How do financial features affect volatility forecasts? Evidence from the oil market and other markets

This study uses six types of generalized autoregressive conditional heteroscedasticity (GARCH) models to estimate the volatility of 28 assets dispersed in the oil, metal, stock, and exchange rate markets, and it explores whether the three financial features of price level, distribution, and leverage effects exist in these four markets. Through an accuracy evaluation, this study also investigates how the financial features affect volatility forecasts and which feature plays the most substantial role in volatility forecasts in each market. Empirical results show that the assets in the oil (resp. exchange rate) market have the greatest (resp. smallest) risk. Moreover, the fat-tailed effect most significantly exists in these four markets, followed by the price level, skewness, and leverage effects. Notably, a negative (resp. positive) volatility elasticity exists in the oil, exchange rate, and stock (resp. metal) markets. Furthermore, both the price level and distribution effects significantly affect the volatility forecasts in the oil market, whereas only the leverage effect slightly affects the volatility forecasts in the metal market. Conversely, the price level, distribution, and leverage effects slightly affect the volatility forecasts in the stock market, whereas no effect can affect the volatility forecasts in the exchange rate market. The price level effect is the most crucial in volatility forecasts in the oil market, whereas the leverage effect is the most crucial in volatility forecasts in the metal and stock markets. Additionally, the GJR-GARCH-N has the best performance in volatility forecasts among the three asymmetric GARCH models.