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Mean reversion in stock index futures markets: A nonlinear analysis

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  • Michael Monoyios
  • Lucio Sarno

Abstract

Several stylized theoretical models of futures basis behavior under nonzero transactions costs predict nonlinear mean reversion of the futures basis towards its equilibrium value. Nonlinearly mean‐reverting models are employed to characterize the basis of the S&P 500 and the FTSE 100 indices over the post‐1987 crash period, capturing empirically these theoretical predictions and examining the view that the degree of mean reversion in the basis is a function of the size of the deviation from equilibrium. The estimated half lives of basis shocks, obtained using Monte Carlo integration methods, suggest that for smaller shocks to the basis level the basis displays substantial persistence, while for larger shocks the basis exhibits highly nonlinear mean reversion towards its equilibrium value. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:285–314, 2002

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  • Michael Monoyios & Lucio Sarno, 2002. "Mean reversion in stock index futures markets: A nonlinear analysis," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 22(4), pages 285-314, April.
    • Handle: RePEc:wly:jfutmk:v:22:y:2002:i:4:p:285-314
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    Cited by:

    1. Hilliard, Jitka, 2014. "Premiums and discounts in ETFs: An analysis of the arbitrage mechanism in domestic and international funds," Global Finance Journal, Elsevier, vol. 25(2), pages 90-107.
    2. Tim Leung & Jiao Li & Xin Li & Zheng Wang, 2016. "Speculative Futures Trading under Mean Reversion," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 23(4), pages 281-304, December.
    3. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, November.
    4. Antoine Lejay & Paolo Pigato, 2020. "Maximum likelihood drift estimation for a threshold diffusion," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 609-637, September.
    5. Antoine Lejay & Paolo Pigato, 2017. "A threshold model for local volatility: evidence of leverage and mean reversion effects on historical data," Working Papers hal-01669082, HAL.
    6. Yang, Jian & Cabrera, Juan & Wang, Tao, 2010. "Nonlinearity, data-snooping, and stock index ETF return predictability," European Journal of Operational Research, Elsevier, vol. 200(2), pages 498-507, January.
    7. Ivan Paya & David A. Peel, 2011. "Systematic sampling of nonlinear models: Evidence on speed of adjustment in index futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 31(2), pages 192-203, February.
    8. Philip, Dennis & Shi, Yukun, 2015. "Impact of allowance submissions in European carbon emission markets," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 27-37.
    9. Diego Bastourre, 2008. "Inversores Financieros en los Mercados de Commodities: Un Modelo con Dinámica de Ajuste no Lineal al Equilibrio," IIE, Working Papers 072, IIE, Universidad Nacional de La Plata.
    10. Andreas Röthig, 2009. "Microeconomic Risk Management and Macroeconomic Stability," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-01565-6, December.
    11. Antoine Lejay & Paolo Pigato, 2019. "A Threshold Model For Local Volatility: Evidence Of Leverage And Mean Reversion Effects On Historical Data," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-24, June.
    12. Buckley, Winston S. & Long, Hongwei, 2015. "A discontinuous mispricing model under asymmetric information," European Journal of Operational Research, Elsevier, vol. 243(3), pages 944-955.

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