We extend earlier work on the information content of the US implied volatility index (VIX) in cross-market forecasting by providing evidence on the information content of several non-VIX market volatility indexes on local and regional stock market volatility. To do so, we employ dynamic automated machine learning models to examine their forecasting performance and spillover effects by considering four advanced markets (the US, Germany, Japan, and Hong Kong) and two emerging markets (China and India). The sample includes five-minute high-frequency data from March 2, 2015, to March 1, 2024. The results show that (1) the local volatility index has the most significant forecasting effect on the local stock market, particularly during periods of market stability; (2) the VIX has significant forecasting spillover effects, but its forecasting role is weaker in the two emerging markets than in the developed markets; (3) after the outbreak of the COVID-19 pandemic, the predictability of the US (Chinese) stock market declined (increased) and the information content of the reconstructed Chinese market implied volatility index (CVIX) rose significantly, surpassing the role of VIX; and (4) the spillover effect is asymmetric, and the intensity of cross-market contagion is greater during market turbulence, especially in developed markets with significant resonance effects. The findings are useful for investors and managers in managing portfolio risks and designing investment strategies across regions and markets and have significant implications for policy makers and central banks as they implement effective financial stability policies to deal with the transmission of non-VIX volatility spillovers to national stock markets.
扫码关注我们
求助内容:
应助结果提醒方式:
