Volatility indices reflect the risk-neutral expectation of future volatility implied in option prices, differing from the volatility predicted by historical volatility forecasting frameworks. Prior studies have overlooked the influence of low-frequency macroeconomic factors on the volatility of the derivatives market. This study applied the GARCH-MIDAS model to forecast the VIX (equities) and GVZ (gold) volatility indices, highlighting the synergistic effects of mixed-frequency modeling and economic policy uncertainty (EPU) indices. After risk neutralization, we accounted for the forward-looking nature of volatility indices by incorporating cross-month adjustments to long-term variances under a risk-neutral framework. Empirical results show that incorporating mixed-frequency components improves forecasting accuracy. The results of the model confidence set (MCS) test verify statistical robustness, whereas the evaluation of economic significance highlights practical relevance. Overall, the integration of EPU into risk-neutral GARCH-MIDAS frameworks provides superior predictive performance compared to other approaches and reveals the critical role of macroeconomic uncertainty in volatility forecasting.
Citation: Gaoxiu Qiao, Yunli Bi, Wanmei Cui, Yaxuan Wang. Forecasting volatility indices in stock and gold markets: Synergistic effects of the GARCH-MIDAS model and economic policy uncertainty[J]. Quantitative Finance and Economics, 2026, 10(2): 188-223. doi: 10.3934/QFE.2026009
Volatility indices reflect the risk-neutral expectation of future volatility implied in option prices, differing from the volatility predicted by historical volatility forecasting frameworks. Prior studies have overlooked the influence of low-frequency macroeconomic factors on the volatility of the derivatives market. This study applied the GARCH-MIDAS model to forecast the VIX (equities) and GVZ (gold) volatility indices, highlighting the synergistic effects of mixed-frequency modeling and economic policy uncertainty (EPU) indices. After risk neutralization, we accounted for the forward-looking nature of volatility indices by incorporating cross-month adjustments to long-term variances under a risk-neutral framework. Empirical results show that incorporating mixed-frequency components improves forecasting accuracy. The results of the model confidence set (MCS) test verify statistical robustness, whereas the evaluation of economic significance highlights practical relevance. Overall, the integration of EPU into risk-neutral GARCH-MIDAS frameworks provides superior predictive performance compared to other approaches and reveals the critical role of macroeconomic uncertainty in volatility forecasting.
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Figures(1) / Tables(18)
Gaoxiu Qiao, Yunli Bi, Wanmei Cui, Yaxuan Wang. Forecasting volatility indices in stock and gold markets: Synergistic effects of the GARCH-MIDAS model and economic policy uncertainty[J]. Quantitative Finance and Economics, 2026, 10(2): 188-223. doi: 10.3934/QFE.2026009
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