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Copula-based deviation measure of cointegrated financial assets

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  • Alexander Shulzhenko

Abstract

This study outlines a comprehensive methodology utilizing copulas to discern inconsistencies in the behavior exhibited by pairs of financial assets. It introduces a robust approach to establishing the interrelationship between the returns of these assets, exploring potential measures of dependence among the stochastic variables represented by these returns. Special emphasis is placed on scrutinizing the traditional measure of dependence, namely the correlation coefficient, delineating its limitations. Furthermore, the study articulates an alternative methodology that offers enhanced stability and informativeness in appraising the relationship between financial instrument returns.

Suggested Citation

  • Alexander Shulzhenko, 2023. "Copula-based deviation measure of cointegrated financial assets," Papers 2312.02081, arXiv.org.
    • Handle: RePEc:arx:papers:2312.02081
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    References listed on IDEAS

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    1. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    2. Evan Gatev & William N. Goetzmann & K. Geert Rouwenhorst, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule," The Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 797-827.
    3. Ibragimov, Rustam & Prokhorov, Artem, 2016. "Heavy tails and copulas: Limits of diversification revisited," Economics Letters, Elsevier, vol. 149(C), pages 102-107.
    4. Hossein Rad & Rand Kwong Yew Low & Robert Faff, 2016. "The profitability of pairs trading strategies: distance, cointegration and copula methods," Quantitative Finance, Taylor & Francis Journals, vol. 16(10), pages 1541-1558, October.
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