Future Stock Market Volatility Research Articles

Volatility Forecasts, Trading Volume and the ARCH vs Option-Implied Volatility Tradeoff

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. This paper seeks to learn something about both of these issues by investigating empirically the role of trading volume (a) in predicting the relative informativeness of volatility forecasts produced by ARCH models versus the volatility forecasts derived from option prices, and (b) in improving volatility forecasts produced by ARCH and option models and combinations of models. We find that if trading volume was low during period t-1 then ARCH is much more important than options for forecasting future stock market volatility. Conversely, if volume was high during period t-1, then option-implied volatility is much more important than ARCH for forecasting future volatility. Our findings reveal an important regime-switching role for trading volume and suggest that option markets may be more efficient in high volume states. Results from various tests also uncover possible sources of volume-related nonlinearity in the relationship between past and future return innovations as captured by asymmetric ARCH models.

The quality of market volatility forecasts implied by S&P 100 index option prices

This study examines the performance of the S&P 100 implied volatility as a forecast of future stock market volatility. The results indicate that the implied volatility is an upward biased forecast, but also that it contains relevant information regarding future volatility. The implied volatility dominates the historical volatility rate in terms of ex ante forecasting power, and its forecast error is orthogonal to parameters frequently linked to conditional volatility, including those employed in various ARCH specifications. These findings suggest that a linear model which corrects for the implied volatility's bias can provide a useful market-based estimator of conditional volatility.