Forecasting Stock Market Volatility
回顾了多种波动率预测模型（如ARCH、GARCH、隐含波动率等），在统一框架下比较其预测能力，发现简单模型难以被超越，仅动量模型和日内数据模型显著提升预测精度。
Volatility as a measure of investment risk is widely accepted by academic researchers and industry professionals and has become ubiquitous in investment analysis. Furthermore, it is among the few financial variables that exhibit predictable time variation. Hence, there is an extensive amount of literature describing volatility models and assessing their forecasting power. This article provides a discussion of the prominent models and compares them in a unified notation framework. The empirical analysis shows that it is hard to outperform even simple trailing variance–type models. Autoregressive conditional heteroskedasticity (ARCH), generalized ARCH (GARCH), implied volatility, asymmetric, and seasonal models hardly improve forecasts despite added complexity. In this study, only momentum-based and intraday data–based models improved predictive accuracy significantly.