We construct the China Crude Oil Volatility Index (CNOVX)—the first model-free, optionimplied measure of forward-looking oil price risk for China—using INE crude oil options from 2021 to 2024 and an adapted CBOE methodology that accounts for sparse strike availability via smooth interpolation and extrapolation. Our results show that CNOVX increases with trading activity in the futures market, declines with option volume, and is strongly predicted by the 30-day realized variance of the SC crude oil futures contract. External shocks, including the Russia–Ukraine conflict and the Geopolitical Risk Index, significantly elevate CNOVX levels. During the COVID-19 pandemic, mortality risk intensifies the volatility-amplifying role of futures trading and strengthens the volatility-dampening effect of options, while confirmed case counts have weaker influence. We further document a pronounced asymmetric leverage effect: negative futures returns raise CNOVX more than positive returns of equal size. However, volatility feedback effects are negligible, as changes in implied volatility respond primarily to contemporaneous market conditions. Overall, CNOVX serves as a timely and informative benchmark for monitoring risk in China’s evolving crude oil derivatives market, with valuable implications for investors, hedgers, and policymakers.

In this paper, we propose a new approach to test the presence of GARCH Effects of China stock market. Our method is based on Maximal Overlap Discrect Wavelet Transform (MODWT)that provides a natural platform to investigate the volatility behavior at different time scales without losing any information.The empirical results show that GARCH effects are more significant at short time horizons as compared to long. Furthermore, when compared the modeling results of GARCH-t with that of EGARCH-t, it yields very higher effectiveness to capture the leverage effect of financial time series at relavant time scales.