Volatility forecasting for low-volatility portfolio selection in the US and the Korean equity markets

Abstract

We consider the problem of low-volatility portfolio selection which has been the subject of extensive research in the field of portfolio selection. To improve the currently existing techniques that rely purely on past information to select low-volatility portfolios, this paper investigates the use of time series regression techniques that make forecasts of future volatility to select the portfolios. In particular, for the first time, the utility of support vector regression and its enhancements as portfolio selection techniques is provided. It is shown that our regression-based portfolio selection provides attractive outperformances compared to the benchmark index and the portfolio defined by a well-known strategy on the data-sets of the S&P 500 and the KOSPI 200.

Keywords:

• Volatility forecasting
• time series regression
• support vector regression
• low-volatility portfolio
• portfolio optimisation

Acknowledgements

The author would like to express sincere thanks to the anonymous reviewers for detailed comments that helped to improve the presentation of the paper.

Notes

No potential conflict of interest was reported by the author.