due in 2020, and further $300 billion in 2021, which would be difficult to roll over in the strained economic climate. At the end of March, Goldman Sachs estimated that $765 billion in U.S. corporate bonds had already experienced rating downgrades. Slippage of firms from investment-grade to junk status continued to pose a stability risk.[59] Fitch forecasted a doubling of defaults on US leveraged loans from 3% in 2019 to 5-6% in 2020, with a default rate for retail and energy companies of up to 20%. Fitch further forecast defaults in these two markets of 8-9% in 2020, totaling $200 billion over two years.[60]

The ability of NCR Corporation and Wynn Resorts to raise $1 billion in unsecured junk-rated debt on 7 April was seen as a sign of increased investor tolerance of risk. The previous week, Yum! Brands and Carnival Corporation were able to issue debt secured against their assets.[61] Some investment funds began spending up to $2.5 billion acquiring loans and bonds that they viewed as becoming undervalued in the chaos of March.[62] Also on 7 April, the Institute of International Finance identified five nation’s corporate sectors with high levels of debt and limited cash that were at the most risk from COVID-19 disruption: Argentina, India, Spain, Thailand, and Turkey.[2]

On 8 April, South Korea began won-denominated debt purchases of up to $16 billion to provide liquidity to investment-grade firms. This enabled Lotte Food Co on 9 April to issue the first won-denominated debt in three weeks. Nevertheless, yields on won-denominated corporate debt were at the highest since 2012 amid pessimism about the global economic outlook and impacts upon South Korean firms.[63]

Fed extends lifeline to “fallen angels”

On 9 April, following passage of the U.S. Coronavirus Aid, Relief, and Economic Security Act (CARES Act), the Fed announced that it would buy up to $2.3 trillion in debt from the U.S. market. This included purchase of debt from “fallen angels”, firms that were downgraded to junk after 22 March.[64][65] The Fed’s Primary and Secondary Market Corporate Credit Facilities totals $750 billion. They are designed as credit backstops for U.S.-listed firms rated at least BBB-/Baa3; if downgraded to junk after 22 March, the firm must be rated at least BB-/Ba3 (the highest tier of junk) when a Facility buys the debt.[66][67] The Fed’s announcement drove a sharp rise in prices on junk bond exchange-traded funds and individual junk-rated bonds, such as Ford Motor Company and Macy’s.[68]

Also on 9 April, ECB President Lagarde dismissed the idea of cancelling Eurozone corporate debt acquired during the COVID-19 crisis, calling it “totally unthinkable”.[69] This followed an opinion piece by former ECB President Mario Draghi arguing that national governments absorbing the cost of debt acquired by companies while economic activity was suspended would be ultimately less harmful to national economies than letting the companies default on their debt and go into restructuring.[70]

On 16 April, Bloomberg News reported that Chinese interest rates were so low that Chinese firms were being incentivized to sell short-term debt in order to buy high-yield and less-regulated wealth management products. This arbitrage was attractive as the poor economic climate reduced incentives to invest in fixed capital and labor. However, it relies on Chinese local and provincial banks remaining solvent to be low risk.[71]

UBS warned on 16 April that the amount of Eurozone BBB-rated debt had risen from $359 billion in 2011 to $1.24 trillion. UBS estimated a high-risk of downgrades to junk status. The average of its models indicated that about $69 billion in Euro non-financial corporate assets may be downgraded to high yield status. There are many uncertainties, but UBS predicts downgrades like that experienced in 2011–12 at the height of the European debt crisis, but not as severe as those experienced by Europe in the 2007-08 financial crisis.[72]

In mid-April, traders in Asian commodity markets reported that it was increasingly difficult to obtain short-term bank letters of credit to conduct deals. Lenders reported that they were reducing exposure by refusing to lend to some smaller firms and demanding more collateral for the loans that they are making; some firms affected by COVID-19-related supply chain disruptions in the low-margin, high-volume commodity business found themselves unable to service their existing debt. One prominent Singaporean commodity firm, Agritrade International had gone bankrupt after being unable to service $1.55 billion in debt, while another, Hin Leong Trading, was struggling to manage almost $4 billion in debt. The Chief Economist of trading giant Trafigura expressed concern that the credit squeeze in Asian commodity markets would spread to the United States and Europe, stating, “We have been talking about this as a series of cascading waves. First the virus, then the economic and then potentially the credit side of it.”[73][74][75]

On 17 April, the $105 billion in debt issued by Mexican oil giant Pemex was downgraded to junk status, making it the largest company to fall from investment grade. However, its bond yields held steady as investors assumed an implicit guarantee by the Mexican government.[76]

On 19 April, The New York Times reported that U.S. corporations had drawn more than $200 billion from existing credit lines during the COVID-19 crisis, far more than had been extended in the 2008 crisis. It noted that debt-laden firms “may be forced to choose between skipping loan payments and laying off workers”. The International Association of Credit Portfolio Managers forecast that credit risk would greatly increase over the next three months.[77]

Neiman Marcus, a U.S. department store retailer, considered bankruptcy protection after failing to cover payments on debt coming due in mid-April.

Neiman Marcus missed payments on about $4.8 billion in debt and stated on 19 April that it would declare bankruptcy, in the context of the ongoing North American retail apocalypse.[78] Ratings agencies had downgraded Neiman Marcus and J. C. Penney the previous week.[79][80] J.C. Penney decided not to make a scheduled $12 million interest payment on a 2036 bond on 15 April and has a one-month grace period before creditors can demand payment.[81]

Negative oil futures focused attention on the U.S. oil sector

On 20 April, May futures contracts for West Texas Intermediate crude oil fell to -$37.63 per barrel as uninterrupted supply met collapsing demand. Even reports that the U.S. administration was considering paying companies not to extract oil did not comfort U.S. oil companies. The head of U.S. oil services company Canary, LLC stated, “A tidal wave of bankruptcies is about to hit the sector.”[82] While oil company bonds had rallied after Fed actions earlier in the month, the collapse of oil prices undermined market confidence. Junk-rated U.S. shale oil companies comprise 12% of the benchmark iShares iBoxx $ High Yield Corporate Bond ETF, which fell 3% from 20 to 21 April. With crude prices so low, U.S. shale oil companies cannot make money pumping more oil. MarketWatch noted that now “investors are likely to focus less on the viability of a driller’s operations and how cheaply it could unearth oil. Instead, money managers would look to assess if a company’s finances were resilient enough to stay afloat during the current economic downturn.” The potential failure of highly leveraged U.S. shale oil bonds may pose a risk to the high-yield market as a whole.[83]

The airline Virgin Australia entered voluntary administration on 21 April, after being unable to manage $4.59 billion in debt. It named Deloitte as its administrator, with the intention of receiving binding offers on the entirety of the company and its operations by the end of June 2020.[84][85]

On 22 April, The New York Times reported that many smaller U.S. oil companies are expected to seek bankruptcy protection in the coming months. Oil production companies have $86 billion in debt coming due between 2020 and 2024, with oil pipeline companies having an additional $123 billion due over the same period. Many U.S. oil firms were operating on Federal loans offered through the CARES Act, but those funds were already running out. The president of oil developer Texland stated, “April is going to be terrible, but May is going to be impossible.”[86]

Assets for companies in the U.S. car rental market, which were not included in the CARES Act, were under severe stress on 24 April. S&P Global Ratings had downgraded Avis and Hertz to “highly speculative”, while credit default swaps for Hertz bonds indicated a 78% chance of default within 12 months and a 100% chance within five years.[87]

Ship owned by Singapore’s Ezra Holdings, an offshore support provider for the oil and gas industry

ANZ Bank reported in late April that corporate debt in Asia was rising fastest in China, South Korea, and Singapore. Energy companies in Singapore and in South Korea, in particular, were singled out for being “over-leveraged and short on cash buffers”. In China, the real estate sector was similarly over-extended. Over 60% of outstanding Singaporean corporate debt was denominated in U.S. dollars, increasing exposure to foreign exchange risk, compared to only a fifth of South Korean corporate debt. ANZ Bank, noting that most Chinese corporate debt is owned by the state and has an implicit guarantee, concluded that Chinese corporations are the least vulnerable to debt loads.[88]

Record debt purchases in April

Between 1 January and 3 May, a record $807.1 billion of U.S. investment-grade corporate bonds were issued.[89] Similarly, U.S. corporations sold over $300 billion in debt in April 2020, a new record. This included Boeing, which sold $25 billion in bonds, stating that it would no longer need a bailout from the U.S. government.[90] Apple, which borrowed $8.5 billion potentially to pay back the $8 billion in debt coming due later in 2020; Starbucks, which raised $3 billion.;[89] Ford, which sold $8 billion in junk-rated bonds despite just losing its investment rating; and cruise line operator Carnival, which increased its offering to $4 billion to meet demand.[91] The main reasons for the lively market are the low interest rates and the Fed’s actions to ensure market liquidity. The iShares iBoxx USD Investment Grade Corporate Bond, an exchange-traded fund with assets directly benefiting from Fed actions, grew by a third between March 11 and the end of April.[92] However, companies are growing increasingly leveraged as they increase their debt while earnings fall. Through the end of April 2020, investment-grade corporate bonds gained 1.4% versus Treasury bonds’ 8.9%, indicating potential investor wariness about the risk of corporate bonds. Morgan Stanley estimated 2020 U.S. investment-grade bond issuance at $1.4 trillion, around 2017’s record, while Barclays estimated the non-financial corporations will need to borrow $125–175 billion in additional debt to cover the drop in earnings from the pandemic recession.[90] Warren Buffett noted that the terms offered by the Fed were far better than those that Berkshire Hathaway could offer.[93]

