Portfolio Optimization & Stochastic Volatility Asymptotics
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 which is well-understood. When volatility is fast meanreverting, this is a singular perturbation problem for a nonlinear Hamilton-Jacobi-Bellman PDE, and when volatility is slowly varying, it is a regular perturbation. These analyses can be combined for multifactor multiscale stochastic volatility models. The asymptotics shares remarkable similarities with the linear option pricing problem, using the properties of the Merton risk-tolerance function. We give examples in the family of mixture of power utilities and also we use our asymptotic analysis to suggest a “practical ” strategy which does not require tracking the fast-moving volatility. In this paper, we present formal derivations of asymptotic approximations, but we indicate the steps for a convergence proof in the case of power utility and single factor stochastic volatility, and we assess our approximation in a particular case where there is an explicit solution.