A bivariate generalized autoregressive conditional heteroscedasticity-in-mean study of the relationship between return variability and trading volume in international futures markets (original) (raw)
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A Bivariate GARCH approach to the futures volume-volatility issue
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1 Related sets of models in the information literature are rational expectations models and asymmetric information models. Rational expectation models associate prices to private information signals, while asymmetric information models emphasize intraday relationships. Rational expectations models typically treat volume as a byproduct of the market mechanism. The intraday asymmetric models show that volume will concentrate at certain times within the day, creating the familiar U-shaped volume and volatility curves. See Grossman [1989] for a collection of papers examining rational expectations models. Admati and Pfleiderer [1988] and Kyle [1985]) are examples of intraday asymmetric models. See Admati [1991 ] for a review of both type s of models.
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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.
The North American Journal of Economics and Finance, 2019
The purpose of this study is to examine the relationships between return and trading volume as well as between return volatility and trading volume by analyzing the asymmetric relationships of contemporaneity and lead-lags between these factors for the S&P 500 VIX Futures Index. We apply the threshold model with the GJR-GARCH framework for empirical analysis herein. The main findings demonstrate that the threshold effects exist in both the contemporaneous and lead-lag relationships between return-volume and volatility-volume. Moreover, the delayed effects of a one-trading-day lag through to three-trading-day lags exist from trading volume to returns and return volatility. Larger trading volume is beneficial for investors to gain returns, but it also leads to higher volatility. The implication of our findings offers a suggestion as to the opportune timing for investors to buy S&P 500 VIX Futures.
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This paper concerns the relationship between the variability of the daily price change and the daily volume of trading on the speculative markets. Our work extends the theory of speculative markets in two ways. First, we derive from economic theory the joint probability distribution of the price change and the trading volume over any interval of time within the trading day. And second, we determine how this joint distribution changes as more traders enter (or exit from) the market. The model's parameters are estimated by FIML using daily data from the 90-day T-bills futures market. The results of the estimation can reconcile a conflict between the price variability-volume relationship for this market and the relationship obtained by previous investigators for other speculative markets.
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Trading imbalances reflect the quality of market information and may contain more information than the number of trades or trading volume. In order to better understand how trading imbalances play a role different from traditional variables (i.e., number of trades and trading volume) in explaining volatility, we use intraday data to examine the dynamic relations among return volatility, trading imbalances, and traditional variables for E-mini S&P 500 futures and Japanese Yen futures contracts, respectively. The Granger-causality tests indicate strong feedback effects between volatility and trading variables, confirming the informationbased and hedging-based trading. We also compare the results of the traditional volumes and trading imbalances through variance decomposition and impulse responses analysis. It is shown that the sequential arrival of private information through trading imbalance is more important in explaining return volatility than the traditional variables, which are a proxy for the public information.
Futures trading volume as a determinant of prices in different momentum phases
International Review of Financial Analysis, 2006
Recent studies contend that trading volume has predictive power for ex ante stock prices, particularly small stocks that do not react quickly to macroeconomic information. This study postulates that a significant amount of macro-information that flows on to stock markets is derived from derivative markets. We examine the impact of short-term futures trading volume and prices on cash stock prices using a case study of 15-min data from the Australian stock index futures market which reports actual trading volume. After applying vector error correction modelling (VECM), variance decomposition and impulse functions, we conclude that futures prices provide a short-term information lead to stock prices that dominates trading volume effects. We also observe asymmetric changes in the impact of trading volume between bull and bear price momentum phases and after large trading volume shocks. These results suggest that, in future, studies on trading volume should control for the cross-correlation impact from derivative prices and the differential impact of trading phases. D 2004 Elsevier Inc. All rights reserved. JEL classification: G15; C52
The Effect of Futures Trading on Spot Price Volatility: Evidence for Brent Crude Oil Using Garch
Journal of Business Finance & Accounting, 1992
There has been widespread interest in the effects of futures trading on prices in the underlying spot market. It has often been claimed that the onset of derivative trading will destabilize the associated spot market and so lead to an increase in spot price volatility there. Others have argued to the contrary, stating that the introduction of futures trading will stabilize prices and so lead to a decrease in price volatility. It has been suggested,' however, that the debate cannot be resolved wholly on a theoretical level and so should be analyzed by empirical investigation.
Journal of International Financial Markets, Institutions and Money, 2005
2), [253][254][255][256][257][258][259][260]. Lamoureux and Lastrapes (1990) analyzing the persistence of GARCH effects on the return of nine international stock exchange indices. The result in all markets shows that the inclusion of trading volume does not substantially reduce the persistence of conditional volatility. This coincidence in results enables us to support the argument of [Sharma, J.L., Mougoue, M., Kamath, R., 1996. Heteroscedasticity in stock market indicator return data: volumen versus GARCH effects. Applied Financial Economics (6), 337-342] that, in market return, macroeconomic factors prevail over those of particular companies. Unexpected trading volume is used as a proxy variable for the information flow rate. Even though this variable is unable to reduce GARCH effects, its greater impact on volatility suggests that this is not so much affected by the level of market activity but rather by unexpected changes in this level.