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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:

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    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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