Haruhiko Kuroda, Governor of the Bank of Japan, oversaw increased buying of corporate paper and longer-term debt in March and April 2020

The Bank of Japan increased its holdings of commercial paper by 27.8% in April 2020, which followed a rise of $16.9% in March. Efforts to alleviate strain on Japanese corporate finances also included increasing BoJ corporate bond holdings by 5.27% in April. The chief market economist at Daiwa Securities noted, “The steps the BOJ has taken so far are aimed at preventing a worsening economy from triggering a financial crisis. We’ll know around late June through July whether their plan will work.”[94]

On 4 May, U.S. retailer J.Crew filed for bankruptcy protection to convert $1.6 billion in debt to equity. Its debt largely resulted from the 2011 leveraged buyout by its current owners. J.Crew became the first U.S. retailer to go bankrupt in the COVID-19 downturn.[95]

In the week of 4 May, the Chamber of Deputies in the National Congress of Brazil was seeking to pass an amendment to the Constitution that would allow the Brazil to buy private sector securities. However, the Central Bank was concerned that bank officials could face accusations of corruption for buying assets from individual companies and were seeking personal liability protection for Central Bank purchases.[96]

As of 6 May, the Fed had not yet utilized its Primary Market Corporate Credit and Secondary Market Corporate Credit facilities and had not explained how companies could be certified for these lending programs. However, investors had already bought debt as if the Fed backstop existed. Bank of America Global Research expressed concern that unless the Fed began actually buying debt, the uncertainty could further roil bond markets.[97]

A group of U.S. Republican lawmakers asked President Trump to mandate that loans be provided to U.S. energy companies through the Coronavirus Aid, Relief, and Economic Security Act’s Main Street Lending Program. They specifically mentioned BlackRock, which is a fiduciary to the Federal Reserve Bank of New York, and had declared in January that it was divesting itself of assets connected to power plant coal. Democratic lawmakers had previously called that oil and gas companies be barred from Main Street facility loans.[98] More than $1.9 billion in CARES Act benefits were being claimed by oil and oil services companies, using a tax provision that allowed companies to claim losses from before the pandemic using the highest tax rate of the previous five years, even if the losses didn’t happen under that tax rate. Dubbed a “stealth bailout” of the oil industry, the loss carryback provision was expected to cost at least $25 billion over 10 years.[99]

On 9 May, Goldman Sachs warned that U.S. investors may be overestimating the Fed guarantees to junk-rated debt. Between 9 April and 4 May, the two largest junk exchange-traded funds (ETFs), the SPDR Bloomberg Barclays High Yield Bond ETF and the iShares iBoxx $ High Yield Corporate Bond ETF, respectively received $1.6 billion and $4.71 billion in net inflows. However, Goldman cautioned that even the BB-rated bonds that make up half of these two ETF’s portfolios are likely to experience further downgrades. State Street Global Advisors commented on the distortions being created by the Fed’s implicit guarantee: “The disconnect between the underlying fundamentals of bond issuers and bond prices is tough to reconcile.”[100]

On 12 May, the Fed began buying corporate bond ETFs for the first time in its history. It stated its intention to buy bonds directly “in the near future”. As companies must prove that they can not otherwise access normal credit to be eligible for the primary market facility, analysts opined that it may create a stigma for companies and be little used. However, the guarantee of a Fed backstop appears to have ensured market liquidity.[101]

In its annual review on 14 May, the Bank of Canada concluded that its three interest rate cuts in March and first ever bond buying program had succeeded in stabilizing Canadian markets. However, it expressed concern about the ability of the energy sector to refinance its debt given historically low oil prices. About C$17 billion in Canadian corporate bonds was sold in April 2020, one of the largest volumes since 2010.[102]

On 15 May, J. C. Penney filed for bankruptcy. It followed the filings of Neiman Marcus and J.Crew, but was the largest U.S. retailer to file by far.[103]

On 22 May, The Hertz Corporation filed for bankruptcy.[104]

On 5 August, Virgin Atlantic filed for bankruptcy.[105]

See also

  • List of countries by corporate debt

Notes

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Loss given default

Loss given default or LGD is the share of an asset that is lost if a borrower defaults.

It is a common parameter in risk models and also a parameter used in the calculation of economic capital, expected loss or regulatory capital under Basel II for a banking institution. This is an attribute of any exposure on bank’s client. Exposure is the amount that one may lose in an investment.

The LGD is closely linked to the expected loss, which is defined as the product of the LGD, the probability of default (PD) and the exposure at default (EAD).

Definition

LGD is the share of an asset that is lost when a borrower defaults. The recovery rate is defined as 1 minus the LGD, the share of an asset that is recovered when a borrower defaults.[1]

Loss given default is facility-specific because such losses are generally understood to be influenced by key transaction characteristics such as the presence of collateral and the degree of subordination.

How to calculate LGD

The LGD calculation is easily understood with the help of an example: If the client defaults with an outstanding debt of $200,000 and the bank or insurance is able to sell the security (e.g. a condo) for a net price of $160,000 (including costs related to the repurchase), then the LGD is 20% (= $40,000 / $200,000).

Theoretically, LGD is calculated in different ways, but the most popular is ‘gross’ LGD, where total losses are divided by exposure at default (EAD). Another method is to divide losses by the unsecured portion of a credit line (where security covers a portion of EAD). This is known as ‘Blanco’ LGD.[a] If collateral value is zero in the last case then Blanco LGD is equivalent to gross LGD. Different types of statistical methods can be used to do this.

Gross LGD is most popular amongst academics because of its simplicity and because academics only have access to bond market data, where collateral values often are unknown, uncalculated or irrelevant. Blanco LGD is popular amongst some practitioners (banks) because banks often have many secured facilities, and banks would like to decompose their losses between losses on unsecured portions and losses on secured portions due to depreciation of collateral quality. The latter calculation is also a subtle requirement of Basel II, but most banks are not sophisticated enough at this time to make those types of calculations.[citation needed]

Calculating LGD under the foundation approach (for corporate, sovereign and bank exposure)

To determine required capital for a bank or financial institution under Basel II, the institution has to calculate risk-weighted assets. This requires estimating the LGD for each corporate, sovereign and bank exposure. There are two approaches for deriving this estimate: a foundation approach and an advanced approach.

Exposure without collateral

Under the foundation approach, BIS prescribes fixed LGD ratios for certain classes of unsecured exposures:

  • Senior claims on corporates, sovereigns and banks not secured by recognized collateral attract a 45% LGD.
  • All subordinated claims on corporates, sovereigns and banks attract a 75% LGD.

Exposure with collateral

Simple LGD example: If the client defaults, with an outstanding debt of 200,000 (EAD) and the bank is able to sell the security for a net price of 160,000 (including costs related to the repurchase), then 40,000, or 20%, of the EAD are lost – the LGD is 20%.

The effective loss given default ({\displaystyle L_{GD}^{*}}) applicable to a collateralized transaction can be expressed as {\displaystyle L_{GD}^{*}=L_{GD}\cdot {\frac {E^{*}}{E}}} Haircut appropriate for currency mismatch between the collateral and exposure (The standard supervisory haircut for currency risk where exposure and collateral are denominated in different currencies is 8%)

The *He and *Hc has to be derived from the following table of standard supervisory haircuts:

However, under certain special circumstances the supervisors, i.e. the local central banks may choose not to apply the haircuts specified under the comprehensive approach, but instead to apply a zero H.

Calculating LGD under the advanced approach (and for the retail-portfolio under the foundation approach)

Under the A-IRB approach and for the retail-portfolio under the F-IRB approach, the bank itself determines the appropriate loss given default to be applied to each exposure, on the basis of robust data and analysis. The analysis must be capable of being validated both internally and by supervisors. Thus, a bank using internal loss given default estimates for capital purposes might be able to differentiate loss given default values on the basis of a wider set of transaction characteristics (e.g. product type, wider range of collateral types) as well as borrower characteristics. These values would be expected to represent a conservative view of long-run averages. A bank wishing to use its own estimates of LGD will need to demonstrate to its supervisor that it can meet additional minimum requirements pertinent to the integrity and reliability of these estimates.

