Forecasting market index volatility using Ross-recovered distributions
将罗斯恢复定理应用于国际指数期权数据，分离隐含波动率为预期波动率和风险偏好代理变量，发现基于罗斯恢复的全球风险偏好加权指标能显著提升已实现波动率和指数超额收益的预测效果。
The Ross recovery theorem shows that option data can reveal the market’s true (physical) expectations. We adapt this approach to international index options data (S&P, FTSE, CAC, SMI, and DAX) to improve volatility forecasting. We separate implied volatility into Ross-recovered expected volatility and a risk preference proxy. We investigate the performance of these variables, constructed domestically or globally, to forecast realized volatility as well as index excess returns. The results show evidence of significantly improved forecasts and yield new insights on the international dynamics of risk expectations and preferences. Across indexes, models using Ross-recovered, value-weighted global measures of risk preferences perform best. The findings suggest that the recovery theorem is empirically useful.