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Stochastic Volatility: Option Pricing Using a Multinomial Recombining Tree

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Stochastic Volatility: Option Pricing Using a Multinomial Recombining Tree Stochastic volatility: option pricing using a multinomial recombining tree Ionut¸Florescu1,3 and Frederi G. Viens2,4 1Department of Mathematical Sciences, Stevens Institute of Technology, Castle Point on the Hudson, Hoboken, NJ 07030 2Department of Statistics, Purdue University, 150 N. University St, West Lafayette, IN 47907-2067 [email protected] [email protected] Abstract We treat the problem of option pricing under the Stochastic Volatility (SV) model: the volatility of the underlying asset is a function of an exogenous stochastic process, typically assumed to be mean- reverting. Assuming that only discrete past stock information is available, we adapt an interacting particle stochastic filtering algorithm due to Del Moral, Jacod and Protter (Del Moral et al., 2001) to estimate the SV, and construct a quadrinomial tree which samples volatilities from the SV filter’s empirical measure approximation at time 0. Proofs of convergence of the tree to continuous-time SV models are provided. Classical arbitrage-free option pricing is performed on the tree, and provides answers that are close to market prices of options on the SP500 or on blue-chip stocks. We compare our results to non-random volatility models, and to models which continue to estimate volatility after time 0. We show precisely how to calibrate our incomplete market, choosing a specific martingale measure, by using a benchmark option. Key words and phrases: incomplete markets, Monte-Carlo method, options market, option pricing, particle method, random tree, stochastic filtering, stochastic volatility. 1 1 Introduction 1.1 Historical context Despite significant development in option pricing theory since the publication of the celebrated articles (Black and Scholes, 1973) and (Merton, 1973), the Black-Scholes formula for the European Call Option remains the most widely used application of stochastic analysis in Finance. Nevertheless, this formula has significant biases, see for example (Rubinstein, 1985). The model’s failure to describe the structure of reported option prices is thought to arise principally from its constant volatility assumption. Allowing the volatility to change over time means that it should generally be modeled as a stochastic process. However, accounting for such stochastic volatility (SV) within an option valuation formula is not an easy task. (Hull and White, 1987), (Chesney and Scott, 1989), (Stein and Stein, 1991), (Heston, 1993) all have constructed various specific stochastic volatility models for option pricing. A notable example of an attempt to find analytic formulas for option prices under stochastic volatility is (Fouque et al., 2000a). Even so, there are no simple formulas for the price of options on stochastic-volatility-driven stocks. When some means of implicit or explicit equations are found, the relations involved are cumbersome and not typically robust. Approximations have been constructed to these and other specific volatility models, e.g. (Hilliard and Schwartz, 1996), and (Ritchken and Trevor, 1999). The discrete tree-based Binomial model (Sharpe, 1978), which proposed a pricing scheme not restricted to seeking explicit formulas, was applied in (Cox et al., 1979) to provide an approximation to the lognormal Black-Scholes model and any associated pricing formulas. Later, (Cox and Rubinstein, 1985) used the same approach to value American style options on dividend paying stock, and they also relaxed some other assumptions of the original Black-Scholes model, solidifying the perceived power and versatility of tree-based pricing. 1.2 Stochastic volatility pricing We believe that if one hopes to find any concrete results for stochastic volatility option pricing, one will have to resort to numerical approximations. Inspired by the success of the binomial models, we too seek a tree-based approximation. This article shows how to achieve our goal using a quadrinomial tree model, one in which, at any time, a stock price’s increment may take any one of four values. Moreover our tree has the property of being highly recombining, just like the classical binomial tree, meaning that its number of nodes increases only polynomially, with a low order, as the time horizon increases. In this section, we describe the structure of our results, with comparison to some existing literature. For our underlying continuous-time stochastic process model, we assume that the price process St and 2 the volatility driving process Yt solve the equations: dSt = µStdt + σ(Yt)StdWt (1.1) dY = α(ν Y )dt + ψ(Y )dZ t − t t t This model spans all the stochastic volatility models considered previously for different specifications of the functions σ(x) and ψ(x). For simplicity, we assume Wt and Zt are two independent Brownian motions. The case when they are correlated can be treated using ideas similar to those presented herein. We choose a mean-reverting type process