This study comprehensively examines the economic and financial drivers of volatility changes in terms of a cross-country perspective. We exhaustively review a wide range of studies related to financial volatility forecasting and collect a diverse set of prediction variables. By analyzing them...

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...

This study examines the relationship between the weather and intraday investor sentiment. Our results indicate that high temperatures, high humidity, high cloud coverage, and extreme rain negatively affect investors’ moods, whereas high winds and long sunshine durations improve their moods....