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The Lead–Lag Relation Between The S&P500 Spot And Futures Markets: An Intraday‐Data Analysis Using A Threshold Regression Model

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  • YIU‐KUEN TSE
  • WAI‐SUM CHAN

Abstract

In this paper we examine the lead–lag interaction between the futures and spot markets of the S&P500 using the threshold regression model on intraday data. The use of threshold variables to model the changes in the regression structure with respect to different market conditions enables us to investigate the lead–lag interaction in a data‐based approach and avoid stratifying the data arbitrarily. Using the basis as the threshold variable, we find that the short‐selling restrictions in the spot market reduce the effect of the spot index as the leading variable. To study the effect of market‐wide information on the interaction between the spot and futures markets, we use the coefficient of determination in the regression of the S&P500 on the Morgan–Stanley Composite Index‐US and the Major Market Index as the threshold variable. We find that the lead effect of the futures market over the spot market is stronger when there is more market‐wide information. On the other hand, the lead effect of the cash market over the futures market is weaker when there is more market‐wide information. In addition, we also use the lagged 45‐min return of the spot market as the threshold variable. We find that the lead effect of the spot market is stronger in periods of directionless trading than in periods of good or bad markets.

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  • Yiu‐Kuen Tse & Wai‐Sum Chan, 2010. "The Lead–Lag Relation Between The S&P500 Spot And Futures Markets: An Intraday‐Data Analysis Using A Threshold Regression Model," The Japanese Economic Review, Japanese Economic Association, vol. 61(1), pages 133-144, March.
    • Handle: RePEc:bla:jecrev:v:61:y:2010:i:1:p:133-144
    DOI: 10.1111/j.1468-5876.2009.00481.x
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    3. Dejan Živkov & Slavica Manić & Ivan Pavkov, 2022. "Nonlinear examination of the ‘Heat Wave’ and ‘Meteor Shower’ effects between spot and futures markets of the precious metals," Empirical Economics, Springer, vol. 63(2), pages 1109-1134, August.
    4. Ren, Fei & Ji, Shen-Dan & Cai, Mei-Ling & Li, Sai-Ping & Jiang, Xiong-Fei, 2019. "Dynamic lead–lag relationship between stock indices and their derivatives: A comparative study between Chinese mainland, Hong Kong and US stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 709-723.
    5. Mandeep Kaur & Kapil Gupta, 2019. "Estimating Hedging Effectiveness Using Variance Reduction And Risk-Return Approaches: Evidence From National Stock Exchange Of India," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 8(4), pages 149-169.
    6. Gong, Chen-Chen & Ji, Shen-Dan & Su, Li-Ling & Li, Sai-Ping & Ren, Fei, 2016. "The lead–lag relationship between stock index and stock index futures: A thermal optimal path method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 63-72.

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