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Statistical arbitrage with vine copulas

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  • Johannes Stübinger
  • Benedikt Mangold
  • Christopher Krauss

Abstract

We develop a multivariate statistical arbitrage strategy based on vine copulas—a highly flexible instrument for linear and nonlinear multivariate dependence modeling. In an empirical application on the S&P 500, we find statistically and economically significant returns of 9.25% p.a. and a Sharpe ratio of 1.12 after transaction costs for the period from 1992 until 2015. Tail risk is limited, with maximum drawdown at 6.57%. The high returns can only partially be explained by common sources of systematic risk. We benchmark the vine copula strategy against other variants relying on the multivariate Gaussian and t-distribution and we find its results to be superior in terms of risk and return characteristics. The multivariate dependence structure of the vine copulas is time-varying, and we see that the share of copulas capable of modelling upper and lower tail dependences increases well over 90% at times of high market turmoil.

Suggested Citation

  • Johannes Stübinger & Benedikt Mangold & Christopher Krauss, 2018. "Statistical arbitrage with vine copulas," Quantitative Finance, Taylor & Francis Journals, vol. 18(11), pages 1831-1849, November.
    • Handle: RePEc:taf:quantf:v:18:y:2018:i:11:p:1831-1849
    DOI: 10.1080/14697688.2018.1438642
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    Citations

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    Cited by:

    1. Mohd Sabri Ismail & Nurulkamal Masseran & Mohd Almie Alias & Sakhinah Abu Bakar, 2024. "Modeling Asymmetric Dependence Structure of Air Pollution Characteristics: A Vine Copula Approach," Mathematics, MDPI, vol. 12(4), pages 1-23, February.
    2. Muhammad Asif Khan & Masood Ahmed & József Popp & Judit Oláh, 2020. "US Policy Uncertainty and Stock Market Nexus Revisited through Dynamic ARDL Simulation and Threshold Modelling," Mathematics, MDPI, vol. 8(11), pages 1-20, November.
    3. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    4. Trent Spears & Stefan Zohren & Stephen Roberts, 2023. "On statistical arbitrage under a conditional factor model of equity returns," Papers 2309.02205, arXiv.org.
    5. Erdinc Akyildirim & Ahmet Goncu & Alper Hekimoglu & Duc Khuong Nguyen & Ahmet Sensoy, 2023. "Statistical arbitrage: factor investing approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(4), pages 1295-1331, December.
    6. Kasper Johansson & Thomas Schmelzer & Stephen Boyd, 2024. "Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization," Papers 2402.08108, arXiv.org.
    7. Johannes Stübinger & Lucas Schneider, 2019. "Statistical Arbitrage with Mean-Reverting Overnight Price Gaps on High-Frequency Data of the S&P 500," JRFM, MDPI, vol. 12(2), pages 1-19, April.
    8. Garg, Jyoti & Karmakar, Madhusudan & Paul, Samit, 2023. "A study on equity home bias using vine copula approach," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    9. Endres, Sylvia & Stübinger, Johannes, 2018. "A flexible regime switching model with pairs trading application to the S&P 500 high-frequency stock returns," FAU Discussion Papers in Economics 07/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    10. Lucas Schneider & Johannes Stübinger, 2020. "Dispersion Trading Based on the Explanatory Power of S&P 500 Stock Returns," Mathematics, MDPI, vol. 8(9), pages 1-22, September.

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