An LGD model assesses the value and/or the quality of a security the bank holds for providing the loan – securities can be either machinery like cars, trucks or construction machines. It can be mortgages or it can be a custody account or a commodity. The higher the value of the security the lower the LGD and thus the potential loss the bank or insurance faces in the case of a default. Banks using the A-IRB approach have to determine LGD values, whereas banks within the F-IRB do only have to do so for the retail-portfolio. For example, as of 2013, there were nine companies in the United Kingdom with their own mortgage LGD models. In Switzerland there were two banks as of 2013. In Germany many thrifts – especially the market leader Bausparkasse Schwäbisch Hall – have their own mortgage LGD models. In the corporate asset class many German banks still only use the values given by the regulator under the F-IRB approach.

Repurchase value estimators (RVEs) have proven to be the best kind of tools for LGD estimates. The repurchase value ratio provides the percentage of the value of the house/apartment (mortgages) or machinery at a given time compared to its purchase price.

Downturn LGD

Under Basel II, banks and other financial institutions are recommended to calculate ‘downturn LGD’ (downturn loss given default), which reflects the losses occurring during a ‘downturn’ in a business cycle for regulatory purposes. Downturn LGD is interpreted in many ways, and most financial institutions that are applying for IRB approval under BIS II often have differing definitions of what Downturn conditions are. One definition is at least two consecutive quarters of negative growth in real GDP. Often, negative growth is also accompanied by a negative output gap in an economy (where potential production exceeds actual demand).

The calculation of LGD (or downturn LGD) poses significant challenges to modelers and practitioners. Final resolutions of defaults can take many years and final losses, and hence final LGD, cannot be calculated until all of this information is ripe. Furthermore, practitioners are of want of data since BIS II implementation is rather new and financial institutions may have only just started collecting the information necessary for calculating the individual elements that LGD is composed of: EAD, direct and indirect Losses, security values and potential, expected future recoveries. Another challenge, and maybe the most significant, is the fact that the default definitions between institutions vary. This often results in a so-called differing cure-rates or percentage of defaults without losses. Calculation of LGD (average) is often composed of defaults with losses and defaults without. Naturally, when more defaults without losses are added to a sample pool of observations LGD becomes lower. This is often the case when default definitions become more ‘sensitive’ to credit deterioration or ‘early’ signs of defaults. When institutions use different definitions, LGD parameters therefore become non-comparable.

Many institutions are scrambling to produce estimates of downturn LGD, but often resort to ‘mapping’ since downturn data is often lacking. Mapping is the process of guesstimating losses under a downturn by taking existing LGD and adding a supplement or buffer, which is supposed to represent a potential increase in LGD when a downturn occurs. LGD often decreases for some segments during a downturn since there is a relatively larger increase of defaults that result in higher cure-rates, often the result of temporary credit deterioration that disappears after the downturn period is over. Furthermore, LGD values decrease for defaulting financial institutions under economic downturns because governments and central banks often rescue these institutions in order to maintain financial stability.

In 2010 researchers at Moody’s Analytics quantify an LGD in line with the target probability event intended to be captured under Basel. They illustrate that the Basel downturn LGD guidelines may not be sufficiently conservative.[2] Their results are based on a structural model that incorporates systematic risk in recovery.[3]

Correcting for different default definitions

One problem facing practitioners is the comparison of LGD estimates (usually averages) arising from different time periods where differing default definitions have been in place. The following formula can be used to compare LGD estimates from one time period (say x) with another time period (say y):

LGDy=LGDx*(1-Cure Ratey)/(1-Cure Ratex)

Country-specific LGD

In Australia, the prudential regulator APRA has set an interim minimum downturn LGD of 20 per cent on residential mortgages for all applicants for the advanced Basel II approaches. The 20 per cent floor is not risk sensitive and is designed to encourage authorised deposit-taking institutions (ADIs) to undertake further work, which APRA believes would be closer to the 20 per cent on average than ADIs’ original estimates.

Importance

LGD warrants more attention than it has been given in the past decade, where credit risk models often assumed that LGD was time-invariant. Movements in LGD often result in proportional movements in required economic capital. According to BIS (2006) institutions implementing Advanced-IRB instead of Foundation-IRB will experience larger decreases in Tier 1 capital, and the internal calculation of LGD is a factor separating the two Methods.[citation needed]

Notes

  1. ^ Named after academic Roberto Blanco

References

  1. ^ Altman, Edward; Resti, Andrea; Sironi, Andrea (July 2004). “Default Recovery Rates in Credit Risk Modelling: A Review of the Literature and Empirical Evidence”. Economic Notes33 (2): 183–208. CiteSeerX 10.1.1.194.4041. doi:10.1111/j.0391-5026.2004.00129.x.
  2. ^ Levy, Amnon; Meng, Qiang; Kaplin, Andrew; Wang, Yashan; Hu, Zhenya (2010). “Implications of PD-LGD Correlation in a Portfolio Setting” (PDF)Moody’s Analytics Whitepaper.
  3. ^ Levy, Amnon; Hu, Zhenya (2007). “Incorporating Systematic Risk in Recovery: Theory and Evidence” (PDF)Moody’s Analytics Whitepaper.

Credit derivative

In finance, a credit derivative refers to any one of “various instruments and techniques designed to separate and then transfer the credit risk[1] or the risk of an event of default of a corporate or sovereign borrower, transferring it to an entity other than the lender[2] or debtholder.

An unfunded credit derivative is one where credit protection is bought and sold between bilateral counterparties without the protection seller having to put up money upfront or at any given time during the life of the deal unless an event of default occurs. Usually these contracts are traded pursuant to an International Swaps and Derivatives Association (ISDA) master agreement. Most credit derivatives of this sort are credit default swaps. If the credit derivative is entered into by a financial institution or a special purpose vehicle (SPV) and payments under the credit derivative are funded using securitization techniques, such that a debt obligation is issued by the financial institution or SPV to support these obligations, this is known as a funded credit derivative.

This synthetic securitization process has become increasingly popular over the last decade, with the simple versions of these structures being known as synthetic collateralized debt obligations (CDOs), credit-linked notes or single-tranche CDOs. In funded credit derivatives, transactions are often rated by rating agencies, which allows investors to take different slices of credit risk according to their risk appetite.[3]

History and participants

The historical antecedents of trade credit insurance, which date back at least to the 1860s, also presaged credit derivatives more indirectly.

The market in credit derivatives as defined in today’s terms started from nothing in 1993 after having been pioneered by J.P. Morgan’s Peter Hancock.[4] By 1996 there was around $40 billion of outstanding transactions, half of which involved the debt of developing countries.[1]

Credit default products are the most commonly traded credit derivative product[5] and include unfunded products such as credit default swaps and funded products such as collateralized debt obligations (see further discussion below).

On May 15, 2007, in a speech concerning credit derivatives and liquidity risk, Timothy Geithner, then President of the Federal Reserve Bank of New York, stated: “Financial innovation has improved the capacity to measure and manage risk.” [6] Credit market participants, regulators, and courts are increasingly using credit derivative pricing to help inform decisions about loan pricing, risk management, capital requirements, and legal liability. The ISDA[7] reported in April 2007 that total notional amount on outstanding credit derivatives was $35.1 trillion with a gross market value of $948 billion (ISDA’s Website). As reported in The Times on September 15, 2008, the “Worldwide credit derivatives market is valued at $62 trillion”.[8]

Although the credit derivatives market is a global one, London has a market share of about 40%, with the rest of Europe having about 10%.[5]

The main market participants are banks, hedge funds, insurance companies, pension funds, and other corporates.[5]

Types

Credit derivatives are fundamentally divided into two categories: funded credit derivatives and unfunded credit derivatives.

An unfunded credit derivative is a bilateral contract between two counterparties, where each party is responsible for making its payments under the contract (i.e., payments of premiums and any cash or physical settlement amount) itself without recourse to other assets.

funded credit derivative involves the protection seller (the party that assumes the credit risk) making an initial payment that is used to settle any potential credit events. (The protection buyer, however, still may be exposed to the credit risk of the protection seller itself. This is known as counterparty risk.)

Unfunded credit derivative products include the following products:

  • Credit default swap (CDS)
  • Total return swap
  • Constant maturity credit default swap (CMCDS)
  • First to Default Credit Default Swap
  • Portfolio Credit Default Swap
  • Secured Loan Credit Default Swap
  • Credit Default Swap on Asset Backed Securities
  • Credit default swaption
  • Recovery lock transaction
  • Credit Spread Option
  • CDS index products

Funded credit derivative products include the following products:

  • Credit-linked note (CLN)
  • Synthetic collateralized debt obligation (CDO)
  • Constant Proportion Debt Obligation (CPDO)
  • Synthetic constant proportion portfolio insurance (Synthetic CPPI)

Key unfunded credit derivative products

Credit default swap

The credit default swap or CDS has become the cornerstone product of the credit derivatives market. This product represents over thirty percent of the credit derivatives market.[5]

The product has many variations, including where there is a basket or portfolio of reference entities, although fundamentally, the principles remain the same. A powerful recent variation has been gathering market share of late: credit default swaps which relate to asset-backed securities.[9]

Total return swap

Key funded credit derivative products

Credit linked notes

In this example coupons from the bank’s portfolio of loans are passed to the SPV which uses the cash flow to service the credit linked notes.

