We study the Merton portfolio optimization problem in the presence of stochastic volatility using asymptotic approximations when the volatility process is characterized by its time scales of fluctuation. This approach is tractable because it treats the incomplete markets problem as a perturbation around the complete market constant volatility problem for the value function, which is well-unders...

Stochastic volatility (SV) models play an important role in finance. Under these models, the volatility of an asset follows an individual stochastic process. In contrast to the GARCH model, the volatility process in the SV model is autonomous with no need to refer to the asset price. It is often assumed that the log-volatility process follows a standard ARMA process in an SV model. However, emp...

The multivariate lognormal model is a basic pricing model for derivatives with multiple underlying processes, for example, spread options. However, the market observation of implied correlation skew examplifies how inaccurate the constant correlation assumption in the multivariate lognormal model can be. In this paper, we study alternative modeling approaches that generate implied correlation s...

Han, Chuan-Hsiang. Singular Perturbations on Non-Smooth Boundary Problems in Finance. (Under the direction of Jean-Pierre Fouque.) In this work we apply asymptotic analysis on compound options, American options, Asian options, and variance (or volatility) contracts in the context of stochastic volatility models. Singular perturbation techniques are primarily used. A singular-regular perturbatio...

This article introduces a new efficient simulation smoother and disturbance smoother for asymmetric stochastic volatility models where there exists a correlation between today’s return and tomorrow’s volatility. The state vector is divided into several blocks where each block consists of many state variables. For each block, corresponding disturbances are sampled simultaneously from their condi...

This paper presents a model for asset returns incorporating both stochastic volatility and jump e ects. The return process is driven by two types of randomness: small random shocks and large jumps. The stochastic volatility process is a ected by both types of randomness in returns. Speci cally, in the absence of large jumps, volatility is driven by the small random shocks in returns through a G...

In this companion paper to “Optimal Investment with Transaction Costs and Stochastic Volatility Part I: Infinite Horizon”, we give an accuracy proof for the finite time optimal investment and consumption problem under fast mean-reverting stochastic volatility of a joint asymptotic expansion in a time scale parameter and the small transaction cost. AMS subject classification 91G80, 60H30. JEL su...

We consider the problem of pricing European forward starting options in the presence of stochastic volatility. By performing a change of measure using the asset price at the time of strike determination as a numeraire, we derive a closed-form solution based on Heston’s model of stochastic volatility.

This paper considers the pricing of options when there are jumps in the pricing kernel and correlated jumps in asset prices and volatilities. We extend theory developed by Nelson (1990) and Duan (1997) by considering limiting models for our resulting approximating GARCH-Jump process. Limiting cases of our processes consist of models where both asset price and local volatility follow jump diffus...

We provide a detailed summary of the large and vibrant emerging literature that deals with the multivariate modeling of conditional volatility of financial time series within the framework of stochastic volatility. The developments and achievements in this area represent one of the great success stories of financial econometrics. Three broad classes of multivariate stochastic volatility models ...