An examination of the complementary volume–volatility information theories

Abstract

The volume–volatility relationship during the dissemination stages of information flow is examined by analyzing various theories relating volume and volatility as complementary rather than competing models. The mixture of distributions hypothesis, sequential arrival of information hypothesis, the dispersion of beliefs hypothesis, and the noise trader hypothesis all add to the understanding of how volume and volatility interact for different types of futures traders. An integrated picture of the volume–volatility relationship is provided by investigating the dynamic linear and nonlinear associations between volatility and the volume of informed (institutional) and uninformed (the general public) traders. In particular, the trading behavior explanation for the persistence of futures volatility, the effect of the timing of private information arrival, and the response of institutional traders to excess noise trading risk is examined. © 2008 Wiley Periodicals, Inc. Jrl Fut Mark 28:963–992, 2008

Dynamic spillover effects in futures markets: UK and US evidence

“…Bryant et al (2006) reject the hypothesis that large speculator and small trader activity, calculated as twice the level of open interest, causes futures volatility. Along similar lines, Chen and Daigler (2008) find that the general public's and institutional traders' volume does not Granger cause futures volatility of the S&P 500 stock index. Following Xu et al (2006), the sensitivity of trading volume to lagged return volatility can be explained by microstructure, public information or inventory control effects.…”

What drives the volume–volatility relationship on Euronext Paris?

“…As documented by Chen and Daigler (2008), this hypothesis was extended in recent works to highlight a strong relationship between information flow and market activity. The MDH consider information flow as a latent common factor that affects both of trading volume and stock prices.…”

Intraday jumps and trading volume: a nonlinear Tobit specification

“…Different empirical studies have tested the MDH (Epps and Epps 1976;Gallant et al 1992;Lamoureux and Lastrapes 1990;Chen et al 2001;Wu and Xu 2000a, b;Rahman et al 2002;Li and Wu 2006;He and Velu 2014). Overall, these studies showed that the introduction of volume to explain volatility is not rejected and that it helps to reduce volatility persistence (Chen and Daigler 2008;Alsubaie and Najand 2009;Louhichi 2011, etc. ), but conclusions about the MDH vary across studies and the samples under consideration.…”

“…In fact, they found that volume associated with negative returns is larger than volume associated with non-negative returns. At the same time, using a trivariate GARCH model, Chen and Daigler (2008) highlighted a nonlinear volume-volatility relationship on futures markets. Meanwhile, Bradley et al (2007) explored the role of trading volume in making out-of-sample forecasts of stock market volatility around the time of the 24 October 1929.…”