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Information Flow between Price Change and Trading Volume in Gold Futures Contracts

Author

Listed:
  • Ramaprasad Bhar

    (School of Banking and Finance, University of New South Wales, Australia)

  • Shigeyuki Hamori

    (Graduate School of Economics, Kobe University, Japan)

Abstract

This article examines the pattern of information flow between the percentage price change and the trading volume in gold futures contracts using daily data over a ten-year period. We employ the robust two-step procedure proposed by Cheung and Ng (1996) to detect the causality in variance. We find evidence of strong contemporaneous causality that is indicative of the mixture of distribution hypothesis of information flow. We also detect, although not as strong, lagged causality running from percentage price change to trading volume. This indicates mild support for sequential information flow as well directed from price change to trading volume. This is contrary to the documented behavior in agricultural futures and crude oil futures, where bi-directional causality has been reported. We hypothesize that this is probably due to the special nature of gold as a commodity and the fact that the gold market takes on added importance when the equity market underperforms.

Suggested Citation

  • Ramaprasad Bhar & Shigeyuki Hamori, 2004. "Information Flow between Price Change and Trading Volume in Gold Futures Contracts," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 3(1), pages 45-56, April.
  • Handle: RePEc:ijb:journl:v:3:y:2004:i:1:p:45-56
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    References listed on IDEAS

    as
    1. Engle, Robert F & Ito, Takatoshi & Lin, Wen-Ling, 1990. "Meteor Showers or Heat Waves? Heteroskedastic Intra-daily Volatility in the Foreign Exchange Market," Econometrica, Econometric Society, vol. 58(3), pages 525-542, May.
    2. Tauchen, George E & Pitts, Mark, 1983. "The Price Variability-Volume Relationship on Speculative Markets," Econometrica, Econometric Society, vol. 51(2), pages 485-505, March.
    3. Moosa, Imad A. & Silvapulle, Param, 2000. "The price-volume relationship in the crude oil futures market Some results based on linear and nonlinear causality testing," International Review of Economics & Finance, Elsevier, vol. 9(1), pages 11-30, February.
    4. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(1), pages 109-126, March.
    5. Jennings, Robert H & Starks, Laura T & Fellingham, John C, 1981. "An Equilibrium Model of Asset Trading with Sequential Information Arrival," Journal of Finance, American Finance Association, vol. 36(1), pages 143-161, March.
    6. Cheung, Yin-Wong & Ng, Lilian K., 1996. "A causality-in-variance test and its application to financial market prices," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 33-48.
    7. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    8. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    9. Ross, Stephen A, 1989. " Information and Volatility: The No-Arbitrage Martingale Approach to Timing and Resolution Irrelevancy," Journal of Finance, American Finance Association, vol. 44(1), pages 1-17, March.
    10. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1992. "Stock Prices and Volume," Review of Financial Studies, Society for Financial Studies, vol. 5(2), pages 199-242.
    11. Andersen, Torben G, 1996. "Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
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    Cited by:

    1. Wo-Chiang Lee & Hui-Na Lin, 2012. "Threshold effects in the relationships between USD and gold futures by panel smooth transition approach," Applied Economics Letters, Taylor & Francis Journals, vol. 19(11), pages 1065-1070, July.
    2. Inagaki, Kazuyuki, 2007. "Testing for volatility spillover between the British pound and the euro," Research in International Business and Finance, Elsevier, vol. 21(2), pages 161-174, June.
    3. Hanabusa, Kunihiro, 2009. "Causality relationship between the price of oil and economic growth in Japan," Energy Policy, Elsevier, vol. 37(5), pages 1953-1957, May.
    4. Takashi Miyazaki & Shigeyuki Hamori, 2013. "Testing for causality between the gold return and stock market performance: evidence for ‘gold investment in case of emergency’," Applied Financial Economics, Taylor & Francis Journals, vol. 23(1), pages 27-40, January.
    5. Fenghua, Wen & Xiaoguang, Yang, 2009. "Empirical study on relationship between persistence-free trading volume and stock return volatility," Global Finance Journal, Elsevier, vol. 20(2), pages 119-127.
    6. Mala Dutt & Sanjay Sehgal, 2018. "Domestic and International Information Linkages between Gold Spot and Futures Markets: An Empirical Study for India," Metamorphosis: A Journal of Management Research, , vol. 17(1), pages 1-17, June.
    7. Zhong, Wanxing & Kong, Rui & Chen, Guang, 2019. "Gold prices fluctuation of co-movement forecast between China and Russia," Resources Policy, Elsevier, vol. 62(C), pages 218-230.

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    More about this item

    Keywords

    autonomy; price-volume dynamics; spillover; causality; GARCH model;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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