A credit linked note is a note whose cash flow depends upon an event, which may be a default, change in credit spread, or rating change. The definition of the relevant credit events must be negotiated by the parties to the note.

A CLN in effect combines a credit-default swap with a regular note (with coupon, maturity, redemption). Given its note-like features, a CLN is an on-balance-sheet asset, in contrast to a CDS.

Typically, an investment fund manager will purchase such a note to hedge against possible down grades, or loan defaults.

Numerous different types of credit linked notes (CLNs) have been structured and placed in the past few years. Here we are going to provide an overview rather than a detailed account of these instruments.

The most basic CLN consists of a bond, issued by a well-rated borrower, packaged with a credit default swap on a less creditworthy risk.

For example, a bank may sell some of its exposure to a particular emerging country by issuing a bond linked to that country’s default or convertibility risk. From the bank’s point of view, this achieves the purpose of reducing its exposure to that risk, as it will not need to reimburse all or part of the note if a credit event occurs. However, from the point of view of investors, the risk profile is different from that of the bonds issued by the country. If the bank runs into difficulty, their investments will suffer even if the country is still performing well.

The credit rating is improved by using a proportion of government bonds, which means the CLN investor receives an enhanced coupon.

Through the use of a credit default swap, the bank receives some recompense if the reference credit defaults.

There are several different types of securitized product, which have a credit dimension.

  • Credit-linked notes (CLN): Credit-linked note is a generic name related to any bond whose value is linked to the performance of a reference asset, or assets. This link may be through the use of a credit derivative, but does not have to be.
  • Collateralized debt obligation (CDO): Generic term for a bond issued against a mixed pool of assets—there also exists CDO-Squared (CDO^2) where the underlying assets are CDO tranches.
  • Collateralized bond obligations (CBO): Bond issued against a pool of bond assets or other securities. It is referred to in a generic sense as a CDO
  • Collateralized loan obligations (CLO): Bond issued against a pool of bank loan. It is referred to in a generic sense as a CDO

CDO refers either to the pool of assets used to support the CLNs or the CLNs themselves.

Collateralized debt obligations

Not all collateralized debt obligations (CDOs) are credit derivatives. For example, a CDO made up of loans is merely a securitizing of loans that is then tranched based on its credit rating. This particular securitization is known as a collateralized loan obligation (CLO) and the investor receives the cash flow that accompanies the paying of the debtor to the creditor. Essentially, a CDO is held up by a pool of assets that generate cash. A CDO only becomes a derivative when it is used in conjunction with credit default swaps (CDS), in which case it becomes a Synthetic CDO. The main difference between CDOs and derivatives is that a derivative is essentially a bilateral agreement in which the payout occurs during a specific event which is tied to the underlying asset.

Other more complicated CDOs have been developed where each underlying credit risk is itself a CDO tranche. These CDOs are commonly known as CDOs-squared.

Pricing

Pricing of credit derivative is not an easy process. This is because:

  • The complexity in monitoring the market price of the underlying credit obligation.
  • Understanding the creditworthiness of a debtor is often a cumbersome task as it is not easily quantifiable.
  • The incidence of default is not a frequent phenomenon and makes it difficult for the investors to find the empirical data of a solvent company with respect to default.
  • Even though one can take help of different ratings published by ranking agencies but often these ratings will be different.

Risks

Risks involving credit derivatives are a concern among regulators of financial markets. The US Federal Reserve issued several statements in the Fall of 2005 about these risks, and highlighted the growing backlog of confirmations for credit derivatives trades. These backlogs pose risks to the market (both in theory and in all likelihood), and they exacerbate other risks in the financial system. One challenge in regulating these and other derivatives is that the people who know most about them also typically have a vested incentive in encouraging their growth and lack of regulation. Incentive may be indirect, e.g., academics have not only consulting incentives, but also incentives in keeping open doors for research.

See also

  • Credit default swap
  • Credit-linked note
  • Jarrow–Turnbull model
  • Merton model

Notes and references

  1. Jump up to:a b The Economist Passing on the risks 2 November 1996
  2. ^ Das, Satyajit (2005). Credit Derivatives: CDOs and Structured Credit Products, 3rd Edition. Wiley. ISBN 978-0-470-82159-6.
  3. ^ Bruyere, Richard; Cont, Rama (2006). Credit Derivatives and Structured Credit: A guide for investors. Wiley. ISBN 978-0470018798.
  4. ^ “AIG: America’s Improved Giant”. The Economist. London. February 2, 2013. Retrieved March 30, 2015.
  5. Jump up to:a b c d “British Banker Association Credit Derivatives Report”(PDF). 2006. Archived from the original (PDF) on 2010-06-02. Retrieved 2007-07-06.
  6. ^ “Liquidity Risk and the Global Economy: Remarks at the Federal Reserve Bank of Atlanta’s 2007 Financial Markets Conference – Credit Derivatives, Sea Island, Georgia”. May 15, 2007.
  7. ^ “ISDA”. April 2007.
  8. ^ Hosking, Patrick; Costello, Miles; Leroux, Marcus (September 16, 2008). “Dow dives as Federal Reserve lines up 75bn emergency loan for AIG”. The Times. London. Retrieved April 30, 2010.
  9. ^ Parker, Edmund; Piracci, Jamila (April 19, 2007). “Documenting credit default swaps on asset backed securities”. Mayer Brown. Archived from the original on May 21, 2011.

Peer-to-peer lending

Peer-to-peer lending, also abbreviated as P2P lending, is the practice of lending money to individuals or businesses through online services that match lenders with borrowers. Peer-to-peer lending companies often offer their services online, and attempt to operate with lower overhead and provide their services more cheaply than traditional financial institutions.[citation needed] As a result, lenders can earn higher returns compared to savings and investment products offered by banks, while borrowers can borrow money at lower interest rates,[1][2][3] even after the P2P lending company has taken a fee for providing the match-making platform and credit checking the borrower.[4][5][6][7] There is the risk of the borrower defaulting on the loans taken out from peer-lending websites.

Also known as crowdlending, many peer-to-peer loans are unsecured personal loans, though some of the largest amounts are lent to businesses. Secured loans are sometimes offered by using luxury assets such as jewelry, watches, vintage cars, fine art, buildings, aircraft, and other business assets as collateral. They are made to an individual, company or charity. Other forms of peer-to-peer lending include student loans, commercial and real estate loans, payday loans, as well as secured business loans, leasing, and factoring.[8]

The interest rates can be set by lenders who compete for the lowest rate on the reverse auction model or fixed by the intermediary company on the basis of an analysis of the borrower’s credit.[9] The lender’s investment in the loan is not normally protected by any government guarantee. On some services, lenders mitigate the risk of bad debt by choosing which borrowers to lend to, and mitigate total risk by diversifying their investments among different borrowers.

The lending intermediaries are for-profit businesses; they generate revenue by collecting a one-time fee on funded loans from borrowers and by assessing a loan servicing fee to investors (tax-disadvantaged in the UK vs charging borrowers) or borrowers (either a fixed amount annually or a percentage of the loan amount). Compared to stock markets, peer-to-peer lending tends to have both less volatility and less liquidity.[10]

Characteristics

Peer-to-peer lending does not fit cleanly into any of the three traditional types of financial institutions—deposit takers, investors, insurers[11]—and is sometimes categorized as an alternative financial service.[12]

Typical characteristics of peer-to-peer lending are:

  • it is sometimes conducted for profit;
  • no necessary common bond or prior relationship between lenders and borrowers;
  • intermediation by a peer-to-peer lending company;
  • transactions take place online;
  • lenders may often choose which borrowers to invest in, if the P2P platform offers that facility;
  • the loans can be unsecured or secured and are not normally protected by government insurance;
  • loans are securities that can be transferred to others, either for debt collection or profit, though not all P2P platforms provide transfer facilities or free pricing choices and costs can be very high, tens of percent of the amount sold, or nil.

Early peer-to-peer lending was also characterized by disintermediation and reliance on social networks but these features have started to disappear. While it is still true that the emergence of internet and e-commerce makes it possible to do away with traditional financial intermediaries and that people may be less likely to default to the members of their own social communities, the emergence of new intermediaries has proven to be time and cost saving. Extending crowdsourcing to unfamiliar lenders and borrowers opens up new opportunities.

