Option prices in stochastic volatility models

TL;DR

This thesis develops analytical and numerical methods for pricing American options in stochastic volatility models, especially the Heston and Bates-Hull-White models, including properties of the value function and convergence of algorithms.

Contribution

It provides a novel analytical characterization of American option values in the Heston model and introduces a hybrid numerical scheme with proven convergence for jump-diffusion models.

Findings

Value function is nondecreasing with respect to volatility.

Proved strict convexity of the American put value function.

Numerical methods are reliable and efficient with proven convergence.

Abstract

In the first part of this thesis, we focus on American options in the Heston model. We first give an analytical characterization of the value function of an American option as the unique solution of the associated (degenerate) parabolic obstacle problem. Our approach is based on variational inequalities in suitable weighted Sobolev spaces. We also investigate the properties of the American value function. We prove that, under suitable assumptions on the payoff, the value function is nondecreasing with respect to the volatility variable. Then, we focus on an American put option and we extend some results which are well known in the Black and Scholes world. In particular, we prove the strict convexity of the value function in the continuation region, some properties of the free boundary function, the Early Exercise Price formula and a weak form of the smooth fit principle. In the second part, we deal with the numerical computation of European and American option prices in jump-diffusion stochastic volatility models. We first focus on the Bates-Hull-White model. We consider a backward hybrid algorithm which uses a Markov chain approximation (a "multiple jumps" tree) in the direction of the volatility and the interest rate and a (deterministic) finite-difference approach in order to handle the underlying asset price process. We also provide a simulation scheme to be used for Monte Carlo evaluations. Numerical results show the reliability and efficiency of the methods. Finally, we analyse the rate of convergence of the hybrid algorithm applied to general jump-diffusion models. We study first-order weak convergence of Markov chains to diffusions under quite general assumptions. Then, we prove the convergence of the algorithm, by studying the stability and the consistency of the hybrid scheme, in a sense that allows us to exploit the probabilistic features of the Markov chain approximation.