Robust portfolio optimization with multi-factor stochastic volatility

TL;DR

This paper develops a robust portfolio optimization framework under multi-factor stochastic volatility models, analyzing worst-case scenarios, derivative trading impacts, and extensions to jump risks, supported by numerical experiments.

Contribution

It introduces an analytical solution for robust portfolio optimization considering multi-factor volatility and jump risks, incorporating derivative trading effects and welfare analysis.

Findings

Robust strategies outperform non-robust ones under model uncertainty.

Derivative trading influences optimal portfolio choices significantly.

Numerical results show utility loss under ambiguity and jump risks.

Abstract

This paper studies a robust portfolio optimization problem under the multi-factor volatility model introduced by Christoffersen et al. (2009). The optimal strategy is derived analytically under the worst-case scenario with or without derivative trading. To illustrate the effects of ambiguity, we compare our optimal robust strategy with some strategies that ignore the information of uncertainty, and provide the corresponding welfare analysis. The effects of derivative trading to the optimal portfolio selection are also discussed by considering alternative strategies. Our study is further extended to the cases with jump risks in asset price and correlated volatility factors, respectively. Numerical experiments are provided to demonstrate the behavior of the optimal portfolio and utility loss.