Abstract
This study investigates the economic and financial drivers of volatility changes and integrates them into stock market volatility forecasting. We first collect a diverse set of predictor variables and analyze them within a unified framework. We discover that only a small number of variables contain significant predictive information, and that the Chinese stock market return significantly predicts U.S. stock market volatility. Using the HAR-LASSO procedure, we integrate the drivers’ predictive information and forecast short-term, medium-term, and long-term market volatilities. Through various volatility timing strategies, we verify that HAR-LASSO-based portfolios lead to outstanding investment performance, regardless of the strategies and forecasting horizons. These results not only economically justify our procedure, but also provide meaningful financial implications of accurate volatility forecasting.
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