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On the predictability of model-free implied correlation

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  • Markopoulou, Chryssa
  • Skintzi, Vasiliki
  • Refenes, Apostolos

Abstract

This paper investigates the existence of predictable patterns in the evolution of the implied correlation series. To this end, alternative time-series specifications are employed to model the correlation dynamics, and the statistical and economic significance of out-of sample forecasts is assessed. The statistical measures provide strong evidence in favor of predictable patterns in the S&P 100 options market. A trading strategy designed to exploit daily changes in the series can yield abnormal profits; however, these profits disappear when transaction costs are incorporated. We conclude that the efficient market hypothesis cannot be rejected.

Suggested Citation

  • Markopoulou, Chryssa & Skintzi, Vasiliki & Refenes, Apostolos, 2016. "On the predictability of model-free implied correlation," International Journal of Forecasting, Elsevier, vol. 32(2), pages 527-547.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:2:p:527-547
    DOI: 10.1016/j.ijforecast.2015.09.008
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    Cited by:

    1. Schadner, Wolfgang, 2021. "Ex-Ante Risk Factors and Required Structures of the Implied Correlation Matrix," Finance Research Letters, Elsevier, vol. 41(C).
    2. Wolfgang Schadner, 2021. "Feasible Implied Correlation Matrices from Factor Structures," Papers 2107.00427, arXiv.org.
    3. Escobar, Marcos & Fang, Lin, 2020. "Stochastic volatility models for the implied correlation index," Finance Research Letters, Elsevier, vol. 35(C).
    4. Wolfgang Schadner & Joshua Traut, 2022. "Estimating Forward-Looking Stock Correlations from Risk Factors," Mathematics, MDPI, vol. 10(10), pages 1-19, May.

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