Most peer-to-peer intermediaries provide the following services:

  • online investment platform to enable borrowers to attract lenders and investors to identify and purchase loans that meet their investment criteria
  • development of credit models for loan approvals and pricing
  • verifying borrower identity, bank account, employment and income
  • performing borrower credit checks and filtering out the unqualified borrowers
  • processing payments from borrowers and forwarding those payments to the lenders who invested in the loan
  • servicing loans, providing customer service to borrowers and attempting to collect payments from borrowers who are delinquent or in default
  • legal compliance and reporting
  • finding new lenders and borrowers (marketing)

History

United Kingdom

Zopa, founded in February 2005, was the first peer-to-peer lending company in the United Kingdom.[13] Funding Circle, launched in August 2010, became the first significant peer-to-business lender and offering small businesses loans from investors via the platform.[14] Funding Circle has originated over £6.3 billion in loans.[15][16]

In 2011, Quakle, a UK peer-to-peer lender founded in 2010, closed down with a near 100% default rate after attempting to measure a borrower’s creditworthiness according to a group score, similar to the feedback scores on eBay; the model failed to encourage repayment.[17][18][19]

In 2012, the UK government invested £20 million into British businesses via peer to peer lenders. A second investment of £40 million was announced in 2014.[20] The intention was to bypass the high street banks, which were reluctant to lend to smaller companies. This action was criticised for creating unfair competition in the UK, by concentrating financial support in the largest platforms.[21]

Investments have qualified for tax advantages through the Innovative Finance Individual Savings Account (IFISA) since April 2016.[22] In 2016, £80bn was invested in ISAs,[23] creating a significant opportunity for P2P platforms. By January 2017, 17 P2P providers were approved to offer the product.[24]

At one stage there were over 100 individual platforms applying for FCA authorisation, although many withdrew their applications as of 2015.[25]

Since April 2014, the peer-to-peer lending industry has been regulated by the Financial Conduct Authority[26] to increase accountability with standard reporting and facilitate the growth of the sector.[27] Peer-to-peer investments do not qualify for protection from the Financial Services Compensation Scheme (FSCS), which provides security up to £75,000 per bank, for each saver,[28] but regulations mandate the companies to implement arrangements to ensure the servicing of the loans even if the platform goes bust.[29]

In 2015, UK peer-to-peer lenders collectively lent over £3bn to consumers and businesses.[30]

According to the Cambridge Centre for Alternative Finance (Entrenching Innovation Report), £3.55B was attributed to Peer to Peer alternative finance models, the largest growth area being property showing a rise of 88% from 2015 to 2016.[31]

United States

The peer-to-peer lending industry in the US started in February 2006 with the launch of Prosper Marketplace, followed by Lending Club.[32] Both Prosper and Lending Club are headquartered in San Francisco, California.[33] Early peer-to-peer platforms had few restrictions on borrower eligibility, which resulted in adverse selection problems and high borrower default rates. In addition, some investors viewed the lack of liquidity for these loans, most of which have a minimum three-year term, as undesirable.[12]

In 2008, the U.S. Securities and Exchange Commission (SEC) required that peer-to-peer companies register their offerings as securities, pursuant to the Securities Act of 1933.[32][34] The registration process was an arduous one; Prosper and Lending Club had to temporarily suspend offering new loans,[35][36][37][38] while others, such as the U.K.-based Zopa Ltd., exited the U.S. market entirely.[35] Both Lending Club and Prosper gained approval from the SEC to offer investors notes backed by payments received on the loans. Prosper amended its filing to allow banks to sell previously funded loans on the Prosper platform.[12] Both Lending Club and Prosper formed partnerships with FOLIOfn to create a secondary market for their notes, providing liquidity to investors.[39] Lending Club had a voluntary registration at this time, whereas Prosper had mandatory registration for all members.[40]

This addressed the liquidity problem and, in contrast to traditional securitization markets, resulted in making the loan requests of peer-to-peer companies more transparent for the lenders and secondary buyers who can access the detailed information concerning each individual loan (without knowing the actual identities of borrowers) before deciding which loans to fund.[35] The peer-to-peer companies are also required to detail their offerings in a regularly updated prospectus. The SEC makes the reports available to the public via EDGAR (Electronic Data-Gathering, Analysis, and Retrieval).

More people turned to peer-to-peer companies for borrowing following the financial crisis of 2007–2008 because banks refused to increase their loan portfolios. The peer-to-peer market also faced increased investor scrutiny because borrowers’ defaults became more frequent and investors were unwilling to take on unnecessary risk.[41]

Lending Club is the largest peer-to-peer lender in US based upon issued loan volume and revenue, followed by Prosper.[32][33] Lending Club is also the largest peer-to-peer lending platform worldwide.[42] The interest rates range from 5.6%-35.8%, depending on the loan term and borrower rating.[43] The default rates vary from about 1.5% to 10% for the more risky borrowers.[33] Executives from traditional financial institutions are joining the peer-to-peer companies as board members, lenders and investors,[44][45] indicating that the new financing model is establishing itself in the mainstream.[34]

China

Many micro loan companies have emerged to serve the 40 million SMEs, many of which receive inadequate financing from state-owned banks, creating an entire industry that runs alongside big banks.

As the Internet and e-commerce grew in the 2000s, many P2P lenders were founded with various target customers and business models.[46]

The first P2PL in Hong Kong was WeLab, which has backing from American venture capital firm Sequoia Capital and Li Ka-Shing’s TOM Group.[47]

Ezubao, a website launched by Yucheng Group in July 2014 purporting to offer P2P services, was shut down in February 2016 by authorities who described it as a Ponzi scheme.[48] Ezubao took in 50 billion renminbi from 900,000 investors.[49]

In China, in 2016 there were more than 4,000 P2P lending platforms, but 2,000 of them had already suspended operations.[50] As of August 2016, cash flow on all P2P lending platform have already exceeded 191 billion Chinese Yuan (US$29 billion) in the month.[51] Lender’s return rate across all P2P lending platform in China is about 10% per annum on average, with a few of them offering more than 24% return rate.[52] A colloquial term for P2P lending in Chinese translates as “grey market”, but is not to be confused with grey markets for goods or an underground economy.

In June and July 2018, scores of Chinese online P2P lending platforms fell into financial or legal troubles because of tightened regulation and liquidity. According to WDZJ.com, a P2P industry information provider, 23 P2P platforms were reported to be in financial distress or under investigation in the first 10 days of July. That follows 63 such cases in June, a higher number than in any month in the previous year.[53]

In late June, Shanghai police detained four senior executives of Tangxiaoseng, an online lending platform controlled by Zibang Financial Service Internet Technology Co. Ltd. and told investors on June 28, 2018 that Zibang Financial was suspected of “illegally raising funds from the public.”[54] On July 20, 2018, iqianbang.com, a Beijng-based P2P lending platform announced to close down, citing “deteriorating online lending environment and drying up liquidity.”[55]

People’s Bank of China announced in early July 2018 said that regulators will extend a two-year-old nationwide campaign to clean up fraud and violations in the online financial market, targeting P2P and other online lending and financial activities. More than 5,000 operations have been shut down since the campaign began in 2016.[56]

In April 2019, one of China’s top peer-to-peer (P2P) lending platforms, tuandai.com, collapsed, resulting in financial losses for scores of Chinese investors.[57]

Australia

In 2012 Australia’s first peer to peer lending platform, SocietyOne, was launched.[58] As of June 2016 the Australian Government has been encouraging the development of financial technology and peer to peer lending startups through its ‘regulatory sandbox’ program.[59]

New Zealand

In New Zealand, peer-to-peer lending became practicable on April 1, 2014, when the relevant provisions of the Financial Markets Conduct Act 2013 came into force. The Act enables peer-to-peer lending services to be licensed.[60]

The Financial Markets Authority issued the first peer-to-peer lending service licence on July 8, 2014, to Harmoney.[61] Harmoney officially launched its service on October 10, 2014.[62]

India

In India, peer-to-peer lending is currently regulated by the Reserve Bank of India, India’s Central Bank.[citation needed] It has published a consultation paper on regulation of P2P lending[63] and the final guidelines were released in 2017.[64] There were over 30 peer-to-peer-lending platforms in India in 2016.[65] Even with first-mover advantage many sites were not able to capture market share and grow their user base, arguably because of the reserved nature of Indian investors or lack of awareness of this type of debt financing. However, peer-to-peer lending platforms in India are helping a huge section of borrowers who have previously been rejected or have failed to qualify for a loan from banks.[66]

As on August 31, 2019, 19 companies have been granted licenses by the Reserve Bank of India.[67][68][69]

Sweden

Peer-to-peer-lending in Sweden is regulated by Finansinspektionen.[70] Launched in 2007, the company Trustbuddy AB was first out on the Swedish market for peer-to-peer-lending, providing a platform for high risk personal loans between 500SEK and 10,000SEK. Trustbuddy filed for bankruptcy by October 2015, a new board cited abuses by outgoing leadership.[citation needed]

Israel

Several peer-to-peer lending services initiated operation and loan origination during 2014, Following the economic uprising of 2011,[71] and public opinion regarding these platforms is positive. The maximum interest rate in Israeli P2P Arenas is limited by the “Extra-Banking Lending Regulations”.[72]

