Exploring the Forecasting of Crude Oil, Gold, and Euro Currency Implied Volatility Indices: Insights From the Decomposed Stock Market Volatility

ABSTRACT This paper explores whether the information from the stock market can provide positive contents for the implied volatility prediction in the crude oil market, gold market, and foreign exchange market. Specifically, we investigate the predictive effects of realized continuous volatility, realized jump volatility, positive and negative realized semi‐variations, and signed jumps from the S&P 500 index on three implied volatility indices, OVX (Crude Oil Volatility Index), GVZ (Gold Volatility Index), and EVZ (Euro Volatility Index). We construct a hybrid method by combining parametric models with machine learning to explore the market spillover effects of stock market information on three markets. The empirical results show that realized measures in the stock market can provide incremental information for the prediction of the implied volatility indices, the positive and negative semi‐variations of stock index showing better performance than that of jump volatility. The method of combining FNN with the parametric model shows better performance compared to SVR. The superiority of this hybrid approach is further verified based on the Model Confidence Set test. Furthermore, an economic significance evaluation confirms that the enhanced predictive accuracy translates into significant economic value.