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Is Trading Imbalance a Better Explanatory Factor in the Volatility Process? Intraday and Daily Evidence from E-mini S&P 500 Index Futures and Information-Based Hypotheses

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Author Info

  • An-Sing Chen

    (National Chung Cheng University, Taiwan)

  • Hui-Jyuan Gao

    (National Chung Cheng University, Taiwan)

  • Mark Leung

    (The University of Texas at San Antonio)

Abstract

This paper examines trading imbalance as well as traditional trading variables in the volume-volatility relation in futures market. Unlike the majority of studies which utilize daily data, our empirical investigation compares an array of intraday frequencies (from five minutes to one hour) with daily interval. The primary analysis is conducted through a series of GARCH tests and the findings are then confirmed by a set of two-stage least square regressions. Since this paper adopts an information-based framework to explain the volume-volatility relation, unexpected trading variables are used to proxy for new market information. Results indicate that different trading imbalance metrics are useful and more significant than traditional trading variables in explaining the volatility relation for all daily and intraday intervals. Empirical findings support the existence of asymmetric information hypothesis at all intervals. On the other hand, mixture of distributions and difference in opinion hypotheses are validated in only some intraday intervals. Moreover, not only are the conclusions from daily observations not the same as the ones from intraday counterparts but also there are differences in the results between longer and shorter intraday intervals.

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File URL: http://business.utsa.edu/wps/MSS/0039MSS-045-2008.pdf
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Bibliographic Info

Paper provided by College of Business, University of Texas at San Antonio in its series Working Papers with number 0039.

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Length: 35 pages
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Date of revision:
Handle: RePEc:tsa:wpaper:0039

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Keywords: Futures markets; price volatility; trading imbalance; number and volume of trades; asymmetric information; difference in opinion; mixture of distributions; GARCH and persistence effect.;

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