Canada

Peer-to-Peer P2P Lending for both real estate-secured and non-real estate-secured transactions by either investors or borrowers, is a mature industry in Canada. Peer-to-Peer P2P lending in real estate-secured transactions is regulated by members of the Mortgage Broker Regulators’ Council of Canada (MBRCC),[73] including: the Financial Services Commission of Ontario (FSCO),[74] the Real Estate Council of Alberta (RECA) [75] and the Financial Institutions Commission of British Columbia (FICOM BC).[76] Starting as early as April 9, 2005 PrivateLender.org: Canada’s Private Lending Network® is incontestably [the word “incontestable” is legislatively defined, pursuant to Canada’s Federal Trade-marks Act R.S.C., 1985, c. T-13)[77]] recognized by Canadian federal government public records[78] as Canada’s first network devoted to peer-to-peer P2P lending in both regulated mortgages (real-estate secured) and non-regulated loans (non-real-estate secured). Proof of federal recognition, registration and “date of first use as April 09, 2005” is found at the Canadian Intellectual Property Office (CIPO).[78] Since inception, member individuals and organizations who use the PrivateLender.org: Canada’s Private Lending Network® platform are continuously registered with Canadian federal or provincial mortgage securities regulators including (but not limited to): the Financial Services Commission of Ontario (FSCO),[79] the Real Estate Council of Alberta (RECA) [80] and the Financial Institutions Commission of British Columbia (FICOM BC).[76] PrivateLender.org: Canada’s Private Lending Network® has the further distinction of being the world’s first and only peer-to-peer P2P network with continuous registration to ISO 9001:2015[81] since May 9, 2008.[82] ISO 9001:2015 is published by the International Organization for Standardization [83] and is the National Standard for Quality Management Systems in 119 countries[84] and registration thereto provides legislators, regulators, customers, prospective customers and other interested parties with at-a-glance “confidence that their products are safe, reliable and of good quality.”.[85] Canadian Capital Markets Securities Regulators (members of the Canadian Securities Administrators)[86] are recent entrants to Canadian Peer-to-Peer P2P lending and are only issuing interim approvals “…in order to test their products, services and applications throughout the Canadian market on a time limited basis.,[87] through “Regulatory Sandbox” programs including the CSA Regulatory Sandbox[87] and the Ontario Securities Commission Sandbox, branded as “OSC Launchpad”.[88]

Brazil

Since April 2018, Brazilian p2p lending companies may operate directly without the intermediation of a bank or other financial institution.[89]

By means of the Resolution 4656/2018, the Central Bank of Brazil created a new type of institution called SEP (personal lending society) that aims to provide a platform for direct negotiation of loans between individuals and companies. A SEP cannot lend using its own resources but only operate as an intermediary. The borrower must be Brazilian individual or company, but there isn’t a restriction regarding lenders nationality.[90]

Latvia

Latvian P2P lending market is developing very rapidly. In Q2 2018 Latvian P2P platforms lent Eur 271.8 million and Eur 1.7 Billion cumulatively.[91] Currently, the most active investors in Latvia’s peer-to-peer lending platforms are residents of Germany, Great Britain, and Estonia.[92]

The two biggest P2P platforms are Mintos and Twino taking over 60% and 20% of market share respectively.[citation needed] Around 9 companies that qualify as P2P investment platform currently operate in Latvia. Mintos was founded in 2015. In September 2018 the total amount of loans funded through Mintos have surpassed Eur 1 billion. Most of the loans funded through Mintos are personal loans with car loans coming second.[93] In 2016 Mintos has raised Eur 2 million in funding from Latvian-based Venture Capital Skillion Ventures.[94] Twino investment platform was launched in 2015, although the company has been operating since 2009 as a loan originator. Since the inception in 2009 TWINO has lent more than Eur 500 million in loans.[95] More than 90% of all loans that are on TWINO platform are short maturity from 1 to 3 months.[96]

In 2015, the Ministry of Finance of Latvia initiated development of a new regulation on the peer-to-peer lending in Latvia to establish regulatory requirements, such as rules for management compliance, AML requirements and other prudential measures.[97]

Ireland

The Irish P2P lending platform Linked Finance was launched in 2013. In 2016, Linked Finance was also authorised to operate in the UK by the Financial Conduct Authority.[98] In 2015, Initiative Ireland launched the first property-backed secured lending P2P platform in Ireland.[99]

Indonesia

In Indonesia, P2P lending is growing fast in recent years and is regulated under OJK since 2016. As of April 2019, there are 106 P2P platforms registered in OJK.[100] P2P platforms provide loans in particular targeting into unbanked population, which is estimated around 100+ million in Indonesia.

Thousands of P2P platforms are illegal. Their applications are believed stealing customers data such as contacts and photos and these are used by the debt collectors to intimidate the customers. The debt collectors contact family members, friends, and even employers of the customers and telling them that the customers have debt that needs to be paid. Some of them suicide. Many cases are reported in the Indonesia’s complaint handling system.[101] Yet the police have not taken serious actions against these cases.

Bulgaria

There is no specific Peer-to-Peer lending regulation in Bulgaria. Currently, Klear Lending is the only Bulgarian platform. It was launched in 2016 and provides personal loans to prime customers. The Peer-to-Peer lending platform is operated by Klear Lending AD, a financial institution registered in the Register per art. 3a of the Credit Institutions Act maintained by the Bulgarian National Bank.[102]

Korea

In Korea, Money Auction and Pop Funding are the very first peer to peer lending companies founded in 2006 and 2007 respectively.[103] Korean P2P lending industry did not attract much public attention until late 2014 and early 2015, during which period a number of new fintech companies were founded underpinned by the global fintech wave with the emergence of Lending Club as the mainstream P2P lending player in the US. New P2P lending companies launched in Korea during this period include 8 Percent, Terafunding, Lendit, Honest Fund and Funda.[104] At the beginning, 8 Percent, Lendit and Honest Fund focused on personal loan origination and Terafunding was the only P2P platform dedicated to the real estate backed loan origination, founded by ex-real estate broker and investor, Tae Young Yang.

There was a brief period of regulatory uncertainty on the P2P business model as the P2P lending model was not officially legalized under the then regulatory regime. 8 percent was briefly shut down by the regulator in Feb 2015 and was reopened again.[105] Korean P2P industry saw an explosive growth in a year. According to the regulator, cumulative P2P lending platform loan origination increased to KRW 311,800,000,000 as of December in 2016 from KRW 72,400,000,000 in March and there was a debate as to whether the industry was getting overheated, with questions on whether the industry offered appropriate investor protection.[106] To respond to these concerns, as of February 2017, Korean regulator imposed an annual investment limit of KRW 10,000,000 for a retail investor on these lending platforms, and KRW 40,000,000 for certain qualified investors.[107]

As of April 2017, there are 148 P2P lending companies in Korea. However, only 40 companies are official members of the Korea P2P Finance Association. These members include Lendit, Roof Funding, Midrate, HF Honest Fund, Villy, 8 Percent, Terafunding, Together Funding and People Funding.[108] According to the Korea P2P Finance Association, cumulative loan lent by its member P2P companies stands at c. KRW 2.3 TRN as of March 2018. By origination category, real estate project financing origination constitutes c. KRW 768,500,000,000, real estate asset backed origination is KRW 611,500,000,000, other asset backed KRW 472,400,000,000 and personal loan origination stands at KRW 443,200,000,000.[109] Average interest yield offered by the member companies is 14.32%.

Germany

In Germany, P2P lending is growing fast in recent years and is regulated under Federal Financial Supervisory Authority. The transaction volume will reach an estimated value of €252 million in 2020.[110]

Legal regulation

In many countries, soliciting investments from the general public is considered illegal. Crowd sourcing arrangements in which people are asked to contribute money in exchange for potential profits based on the work of others are considered to be securities.

Dealing with financial securities is connected to the question of ownership: in the case of person-to-person loans, the problem is who owns the loans (notes) and how that ownership is transferred between the originator of the loan (the person-to-person lending company) and the individual lender(s).[36][37] This question arises especially when a peer-to-peer lending company does not merely connect lenders and borrowers but also borrows money from users and then lends it out again. Such activity is interpreted as a sale of securities, and a broker-dealer license and the registration of the person-to-person investment contract is required for the process to be legal. The license and registration can be obtained at a securities regulatory agency such as the U.S. Securities and Exchange Commission (SEC) in the U.S., the Ontario Securities Commission in Ontario, Canada, the Autorité des marchés financiers in France and Québec, Canada, or the Financial Services Authority in the UK.

