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


  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 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 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.