Intraday Trading by floor traders and customers in futures markets: whose trades drive the volatility-volume relation?(Author abstract)

Introduction

Under the mixture-of-distributions hypothesis in Clark (1973) and Tauchen and Pitts (1983), there exists a positive relation between prices and trading volume because both respond to the same underlying latent information. On the other hand, there is a considerable interest in the impact of trader type on volatility. Theoretic models such as De Long et al. (1990) and Campbell and Kyle (1993) show that trading by uninformed noise traders increases market volatility. Shalan (1993) reaches a similar conclusion that the least informed traders increase volatility, whereas informed trading reduces volatility by removing uncertainty in the marketplace. In addition, Harris and Raviv (1993) show that even in the absence of noise traders, traders with homogenous prior information but different processing procedure can result in a positive volatility-volume relation. (1)

This paper aims to shed light on the question of whose trades drive the volatility-volume relation. Such a study presents an empirical test of the financial theories discussed earlier. In addition, the effect of trading parties on market volatility is an important public policy issue. For example, the advocates of throwing sand in the gear argue that transaction tax should be imposed to reduce volatility. One can argue that such a tax be imposed specifically on retail customers if only volume from trading by retail customers is positively related to price volatility. (2)

The paper makes four improvements over the previous studies. First, the unique transaction dataset in this study codes both parties for each trade, e.g., a floor trader against a retail customer. In contrast, data in previous studies such as Manaster and Mann (1996) and Wiley and Daigler (1998) contain trader type only for one of the two parties involved in a trade. The use of only one party in identification can hide the true impact of trader types on volatility. This paper is the most vigorous examination that reveals true pairings of parties and their effect on market volatility.

Second, minute-by-minute transaction data are used in this study, whereas previous studies use daily data. Given the speed of trading and price adjustments in future markets, studies with low frequency data easily can fail to capture the volatility-volume relation that is evident only in intraday data. Therefore, results from this study can complement and contrast with those from the previous studies to assess whether the volatility-volume relation is robust to market microstructures such as the frequency of data.

The third improvement comes from the fact that Hausman specification test is used to test the presence of simultaneity between volatility and volume. In the presence of simultaneity bias, ordinary least squares (OLS) method is replaced by the generalized method of moments (GMM) approach with instrumental variables to study the contemporaneous intraday volatility-volume relation. The removal of simultaneity bias makes the results in the paper more reliable.

Finally, in addition to using a vector-autoregressive (VAR) model to answer the question of which types of trading Granger-cause volatility, volume is decomposed into expected and unexpected parts using autoregressive integrated moving average (ARIMA) models to study the contemporaneous intraday volatility-volume relation.