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Herding, Information Cascades and Volatility Spillovers in Futures Markets

Author

Listed:
  • Michael McAleer

    (National Tsing Hua University)

  • Kim Radalj

Abstract

Economists and financial analysts have begun to recognise the importance of the actions of other agents in the decision-making process. Herding is the deliberate mimicking of the decisions of other agents. Examples of mimicry range from the choice of restaurant, fashion and financial market participants, to academic research. Herding may conjure negative images of irrational agents sheepishly following the actions of others, but such actions can be rational under asymmetric information and uncertainty. This paper uses futures position data in nine different markets of the Commodity Futures Trading Commission (CFTC) to provide a direct test of herding behaviour, namely the extent to which small traders mimic the positions of large speculators. Evidence consistent with herding among small traders is found for the Canadian dollar, British pound, gold, S&P 500 and Nikkei 225 futures. Consistent with survey-based results on technical analysis, the positions are significantly correlated with both current and past market returns. Using various time-varying volatility models to accommodate conditional heteroskedasticity, the empirical results are found to be robust to alternative models and methods of estimation. When a test of causality-in-variance is used to analyse if volatility among small traders spills over into spot markets, it is found that spillovers occur only with Nikkei 225 futures. The policy implications of the findings are also discussed

Suggested Citation

  • Michael McAleer & Kim Radalj, 2013. "Herding, Information Cascades and Volatility Spillovers in Futures Markets," Journal of Reviews on Global Economics, Lifescience Global, vol. 2, pages 307-329.
  • Handle: RePEc:lif:jrgelg:v:2:y:2013:p:307-329
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    Cited by:

    1. P. K. Mishra & S. K. Mishra, 2023. "Do Banking and Financial Services Sectors Show Herding Behaviour in Indian Stock Market Amid COVID-19 Pandemic? Insights from Quantile Regression Approach," Millennial Asia, , vol. 14(1), pages 54-84, March.
    2. Sophie van Huellen, 2020. "Approaches To Price Formation In Financialized Commodity Markets," Journal of Economic Surveys, Wiley Blackwell, vol. 34(1), pages 219-237, February.
    3. Papadamou, Stephanos & Kyriazis, Nikolaos A. & Tzeremes, Panayiotis & Corbet, Shaen, 2021. "Herding behaviour and price convergence clubs in cryptocurrencies during bull and bear markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    4. Lin, William & Sun, David & Tsai, Shih-Chuan, 2010. "Does trading remove or bring frictions?," MPRA Paper 37285, University Library of Munich, Germany, revised Jan 2011.
    5. Simões Vieira, Elisabete F. & Valente Pereira, Márcia S., 2015. "Herding behaviour and sentiment: Evidence in a small European market," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 18(1), pages 78-86.
    6. Tian, Xin & Song, Yan & Luo, Chunlin & Zhou, Xiaoyang & Lev, Benjamin, 2021. "Herding behavior in supplier innovation crowdfunding: Evidence from Kickstarter," International Journal of Production Economics, Elsevier, vol. 239(C).

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    Keywords

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    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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