Dynamic Mean-Variance Efficient Fractional Kelly Portfolios in a Stochastic Volatility Model

64 Pages Posted: 20 Aug 2020

See all articles by Xue Dong He

Xue Dong He

The Chinese University of Hong Kong - Department of Systems Engineering and Engineering Management

Zhao Li Jiang

The Chinese University of Hong Kong (CUHK) - Department of Systems Engineering and Engineering Management; National University of Singapore (NUS) - Risk Management Institute

Date Written: August 13, 2020

Abstract

Fractional Kelly portfolios are popular investment strategies in the market. In this paper, we improve the mean-variance efficiency of a fractional Kelly portfolio by minimizing the variance of the return of a portfolio subject to the constraint that the expected return rate of the portfolio is as high as that of the fractional Kelly portfolio. We consider so-called equilibrium portfolio strategies due to time inconsistency caused by the mean-variance criterion. We drive an equilibrium strategy in closed form and show that it reduces the variance of portfolio return compared to the fractional Kelly portfolio, although the reduction is quantitatively small.

Keywords: fractional Kelly portfolios, dynamic mean-variance analysis, stochastic volatility, time inconsistency, equilibrium strategies

JEL Classification: G11, D81, C61

Suggested Citation

He, Xue Dong and Jiang, Zhao Li, Dynamic Mean-Variance Efficient Fractional Kelly Portfolios in a Stochastic Volatility Model (August 13, 2020). Available at SSRN: https://ssrn.com/abstract=3670621 or http://dx.doi.org/10.2139/ssrn.3670621

Xue Dong He (Contact Author)

The Chinese University of Hong Kong - Department of Systems Engineering and Engineering Management ( email )

505 William M.W. Mong Engineering Building
The Chinese University of Hong Kong, Shatin, N.T.
Hong Kong
Hong Kong

HOME PAGE: http://https://sites.google.com/site/xuedonghepage/home

Zhao Li Jiang

The Chinese University of Hong Kong (CUHK) - Department of Systems Engineering and Engineering Management ( email )

Hong Kong
China

National University of Singapore (NUS) - Risk Management Institute ( email )

21 Heng Mui Keng Terrace
Level 4
Singapore, 119613
Singapore

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