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Nonlinear dynamics and competing behavioral interpretations: Evidence from intra‐day FTSE‐100 index and futures data

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  • David G. McMillan
  • Alan E. H. Speight

Abstract

Extant empirical research has reported nonlinear behavior within arbitrage relationships. In this article, the authors consider potential nonlinear dynamics within FTSE‐100 index and index‐futures. Such nonlinearity can be rationalized by the existence of transactions costs or through the interaction between informed and noise traders. They consider several empirical models designed to capture these alternative dynamics. Their empirical results provide evidence of a stationary basis term, and thus cointegration between index and index‐futures, and the presence of nonlinear dynamics within that relationship. The results further suggest that noise traders typically engage in momentum trading and are more prone to this behavior type when the underlying market is rising. Fundamental, or arbitrage, traders are characterized by heterogeneity, such that there is slow movement between regimes of behavior. In particular, fundamental traders act more quickly in response to small deviations from equilibrium, but are reluctant to act quickly in response to larger mispricings that are exposed to greater noise trader price risk. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:343–368, 2006

Suggested Citation

  • David G. McMillan & Alan E. H. Speight, 2006. "Nonlinear dynamics and competing behavioral interpretations: Evidence from intra‐day FTSE‐100 index and futures data," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(4), pages 343-368, April.
  • Handle: RePEc:wly:jfutmk:v:26:y:2006:i:4:p:343-368
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    Cited by:

    1. Baoqiang Zhan & Shu Zhang & Helen S. Du & Xiaoguang Yang, 2022. "Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 861-882, October.
    2. Jieye Qin & Christopher J. Green & Kavita Sirichand, 2019. "Determinants of Nikkei futures mispricing in international markets: Dividend clustering, currency risk, and transaction costs," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(10), pages 1269-1300, October.
    3. Dahmene, Meriam & Boughrara, Adel & Slim, Skander, 2021. "Nonlinearity in stock returns: Do risk aversion, investor sentiment and, monetary policy shocks matter?," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 676-699.
    4. Xu, Feng & Wan, Difang, 2015. "The impacts of institutional and individual investors on the price discovery in stock index futures market: Evidence from China," Finance Research Letters, Elsevier, vol. 15(C), pages 221-231.
    5. Andreas Röthig, 2009. "Microeconomic Risk Management and Macroeconomic Stability," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-01565-6, October.
    6. McMillan, David G. & Philip, Dennis, 2012. "Short-sale constraints and efficiency of the spot–futures dynamics," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 129-136.
    7. Chen, Xiangyu & Tongurai, Jittima, 2021. "Cross-commodity hedging for illiquid futures: Evidence from China's base metal futures market," Global Finance Journal, Elsevier, vol. 49(C).

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