Heston model

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In finance, the Heston model, named after Steven Heston, is a mathematical model describing the evolution of the volatility of an underlying asset.[1] It is a stochastic volatility model: such a model assumes that the volatility of the asset is not constant, nor even deterministic, but follows a random process.

Basic Heston model[edit]

The basic Heston model assumes that St, the price of the asset, is determined by a stochastic process:[2]

where , the instantaneous variance, is a CIR process:

and are Wiener processes (i.e., random walks) with correlation ρ, or equivalently, with covariance ρ dt.

The parameters in the above equations represent the following:

  • is the rate of return of the asset.
  • is the long variance, or long run average price variance; as t tends to infinity, the expected value of νt tends to θ.
  • is the rate at which νt reverts to θ.
  • is the volatility of the volatility, or 'vol of vol', and determines the variance of νt.

If the parameters obey the following condition (known as the Feller condition) then the process is strictly positive [3]

Extensions[edit]

In order to take into account all the features from the volatility surface, the Heston model may be a too rigid framework.[citation needed] It may be necessary to add degrees of freedom to the original model. A first straightforward extension is to allow the parameters to be time-dependent.[citation needed] The model dynamics are then written as:

Here , the instantaneous variance, is a time-dependent CIR process:

and are Wiener processes (i.e., random walks) with correlation ρ. In order to retain model tractability, one may require parameters to be piecewise-constant.[citation needed]

Another approach is to add a second process of variance, independent of the first one.[citation needed]

A significant extension of Heston model to make both volatility and mean stochastic is given by Lin Chen (1996).[citation needed] In the Chen model the dynamics of the instantaneous interest rate are specified by

Risk-neutral measure[edit]

See Risk-neutral measure for the complete article

A fundamental concept in derivatives pricing is that of the Risk-neutral measure;[citation needed] this is explained in further depth in the above article. For our purposes, it is sufficient to note the following:

  1. To price a derivative whose payoff is a function of one or more underlying assets, we evaluate the expected value of its discounted payoff under a risk-neutral measure.
  2. A risk-neutral measure, also known as an equivalent martingale measure, is one which is equivalent to the real-world measure, and which is arbitrage-free: under such a measure, the discounted price of each of the underlying assets is a martingale. See Girsanov's theorem.
  3. In the Black-Scholes and Heston frameworks (where filtrations are generated from a linearly independent set of Wiener processes alone), any equivalent measure can be described in a very loose sense by adding a drift to each of the Wiener processes.
  4. By selecting certain values for the drifts described above, we may obtain an equivalent measure which fulfills the arbitrage-free condition.

Consider a general situation where we have underlying assets and a linearly independent set of Wiener processes. The set of equivalent measures is isomorphic to Rm, the space of possible drifts. Consider the set of equivalent martingale measures to be isomorphic to a manifold embedded in Rm; initially, consider the situation where we have no assets and is isomorphic to Rm.

Now consider each of the underlying assets as providing a constraint on the set of equivalent measures, as its expected discount process must be equal to a constant (namely, its initial value). By adding one asset at a time, we may consider each additional constraint as reducing the dimension of by one dimension. Hence we can see that in the general situation described above, the dimension of the set of equivalent martingale measures is .

In the Black-Scholes model, we have one asset and one Wiener process. The dimension of the set of equivalent martingale measures is zero; hence it can be shown that there is a single value for the drift, and thus a single risk-neutral measure, under which the discounted asset will be a martingale.[citation needed]

In the Heston model, we still have one asset (volatility is not considered to be directly observable or tradeable in the market) but we now have two Wiener processes - the first in the Stochastic Differential Equation (SDE) for the asset and the second in the SDE for the stochastic volatility. Here, the dimension of the set of equivalent martingale measures is one; there is no unique risk-free measure.[citation needed]

This is of course problematic; while any of the risk-free measures may theoretically be used to price a derivative, it is likely that each of them will give a different price. In theory, however, only one of these risk-free measures would be compatible with the market prices of volatility-dependent options (for example, European calls, or more explicitly, variance swaps). Hence we could add a volatility-dependent asset;[citation needed] by doing so, we add an additional constraint, and thus choose a single risk-free measure which is compatible with the market. This measure may be used for pricing.