Securities offered by the U.S. peer-to-peer lenders are registered with and regulated by the SEC. A recent report by the U.S. Government Accountability Office explored the potential for additional regulatory oversight by Consumer Financial Protection Bureau or the Federal Deposit Insurance Corporation, though neither organization has proposed direct oversight of peer-to-peer lending at this time.[111] In 2016, New York state sent “warning letters” threatening to require 28 peer-to-peer lenders to obtain a license to operate unless they “immediately” complied with responses to demands to disclose their lending practices and products available in the state.[112]

In the UK, the emergence of multiple competing lending companies and problems with subprime loans has resulted in calls for additional legislative measures that institute minimum capital standards and checks on risk controls to preclude lending to riskier borrowers, using unscrupulous lenders or misleading consumers about lending terms.[113]

Advantages and criticism

Interest rates

One of the main advantages of person-to-person lending for borrowers can sometimes be better rates than traditional bank rates can offer.[114] The advantages for lenders can be higher returns than obtainable from a savings account or other investments, but subject to risk of loss, unlike a savings account.[115] Interest rates and the methodology for calculating those rates varies among peer-to-peer lending platforms. The interest rates may also have a lower volatility than other investment types.[116]

Socially-conscious investment

For investors interested in socially conscious investing, peer-to-peer lending offers the possibility of supporting the attempts of individuals to break free from high-rate debt, assist persons engaged in occupations or activities that are deemed moral and positive to the community, and avoid investment in persons employed in industries deemed immoral or detrimental to community.[117][118]

Credit risk

Peer-to-peer lending also attracts borrowers who, because of their credit status or the lack thereof, are unqualified for traditional bank loans. Because past behavior is frequently indicative of future performance and low credit scores correlate with high likelihood of default, peer-to-peer intermediaries have started to decline a large number of applicants and charge higher interest rates to riskier borrowers that are approved.[41]

It seemed initially that one of the appealing characteristics of peer-to-peer lending for investors was low default rates, e.g. Prosper’s default rate was quoted to be only at about 2.7 percent in 2007.[115]

The actual default rates for the loans originated by Prosper in 2007 were in fact higher than projected. Prosper’s aggregate return (across all credit grades and as measured by LendStats.com, based upon actual Prosper marketplace data) for the 2007 vintage was (6.44)%, for the 2008 vintage (2.44)%, and for the 2009 vintage 8.10%. Independent projections for the 2010 vintage are of an aggregate return of 9.87.[119] During the period from 2006 through October 2008 (referred to as ‘Prosper 1.0’), Prosper issued 28,936 loans, all of which have since matured. 18,480 of the loans fully paid off and 10,456 loans defaulted, a default rate of 36.1%. $46,671,123 of the $178,560,222 loaned out during this period was written off by investors, a loss rate of 26.1%.[120]

Since inception, Lending Club’s default rate ranges from 1.4% for top-rated three-year loans to 9.8% for the riskiest loans.[33]

The UK peer-to-peer lenders quote the ratio of bad loans at 0.84% for Zopa of the £200m during its first seven years of lending history. As of November 2013, Funding Circle’s current bad debt level was 1.5%, with an average 5.8% return after all bad debt and fees. This is comparable to the 3-5% ratio of mainstream banks and the result of modern credit models and efficient risk management technologies used by P2P companies.[17]

At the other end of the range are places such as Bondora that do lending to less credit-worthy customers, with default rates varying up to as high as 70+% for loans made to Slovak borrowers on that platform, well above those of its original Estonian market.

Government protection

Because, unlike depositors in banks, peer-to-peer lenders can choose themselves whether to lend their money to safer borrowers with lower interest rates or to riskier borrowers with higher returns, in the US peer-to-peer lending is treated legally as investment and the repayment in case of borrower defaulting is not guaranteed by the federal government (U.S. Federal Deposit Insurance Corporation) the way bank deposits are.[121]

A class action lawsuit, Hellum v. Prosper Marketplace, Inc. was held in Superior Court of California on behalf of all investors who purchased a note on the Prosper platform between January 1, 2006 and October 14, 2008. The plaintiffs alleged that Prosper offered and sold unqualified and unregistered securities, in violation of California and federal securities laws during that period. Plaintiffs further allege that Prosper acted as an unlicensed broker/dealer in California. The Plaintiffs were seeking rescission of the loan notes, rescissory damages, damages, and attorneys’ fees and expenses.[122] On July 19, 2013 the class action lawsuit was settled. Under the settlement terms Prosper will pay $10 million to the class action members.[123]

Peer-to-peer lending sponsors

Peer-to-peer lending sponsors are organizations that handle loan administration on behalf of others including individual lenders and lending agencies, but do not loan their own money.[124][125] Notable peer-to-peer lending sponsors include:

  • Kiva
  • Lendwithcare
  • MYC4
  • Vittana (defunct)
  • Wokai (defunct)
  • Zidisha

See also

  • Alternative finance
  • Alternative financial services
  • Comparison of crowdfunding services
  • Customer to customer
  • Non-bank financial institution
  • Peer-to-peer banking
  • Self-Organized Funding Allocation
  • Peer-to-peer lending companies

References

  1. ^ “P2P Lending: What is an Expected Return? A Survey of Industry Voices”. LendingMemo. September 27, 2013. Retrieved March 28, 2017.
  2. ^ “Savings Account as Investment – The Simple Dollar”. The Simple Dollar. December 11, 2011. Retrieved March 28, 2017.
  3. ^ “Here’s How the Average Savings Account Interest Rate Compares to Yours | GOBankingRates”. GOBankingRates. March 23, 2017. Retrieved March 28, 2017.
  4. ^ “Rates & Fees”. www.lendingclub.com. Retrieved March 28,2017.
  5. ^ “What fees does Lending Club charge investors?”. Lending Club. Retrieved March 28, 2017.
  6. ^ “Prosper Help”. Archived from the original on February 12, 2013. Retrieved March 28, 2017.
  7. ^ “Interest Rates and Fees on Lending Club & Prosper Loans”. LendingMemo. April 30, 2014. Retrieved March 28, 2017.
  8. ^ Moenninghoff, S., Wieandt, A. (May 20, 2014). “The Future of Peer-to-Peer Finance”. Zeitschrift für Betriebswirtschaftliche Forschung. SSRN 2439088.
  9. ^ Lepro, Sara (December 20, 2010). “Prosper Ditches Auction Pricing for Model Like P-to-P Rival’s”. American Banker. Retrieved July 31, 2012.
  10. ^ J. D. Roth Taking a Peek at Peer-to-Peer Lending TimeNovember 15, 2012; Accessed March 22, 2013.
  11. ^ Robert E. Wright; Vincenzo Quadrini. Chapter 2 Section 5: Financial Intermediaries (PDF). Retrieved August 5, 2012.
  12. Jump up to:a b c Bradley, Christine; Burhouse, Susan; Gratton, Heather; Miller, Rae-Ann (2009). “Alternative Financial Services: A Primer”. FDIC Quarterly3 (Q1). Federal Deposit Insurance Corporation. Retrieved July 30, 2012.
  13. ^ “zopa.com: Key facts”.
  14. ^ Collinson, Patrick (August 28, 2010). “Peer-to-peer lending and saving: Making everyone happy”. The Guardian.
  15. ^ “fundingcircle.com: Statistics”.
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Ijarah

Ijarah, (Arabic: الإجارة‎, al-Ijārah, “to give something on rent”[1][2] or “providing services and goods temporarily for a wage”[3] (a noun, not a verb)), is a term of fiqh (Islamic jurisprudence)[1] and product in Islamic banking and finance. In traditional fiqh, it means a contract for the hiring of persons or renting/leasing of the services or the “usufruct” of a property, generally for a fixed period and price.[4] In hiring, the employer is called musta’jir, while the employee is called ajir.[1] Ijarah need not lead to purchase. In conventional leasing an “operating lease” does not end in a change of ownership, nor does the type of ijarah known as al-ijarah (tashghiliyah).[4]

In Islamic finance, al Ijarah does lead to purchase (Ijara wa Iqtina, or “rent and acquisition”) and usually refers to a leasing contract of property (such as land, plant, office automation, a motor vehicle), which is leased to a client for stream of rental and purchase payments, ending with a transfer of ownership to the lessee, and otherwise follows Islamic regulations.[4]

Rules

Islamic finance theorist Muhammad Taqi Usmani lists seventeen “Basic Rules of Leasing” (leasing referring to Islamic leasing which Usmani uses interchangeably with ijarah) in his work Islamic Finance: Principles and Practice — although “the principles of ijarah are so numerous that a separate volume is required for their full discussion”.[5] Some of the rules include agreeing on the cost of the lease and the period of time for which it will last; clear terms in the contract; agreeing on purpose the lessee will use the property for, which they must stick to; the lessor (owner of the leased property) agreeing to bear all the “liabilities emerging from the ownership”, etc.[5] Usmani lists eleven “basic differences between the contemporary financial leasing” and “leasing allowed by the Shari‘ah”.[6]

Faleel Jamaldeen lists three features of ijarah that distinguish it from conventional leasing:[3]

  • The lessor must own the asset being leased for the entire period of the lease.
  • No compound interest may be charged if the lessee delays or defaults on payment.
  • Use of the asset being leased must be specified in the contract.

Types of Ijarah

There are several types of ijarah:

Ijarah thumma al bai’ (hire purchase)

In this transaction (hire purchase[7] or Lease-Sale or Financial Lease)[8] the customer leases (hires) a good and agrees to purchase it, paying in installments so that by the end of the lease it owns the good free and clear. This involves two contracts:

  1. an Ijarah that outlines the terms for leasing or renting over a fixed period;
  2. Bai that triggers a sale to be completed by the end of the term of the Ijarah.

