Mean-variance portfolio selection under Volterra Heston model
Abstract
Motivated by empirical evidence for rough volatility models, this paper investigates continuous-time mean-variance (MV) portfolio selection under the Volterra Heston model. Due to the non-Markovian and non-semimartingale nature of the model, classic stochastic optimal control frameworks are not directly applicable to the associated optimization problem. By constructing an auxiliary stochastic process, we obtain the optimal investment strategy, which depends on the solution to a Riccati-Volterra equation. The MV efficient frontier is shown to maintain a quadratic curve. Numerical studies show that both roughness and volatility of volatility materially affect the optimal strategy.
Keywords
Cite
@article{arxiv.1904.12442,
title = {Mean-variance portfolio selection under Volterra Heston model},
author = {Bingyan Han and Hoi Ying Wong},
journal= {arXiv preprint arXiv:1904.12442},
year = {2020}
}
Comments
Final version, 22 pages, 5 figures, to appear in Applied Mathematics & Optimization