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Copula-based measures of dependence structure in assets returns

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  • Viviana Fernandez

Abstract

Copula modeling has become an increasingly popular tool in finance to model assets returns dependency. In essence, copulas enable us to extract the dependence structure from the joint distribution function of a set of random variables and, at the same time, to separate the dependence structure from the univariate marginal behavior. In this study, based on U.S. stock data, we illustrate how tail-dependency tests may be misleading as a tool to select a copula that closely mimics the dependency structure of the data. This problem becomes more severe when the data is scaled by conditional volatility and/or filtered out for serial correlation. The discussion is complemented, under more general settings, with Monte Carlo simulations.

Suggested Citation

  • Viviana Fernandez, 2006. "Copula-based measures of dependence structure in assets returns," Documentos de Trabajo 228, Centro de Economía Aplicada, Universidad de Chile.
    • Handle: RePEc:edj:ceauch:228
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    1. Sehgal, Sanjay & Pandey, Piyush & Diesting, Florent, 2017. "Examining dynamic currency linkages amongst South Asian economies: An empirical study," Research in International Business and Finance, Elsevier, vol. 42(C), pages 173-190.
    2. Lee, Sangwook & Kim, Min Jae & Kim, Soo Yong, 2011. "Interest rates factor model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2531-2548.
    3. Wang, Zong-Run & Chen, Xiao-Hong & Jin, Yan-Bo & Zhou, Yan-Ju, 2010. "Estimating risk of foreign exchange portfolio: Using VaR and CVaR based on GARCH–EVT-Copula model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4918-4928.
    4. Sanjay Sehgal & Piyush Pandey & Florent Deisting, 2018. "Time varying integration amongst the South Asian equity markets: An empirical study," Cogent Economics & Finance, Taylor & Francis Journals, vol. 6(1), pages 1452328-145, January.
    5. Giulia Risca & Stefania Galimberti & Paola Rebora & Alessandro Cattoni & Maria Grazia Valsecchi & Giulia Capitoli, 2025. "Archimedean Copulas: A Useful Approach in Biomedical Data—A Review with an Application in Pediatrics," Stats, MDPI, vol. 8(3), pages 1-19, August.
    6. Barbedo, Claudio H.S. & de Melo, Eduardo F.L., 2012. "Joint dynamics of Brazilian interest rate yields and macro variables under a no-arbitrage restriction," Journal of Economics and Business, Elsevier, vol. 64(5), pages 364-376.
    7. Sanjay Sehgal & Piyush Pandey & Florent Deisting, 2018. "Stock Market Integration Dynamics and its Determinants in the East Asian Economic Community Region," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(2), pages 389-425, June.
    8. Harish Kamal & Samit Paul, 2024. "Liquidity‐adjusted value‐at‐risk using extreme value theory and copula approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1747-1769, September.
    9. Del Brio, Esther B. & Mora-Valencia, Andrés & Perote, Javier, 2014. "Semi-nonparametric VaR forecasts for hedge funds during the recent crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 330-343.
    10. Michael C. Munnix & Rudi Schafer, 2011. "A Copula Approach on the Dynamics of Statistical Dependencies in the US Stock Market," Papers 1102.1099, arXiv.org, revised Mar 2011.
    11. Mike So & Alex Tse, 2009. "Dynamic Modeling of Tail Risk: Applications to China, Hong Kong and Other Asian Markets," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 16(3), pages 183-210, September.
    12. Chen, Rongda & Li, Cong & Wang, Weijin & Wang, Ze, 2014. "Empirical analysis on future-cash arbitrage risk with portfolio VaR," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 210-216.
    13. Muteba Mwamba, John & Mokwena, Paula, 2013. "International diversification and dependence structure of equity portfolios during market crashes: the Archimedean copula approach," MPRA Paper 64384, University Library of Munich, Germany.
    14. Songjiao Chen & William Wilson & Ryan Larsen & Bruce Dahl, 2016. "Risk Management for Grain Processors and “Copulas”," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 64(2), pages 365-382, June.
    15. Münnix, Michael C. & Schäfer, Rudi, 2011. "A copula approach on the dynamics of statistical dependencies in the US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4251-4259.

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