The informational role of option trading volume in the S&P 500 futures options markets

Abstract

This paper analyses the dynamic relations between future price volatility of the S&P 500 index futures and trading volume of S&P 500 futures options to examine the informational role of the option volume in predicting the future price volatility. Using a pooled cross-sectional and time-series data framework, the paper uses the error components and dummy variable models to allow for the relations between volatility and volume to vary by the option's time-to-maturity and moneyness. The results suggest that previous call and put volumes have a strong predictive ability with respect to the future price volatility. The results also indicate that the future price volatility has a leading positive effect on the option volume, but that the rises and falls in volatility exert asymmetric influences on the option volume. These findings support the hypothesis that both the information- and hedge-related trading explain most of the trading volume of S&P 500 futures options.

Notes

In addition, unlike the implied volatilities in which each specific option maturity has its own distinct implied volatility, the GARCH volatilities do not distinguish between maturity-specific volatilities and thus the same conditional volatility is used for the expected future volatility of all option maturities. Thus, GARCH procedures do not provide maturity-specific volatilities which are important in our pooled cross-sectional and time-series analysis of the volume-volatility relations.

Although the futures options data from the CME list implied volatility of the futures options, the CME does not specify the nature of these volatilities and the procedures used to compute them. Thus, implied volatilities are estimated using Whaley's (1986) procedure. However, the mean and variance of the estimated implied volatilities are very close to those reported by the CME.

A futures option is considered to be near the money when the ratio of the spot index value to the exercise index value is between 0.975 and 1.025. A call (put) option is out-of-the-money (in-the-money) when the spot index to exercise index ratio is less than 0.975, and a call (put) option is in-the-money (out-of-the-money) when the spot index to exercise index ratio is greater than 1.025. Slight variations in these upper and lower limits of the moneyness classes do not materially affect the results in Tables 3–7.

The results of the Phillips–Perron unit root tests are not affected when both trend and intercept terms are allowed or ignored in the tests.

The results that follow are not materially affected when estimation is executed using an asymmetric lag structure that allows for higher than five lagged terms in either the volatility or the volume series.

The estimation of dummy variable model for Equations 3 and 4 also provides the intercept and t-value for each of the 40 distinct cross-sections in the data. These intercepts are not reported in Table 3 for brevity.

The equations for I _t and V _t are also estimated by the seemingly unrelated regression (SUR) method to check the possible impact of accounting for error-related bias on the estimated parameters. The SUR-based results are very similar to the OLS results in Table 3.

The negative (positive) change in volatility variable in Table 6 is constructed as the product of the negative (positive) change in volatility and a dummy variable that takes the value one when the volatility change is negative (positive) and zero otherwise.