Forecasting Stock Market Volatility and Application to Volatility Timing Portfolios

Abstract

This study predicts stock market volatility and applies them to the standard problem in finance, namely, asset allocation. Based on machine learning and model averaging approaches, we integrate the drivers’ predictive information to forecast market volatilities. Using various evaluation methods, we verify that those high-dimensional models have better predictive performance relative to the standard volatility models. Furthermore, we construct volatility timing portfolios and discover that portfolios based on high-dimensional models mostly yield higher Sharpe ratios compared with the market.

Keywords: Asset Allocation, Machine Learning, Model averaging, Volatility forecasting, Volatility timing portfolio