VOLATILITY FORECASTS, TRADING VOLUME, AND THE ARCH VERSUS OPTION‐IMPLIED VOLATILITY TRADE‐OFF
研究交易量如何影响ARCH模型与期权隐含波动率在预测股市波动中的相对重要性，发现低交易量时ARCH更有效，高交易量时期权隐含波动率更关键。
Abstract We investigate empirically the role of trading volume (1) in predicting the relative informativeness of volatility forecasts produced by autoregressive conditional heteroskedasticity (ARCH) models versus the volatility forecasts derived from option prices, and (2) in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. We find that if trading volume was low during period t −1 relative to the recent past, ARCH is at least as important as options for forecasting future stock market volatility. Conversely, if volume was high during period t −1 relative to the recent past, option‐implied volatility is much more important than ARCH for forecasting future volatility. Considering relative trading volume as a proxy for changes in the set of information available to investors, our findings reveal an important switching role for trading volume between a volatility forecast that reflects relatively stale information (the historical ARCH estimate) and the option‐implied forward‐looking estimate.