One Islamic Bank (Devon Bank) describes the process as follows

An ijarah transaction involves two components: a purchase agreement and a lease. You go out and find the property you would like us to purchase on your behalf. You negotiate the price and other aspects of the purchase. You make any initial payment of earnest money to reserve the property. You make sure that the purchase contract allows [the] Bank to step into the transaction as the buyer. The Bank then buys the property. At the closing, the Bank enters into an agreement to sell the property to you for a fixed price-the purchase price the Bank paid plus any transaction costs not paid by you at the closing. Ownership of the property is transferred to you after this price has been paid to the Bank. A payment schedule is established so that in exchange for keeping the property rented, your payments are deferred over time.[9]

The buyer’s installment payments will remain the same (or fairly close to the same) through the contract, but the portion of the payment going towards ownership of the property will increase to 100% over time as the portion going to pay rent/lease decreases to 0% — the decrease in rent/lease reflecting the decrease in the bank’s equity of the property as the buyer’s increases (much like the interest portion of a conventional mortgage payment declines to zero and the equity payment increases to 100% over time).

(This type of transaction is similar to the contractum trinius, a technique used by European bankers and merchants during the Middle Ages to comply with the letter of the Church’s prohibition on interest bearing loans. In a contractum, two parties would enter into three (trinius) concurrent and interrelated legal contracts, the net effect being the paying of a fee for the use of money for the term of the loan. The use of concurrent interrelated contracts is also prohibited under Shariah Law.)[4]

As of around 2013 there were 15 banks in Malaysia that offered this mode of finance (sometimes abbreviated as AITAB) for “individual and corporate customers”.[10]

Ijarah wa-iqtina (or al-ijarah muntahia bitamleek)

Ijarah wa-iqtina[11] (literally, “lease and ownership”)[12] is also called al ijarah muntahia bitamleek (“lease that ends with ownership”).[13] Like a ijara thumma bay`, it may involve both a lease contract and a sale contract. However, in an ijara wa iqtina contract the transfer of ownership occurs as soon as the lessee pays the purchase price of the asset — anytime during the leasing period.[14]

Another source describes the difference between ijara muntahia bittamleek and ijara thumma bay` as that in ijara muntahia bittamleek sale/ownership transfer is “an option given to the lessee”. In ijara thumma bay` sale is part of the contract.[15]

An Islamically correct ijara wa iqtina contract “rests” on three conditions:

  1. The lease and the transfer of ownership of the asset or the property should be recorded in separate documents.[14]
  2. The agreement to transfer of ownership should not be a pre-condition to the signing of the leasing contract.[14]
  3. The “promise” to transfer the ownership should be unilateral and should be binding only on the lessor.[14]

Another source (investment-and-finance.net)[16] describes Ijarah muntahia bittamleek as being though

  1. hibah (gift), where legal title is transferred to the lessee without any more payments, and which according to investment-and-finance.net “is widely used by Islamic banks.”[16]
  2. or through sales. Ijarah muntahia bittamleek through sales may be of three types:
a) where there is a gradual transfer of legal title of the leased property during the leasing “tenor” (period of time of the lease).[16]
b) where the legal title is transferred at the end of lease tenor for “a token consideration”.[16]
c) where ownership is transferred before the end of the lease tenor for a price equivalent to the remaining ijarah installments (net of rental).[16]

ijara mawsoofa bi al dhimma

In a “forward ijarah” or ijara mawsoofa bi al dhimma Islamic contract, (literally “lease described with responsibility”, also transliterated ijara mawsufa bi al thimma), the service or benefit being leased is well-defined, but the particular unit providing that service or benefit is not identified. Thus, if a unit providing the service or benefit is destroyed, the contract is not void.[17]

In contemporary Islamic finance, ijara mawsoofa bi al dhimma is the leasing of something (such as a home, office, or factory) not yet produced or constructed. This means the ijara mawsoofa bi al dhimma contract is combined with a Istisna contract for construction of whatever it is that will provide the service or benefit.[18] The financer finances its making, while the party begins leasing the asset after “taking delivery” of it. While forward sales normally do not comply with sharia, it is allowed using ijarah provided rent/lease payment do not begin until after the customer takes delivery. Also required by sharia is that the asset be clearly specified, its rental rate be clearly set (although the rate may float based on the agreement of both parties).[19]

Challenges

According to M.T. Usmani, “some requirements of Shari‘ah are often overlooked” in transactions of ijarah in the real world, as when an unforeseeable circumstance leads to the destruction of the asset but the lessee is required to keep paying the rent in violation of the principle that the lessor assumes the liability for his ownership and offers any usufruct to the lessee.[20]

Other challenges are not of failure to follow sharia law properly in practice but of disadvantages in cost or consumer protection ijarah has compared to conventional finance. Mahmud el-Gamal notes the added expense of the bank/financer having to “maintain substantial ownership of the property throughout the lease period” compared to financial leases used by conventional finance.[21] Another problem is that the ijarah customer may be “exposed to the risk of losing the property if the financier is sued, loses, and declares bankruptcy” even if the customer has paid off 90% of the property price. A workaround (with additional cost) is to establish “bankruptcy-remote” Special-purpose entitys to hold title to the property and “serve as parties to various agreements regarding obligations for repairs and insurance”.[21]

Abu Umar Faruq Ahmad writes in Theory and Practice of Modern Islamic Finance: The Case Analysis from Australia that at least in that country the lessee of Ijarah wa Iqtina house purchase is in a weaker legal position than the payer of a conventional mortgage. Firstly, the Ijarah wa Iqtina lessor/lender can evict the borrower/buyer who is “a few months in arrears” because the borrower is a tenant not an owner. In contrast the conventional borrower/buyer/mortgagor cannot because they have “security of tenure”. Secondly if the Lender/mortgagee in a conventional mortgage does foreclose on the buyer and re-sell the property, they are “obliged by law to secure the best possible price” and to make available, “a full account” of the resale transactions to the foreclosed borrower. In a Ijarah wa Iqtina contract the lessor/lender has “no such obligation” to the lessee.[22]

Muhammad Akram Khan criticizes ijara’s customer protection vis-à-vis conventional interest-bearing loans in an example:

Suppose, for example, a person takes a five-year interest-bearing loan to buy a car. After two years, if he finds that keeping the car and the loan is uneconomical, he can sell the car in the market and repay the loan. This is not so in the case of ijara. Ijara finance cannot be terminated prematurely.[23][24]

References

Citations

  1. Jump up to:a b c Usmani, Introduction to Islamic Finance, 1998: p.109
  2. ^ “Islamic Glossary. Ijarah”. Islamicity. Retrieved 19 August 2017.
  3. Jump up to:a b Jamaldeen, Islamic Finance For Dummies, 2012:157
  4. Jump up to:a b c d “MEANING OF IJARAH”. academia.edu. Retrieved 21 July 2016.
  5. Jump up to:a b Usmani, Introduction to Islamic Finance, 1998: p.111-113
  6. ^ Usmani, Introduction to Islamic Finance, 1998: p.114-124
  7. ^ “Ijarah thumma al bai“. IjaraCDC. Retrieved 4 October 2017.
  8. ^ “Ijara Contracts”. Ijara Community Development Corporation. Retrieved 4 October 2017.
  9. ^ “Ijarah”. Devon Bank. Retrieved 2 October 2017.
  10. ^ Azizi, Mohd (c. 2013). “Al-Ijarah Thumma al-Bay“. academia.edu. p. 5. Retrieved 5 October 2017.
  11. ^ “Definition of “Ijarah wa-iqtina “. Islamic Banker. Retrieved 21 July 2016.
  12. ^ “ijara”. Islamic-finance.com. Retrieved 19 August 2017.
  13. ^ “Ijara Muntahia-bi-tamleek”. ijara community development corp. Retrieved 24 September 2017.
  14. Jump up to:a b c d “What is Ijara wa Iqtina?”. Ijara CDC. Retrieved 21 July2016.
  15. ^ “What is the Difference Between Ijara Muntahia Bittamleek and Ijara Thumma Bay’?”. Investment & Finance. Retrieved 4 October2017.
  16. Jump up to:a b c d e “Types of Ijarah”. Investment and Finance. Retrieved 5 October 2017.
  17. ^ Delorenzo, Yusuf Talal (n.d.). A Guide to Islamic Finance. Thompson Reuters. p. 59.
  18. ^ Jamaldeen, Islamic Finance For Dummies, 2012:158
  19. ^ “Ijarah Mawsufa Fi al-Dhimmah”. investment&finance. 12 February 2013. Retrieved 25 September 2017.
  20. ^ Usmani, ”Introduction to Islamic Finance”, 1998: p.167
  21. Jump up to:a b El-Gamal, Islamic Finance, 2006: p.14
  22. ^ Ahmad, Abu Umar Faruq (2010). Theory and Practice of Modern Islamic Finance: The Case Analysis from Australia. Universal-Publishers. p. 210. ISBN 9781599425177. Retrieved 4 October2017.
  23. ^ Visser, Hans (2009). Islamic Finance: Principles and practice. Cheltenham, UK; Northampton, MA, USA: Edward Elgar.
  24. ^ Khan, What Is Wrong with Islamic Economics?, 2013: p.349