The authors examine whether greater futures-trading activity (volume and open interest) is associated with greater equity volatility. They partition each trading activity series into expected and unexpected components, and document that while equity volatility covaries positively with unexpected futures-trading volume, it is negatively related to forecastable futures-trading activity. Further, though futures-trading activity is systematically related to the futures contract life cycle, the authors find no evidence of a relation between the futures life cycle and spot equity volatility. These findings are consistent with theories predicting that active futures markets enhance the liquidity and depth of the equity markets.

Following the crash of 1987, one contentious regulatory issue has been whether margin activity exacerbated the decline in equity values. The authors contrast the crash behavior of NASDAQ securities eligible for margin trading with the behavior of ineligible ones. Consistent with the hypothesis that margin-eligible securities were more frequently subjected to margin calls and forced sales, they find that abnormal volumes were uniformly larger for eligible securities. However, there is no evidence that this activity provoked additional price depreciation. Margin-eligible securities actually fell by one percent less than the ineligible securities over the period.

Following the crash of 1987, one contentious regulatory issue has been whether margin activity exacerbated the decline in equity values. We contrast the crash behavior of NASDAQ securities eligible for margin trading with the behavior of ineligible ones. Consistent with the hypothesis that margin‐eligible securities were more frequently subjected to margin calls and forced sales, we find that abnormal volumes were uniformly larger for eligible securities. However, there is no evidence that this activity provoked additional price depreciation. Margin‐eligible securities actually fell by one percent less than the ineligible securities over the period.

We use predictions of aggregate stock return variances from daily data to estimate time‐varying monthly variances for size‐ranked portfolios. We propose and estimate a single factor model of heteroskedasticity for portfolio returns. This model implies time‐varying betas. Implications of heteroskedasticity and time‐varying betas for tests of the capital asset pricing model (CAPM) are then documented. Accounting for heteroskedasticity increases the evidence that risk‐adjusted returns are related to firm size. We also estimate a constant correlation model. Portfolio volatilities predicted by this model are similar to those predicted by more complex multivariate generalized‐autoregressive‐conditional‐heteroskedasticity (GARCH) procedures.

We use predictions of aggregate stock return variances from daily data to estimate time-varying monthly variances for size-ranked portfolios. We propose and estimate a single factor model of heteroskedasticity for portfolio returns. This model implies time-varying betas. Implications of heteroskedasticity and time-varying betas for tests of the capital asset pricing model (CAPM) are then documented. Accounting for heteroskedasticity increases the evidence that risk-adjusted returns are related to firm size. We also estimate a constant correlation model. Portfolio volatilities predicted by this model are similar to those predicted by more complex multivariate generalized-autoregressive-conditional-heteroskedasticity (GARCH) procedures.

The authors use predictions of aggregate stock return variances from daily data to estimate time-varying monthly variances for size-ranked portfolios. The authors propose and estimate a single factor model of heteroskedasticity for portfolio returns. This model implies time-varying betas. Implications of heteroskedasticity and time-varying betas for tests of the capital asset pricing model are then documented. Accounting for heteroskedasticity increases the evidence that risk-adjusted returns are related to firm size. The authors also estimate a constant correlation model. Portfolio volatilities predicted by this model are similar to those predicated by more complex multivariate generalized autoregressive conditional heteroskedasticity procedures.

Hendrik Bessembinder, Paul J. Seguin, Price Volatility, Trading Volume, and Market Depth: Evidence from Futures Markets, The Journal of Financial and Quantitative Analysis, Vol. 28, No. 1 (Mar., 1993), pp. 21-39