Forecasting Chinese stock market volatility with option-implied risk aversion: Evidence from extended realized EGARCH-MIDAS approach

This paper investigates the role of option -implied risk aversion (IRA) in forecasting the Chinese stock market volatility. We derive the IRA from the SSE 50ETF option prices using behavioral pricing kernel theory. We extend the realized EGARCH-MIDAS (REGARCH-MIDAS) model to incorporate the IRA. Furthermore, we propose the REGARCH-MIDAS-IRA-ES model accounting for the extreme IRA information to model and forecast the Chinese stock market volatility. Empirical results based on the extended REGARCH-MIDAS models show that the IRA has a significantly positive impact on the long-term volatility of Chinese stock market, and the extremely positive IRA has a greater impact on the long-term volatility compared to the extremely negative and normal IRA. Moreover, the IRA possesses predictive value for the Chinese stock market volatility. In particular, we observe that the extreme IRA can provide more valuable information to forecast the Chinese stock market volatility. Our proposed REGARCH-MIDAS-IRA-ES model outperforms a variety of competing models in terms of outof -sample volatility forecast. We confirm that the superior forecasting performance of the model is robust to different out -of -sample evaluation approach, different out -of -sample forecast windows, different MIDAS lags and different volatility states. Finally, a volatility -timing strategy demonstrates that incorporating the IRA, and in particular the extreme IRA, leads to economic gains for a mean-variance utility investor.