to drive the volatility because this seems to be the most lucrative choice from the practical point of view, as noted for example in (Fouque et al., 2000a). Our quadrinomial approximation for this model holds in fact for a much wider class of volatilities than the mean-reverting ones in (1.1); the latter are used here for illustrative purposes. The SV model (1.1) describes an incomplete market, implying that any derivative price will not be unique, a situation which is resolved by choosing a benchmark option, i.e. fixing the price of a specific derivative a priori. More details are in the leading paragraph of Section 4, and also in Sections 4.4 and 6. The issue of modeling random volatility is made difficult by the fact that it is not observable. A tree approximation approach has been attempted before, most notably (Leisen, 2000) who uses a binomial tree for the volatility and a so-called 8 successors tree for the price. His idea is similar to the one applied in (Nelson and Ramaswamy, 1990) for the case when the volatility is deterministic. However, that idea falls short of replicating the theoretical model when applied to Leisen’s case, since the transformation used to eliminate the volatility does not work with stochastic volatility. Another interesting article is (Aingworth et al., 2003) where a Markov chain is used for the volatility process; unfortunately, the price tree therein is not recombining. Our method for estimating this volatility distribution uses a genetic-type algorithm based on work by Del Moral, Jacod, and Protter (Del Moral et al., 2001). This algorithm works with a fixed number of interacting particles, and gives a good approximation of the optimal estimation (in the sense of least squares) of the volatility given the observed stock prices. Hence it can be best described as an optimal stochastic volatility particle filter 1. 1.3 Structure of the article After some preliminary results on the SV model in Section 2, Section 3 details the stochastic volatility filtering algorithm. Based on this estimated volatility distribution we construct two different models: a 1We mention that for our later construction of the tree the particle filtering step is not necessary. Any good approximation of the distribution of the hidden process Y would serve for the construction of the tree. The reason we choose the specific method described in Section 3 is that it is the best method currently available. In the future if simpler or better algorithms should be developed the adaptation to these new methods is straightforward. We thank one of our anonymous referees for this observation. 3 Static model in Section 4 (see page 12) and a Dynamic model in Section 5, the distinction being that in the static model, volatility estimation is performed only up to the time at which pricing occurs. Subsections 4.1 and 4.2 present, for the Static model, the process of constructing a quadrinomial recom- bining tree which converges in distribution to the price process, and includes the stochastic volatility particle filter. Subsection 4.3 gives the proof of convergence of our quadrinomial tree to the solution of equation (1.1). This proof is based on a general result of convergence of Markov chains to diffusions, which we establish first, in Section 2. We go to the trouble of checking that appropriate classical results can be applied, rather than vaguely quoting Markov chain convergence theorems from referenced books (Stroock and Varadhan, 1979) and (Ethier and Kurtz, 1986), as seems to be the norm in the literature. It is possible to show – although we leave a full proof out of our article for the sake of conciseness – that a tree with anything less than four basic successors could not possibly converge to the Markov process (1.1) it tries to approximate. This includes any binomial or trinomial tree construction. The basic idea is that the main convergence theorem in Section 2 cannot be verified for smaller trees, because we simply do not have enough parameters in the model to verify all the equations involved. Another fact worth mentioning, which we also leave out in this paper, is that if the correlation between the processes W and Z in (1.1) is nonzero, our quadrinomial tree will not work anymore, but a pentanomial tree should be more than sufficient to handle it. The Dynamic model presented in Section 5 for the sake of comparison, is what one would expect to have to construct in order to be consistent with the stochastic volatility model and the stochastic volatility particle filter estimation method; to the best of our knowledge, current Wall Street practice uses similar techniques. We present a standard Monte-Carlo method for pricing in this case, based on an Euler-type discretization of the governing system of stochastic differential equations (1.1). Our Static model differentiates itself from the Dynamic one because, once the stochastic volatility has been estimated up to the the time at which the option pricing question is being asked, the random volatility distribution remains unchanged thereafter for the construction of the model.
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