Mean–variance portfolio selection under Volterra Heston model
Publication in refereed journal
Officially Accepted for Publication

Times Cited
Altmetrics Information

Other information
AbstractMotivated 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.
Acceptance Date24/01/2020
All Author(s) ListBingyan Han, Hoi Ying Wong
Journal nameApplied Mathematics and Optimization
LanguagesEnglish-United States
KeywordsMean–variance portfolio, Volterra Heston model, Riccati–Volterra equations, Rough volatility

Last updated on 2020-23-10 at 01:05