An examination of linear and nonlinear causal relationships between price variability and volume in petroleum futures markets

Abstract

Fujihara and Mougoue tegralreveals significant bidirectional nonlinear causal relationships between the filtered returns and volume series. Using a third-order moment test, this study finds that the nonlinear dependence in the futures returns and volume series arises from the variance, rather than the mean, of the process. Consequently, the filtered returns and volume series are adjusted for conditional heteroscedasticity. The study then examines the GARCHfiltered returns and volume series and finds that, even after adjusting for volatility effects, there is still strong evidence of bidirectional nonlinear Granger causality and concludes that the nonlinear process may influence both the mean and variance of futures returns and volume. The finding of strong nonlinear causal relationships between petroleum futures returns and trading volume implies that knowledge of current trading volume improves the ability to forecast futures prices. Thus, the results of this study should be useful to regulators, practitioners, and futures markets participants whose success hinges crucially on the ability to forecast futures price movements.

Causality in quantiles and dynamic stock return–volume relations

“…Granger et al (1986) and Cheung and Ng (1996) consider testing non-causality in conditional variance, whereas Hiemstra and Jones (1994) derive a test for nonlinear causal relations. These tests have been widely used in the literature (e.g., Fujihara and Mougoué, 1997;Silvapulle and Choi, 1999;Chen et al, 2001;Ciner, 2002;Lee and Rui, 2002). A serious limitation of this approach is that non-causality in mean (or in variance) need not carry over to other distribution characteristics or different parts of the distribution.…”

Back to the Futures: An Assessment of Commodity Market Efficiency and Forecast Error Drivers

“…The adoption of the log form is also a common practice in the statistical analysis of the prices of futures contracts (Garbade and Silber, 1983;Serletis and Scowcroft, 1991;Fortenbery and Zapata, 1993;Fujihara and Mougoué, 1997;Moosa and Silvapulle, 2000;Joyeux and Milunovich, 2010). Using lower case letters for logs, the level equation can be written as:…”

Agent-based computational modeling of the stock price–volume relation

“…By further using the delta method, 12 Hiemstra and Jones [32, pp.1660-1662] suggested using a consistent estimator for r 2 ðm; Lx; Ly; eÞ in Eq. (23) to conduct the test empirically. Note that a significant positive value in Eq.…”

“…Note that a significant positive value in Eq. (23) suggests that fY t g does Granger-cause fX t g (nonlinearly). Nevertheless, a significant negative test statistic is indicative that ''knowledge of the lagged values of Y confounds the prediction of X '' (see Hiemstra and Jones [32, p. 1648], italics added).…”

“…Hiemstra and Jones [32] modified their test slightly to allow the two series under consideration to display ''weak (or short-term) temporal dependence''. Several researchers have already adopted this modified Baek and Brock test to uncover price and volume causal relation in real world financial markets [23,32,50]. In most of the cases, they found bi-directional nonlinear Granger causality in the prices and trading volume.…”