Market expectations of future return volatility play a crucial role in finance; so too does our understanding of the process by which information is incorporated in security prices through the trading process. The authors seek to learn something about both of these issues by investigating empirically the role of trading volume in predicting the relative informativeness of volatility forecasts produced by ARCH models versus the volatility forecasts derived from option prices and in improving volatility forecasts produced by ARCH and option models and combinations of models. Daily and monthly data are explored. The authors find that if trading volume was low during period $t – 1$ relative to the recent past, then 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, then 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, their 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.
JEL classification: G0
Key words: ARCH, volatility forecasting, VIX, options-implied volatility, trading volume
The authors thank Ron Giammarino, Lisa Kramer, Alan Kraus, and participants in various seminars and conferences for helpful discussions and the Social Sciences and Humanities and Research Council of Canada for financial support. The views expressed here are the authors’ and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors’ responsibility.
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