Implementation[edit]

  • A recent discussion of implementation of the Heston model is given in a paper by Kahl and Jäckel .[4]
  • Information about how to use the Fourier transform to value options is given in a paper by Carr and Madan.[5]
  • Extension of the Heston model with stochastic interest rates is given in the paper by Grzelak and Oosterlee.[6]
  • Derivation of closed-form option prices for time-dependent Heston model is presented in the paper by Gobet et al.[7]
  • Derivation of closed-form option prices for double Heston model are presented in papers by Christoffersen

[8] and Gauthier. [9]

  • There exist few known parametrisation of the volatility surface based on the Heston model (Schonbusher, SVI and gSVI).
  • Use of the model in a local stochastic volatility context is given in a paper by Van Der Weijst.[10]

Calibration[edit]

The calibration of the Heston model is often formulated as a least squares problem, with the objective function minimizing the difference between the prices observed in the market and those calculated from the Heston model.

The prices are typically those of vanilla options. Sometimes the model is also calibrated to the variance swap term-structure as in Guillaume and Schoutens[11]. Yet another approach is to include forward start options, or barrier options as well, in order to capture the forward smile.

Under the Heston model, the price of vanilla options is given analytically, but requires a numerical method to compute the integral. Le Floc'h[12] summarizes the various quadratures applied and proposes an efficient adaptive Filon quadrature.

The calibration problem involves the gradient of the objective function with respect to the Heston parameters. A finite difference approximation of the gradient has a tendency to create artificial numerical issues in the calibration. It is a much better idea to rely on automatic differentiation techniques. For example, the tangent mode of algorithmic differentiation may be applied using dual numbers in a straightforward manner. Alternatively, Cui et al.[13] give explicit formulas for the analytical gradient. The latter was obtained by introducing an equivalent but tractable form of the Heston characteristic function.

See also[edit]

References[edit]

  1. ^ Heston, Steven L. (1993). "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options". The Review of Financial Studies. 6 (2): 327–343. doi:10.1093/rfs/6.2.327. JSTOR 2962057.
  2. ^ Wilmott, P. (2006), Paul Wilmott on quantitative finance (2nd ed.), p. 861
  3. ^ Albrecher, H.; Mayer, P.; Schoutens, W.; Tistaert, J. (January 2007), "The Little Heston Trap", Wilmott Magazine: 83–92, CiteSeerX 10.1.1.170.9335
  4. ^ Kahl, C.; Jäckel, P. (2005). "Not-so-complex logarithms in the Heston model" (PDF). Wilmott Magazine: 74–103.
  5. ^ Carr, P.; Madan, D. (1999). "Option valuation using the fast Fourier transform" (PDF). Journal of Computational Finance. 2 (4): 61–73. CiteSeerX 10.1.1.6.9994. doi:10.21314/JCF.1999.043.
  6. ^ Grzelak, L.A.; Oosterlee, C.W. (2011). "On the Heston Model with Stochastic Interest Rates". SIAM J. Financial Math. 2: 255–286. doi:10.1137/090756119.
  7. ^ Benhamou, E.; Gobet, E.; Miri, M. (2009). "Time Dependent Heston Model". CiteSeerX 10.1.1.657.6271. doi:10.2139/ssrn.1367955. SSRN 1367955. Cite journal requires |journal= (help)
  8. ^ Christoffersen, P.; Heston, S.; Jacobs, K. (2009). "The Shape and Term Structure of the Index Option Smirk: Why Multifactor Stochastic Volatility Models Work so Well". SSRN 1447362. Cite journal requires |journal= (help)
  9. ^ Gauthier, P.; Possamai, D. (2009), Efficient Simulation of the Double Heston Model, SSRN 1434853
  10. ^ van der Weijst, Roel (2017). "Numerical Solutions for the Stochastic Local Volatility Model". Cite journal requires |journal= (help)
  11. ^ Guillaume, Florence (April 23, 2013). "Heston Model: The Variance Swap Calibration". SSRN. SSRN 2255550.
  12. ^ Le Floc'h, Fabien (2018). "An adaptive Filon quadrature for stochastic volatility models". Journal of Computational Finance. 22 (3): 65–88. doi:10.21314/JCF.2018.356.
  13. ^ Yiran Cui; Sebastian del Baño Rollin; Guido Germano (26 May 2016). "Full and fast calibration of the Heston stochastic volatility model". arXiv:1511.08718 [q-fin.CP].
  14. ^ Mahdavi Damghani, Babak (2013). "De-arbitraging With a Weak Smile: Application to Skew Risk". Wilmott. 2013 (1): 40–49. doi:10.1002/wilm.10201.
  15. ^ Dell'Era, Mario. "CLOSED FORM SOLUTION FOR HESTON PDE BY GEOMETRICAL TRANSFORMATIONS". Asian Economic and Financial Review. Asian Economic and Financial Review, 2014, 4(6): 793-807.