Option-Implied Equity Premium Predictions via Entropic Tilting
提出一种新方法，利用期权市场信息改进股权溢价密度预测，通过熵倾斜将期权隐含风险中性分布的二阶矩与GARCH和SV模型结合，提升预测准确性。
We propose a new method to improve density forecasts of the equity premium using information from options markets. We obtain predictive densities from stochastic volatility (SV) and GARCH models, which we then tilt using the second moment of the risk-neutral distribution implied by options prices while imposing a non-negativity constraint on the equity premium. By combining the backward-looking information contained in the GARCH and SV models with the forward-looking information from options prices, our procedure improves the performance of predictive densities. Using density forecasts of the U.S. equity premium from January 1990 to December 2014, we find that tilting leads to more accurate predictions using statistical and economic criteria.