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Copula-based measures of dependence structure in assets returns

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  • Viviana Fernandez

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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.

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Bibliographic Info

Paper provided by Centro de Economía Aplicada, Universidad de Chile in its series Documentos de Trabajo with number 228.

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Date of creation: 2006
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Handle: RePEc:edj:ceauch:228

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  1. Panchenko, Valentyn, 2005. "Goodness-of-fit test for copulas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 176-182.
  2. Rosenberg, Joshua V. & Schuermann, Til, 2006. "A general approach to integrated risk management with skewed, fat-tailed risks," Journal of Financial Economics, Elsevier, vol. 79(3), pages 569-614, March.
  3. Ser-Huang Poon, 2004. "Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications," Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 581-610.
  4. Mendes, Beatriz V.M. & Leal, Ricardo P.C. & Carvalhal-da-Silva, Andre, 2007. "Clustering in emerging equity markets," Emerging Markets Review, Elsevier, vol. 8(3), pages 194-205, September.
  5. Bartram, Sohnke M. & Taylor, Stephen J. & Wang, Yaw-Huei, 2007. "The Euro and European financial market dependence," Journal of Banking & Finance, Elsevier, vol. 31(5), pages 1461-1481, May.
  6. Giesecke, Kay, 2004. "Correlated default with incomplete information," Journal of Banking & Finance, Elsevier, vol. 28(7), pages 1521-1545, July.
  7. U. Cherubini & E. Luciano, 2002. "Bivariate option pricing with copulas," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(2), pages 69-85.
  8. Markus Junker & Alexander Szimayer & Niklas Wagner, 2004. "Nonlinear Term Structure Dependence: Copula Functions, Empirics, and Risk Implications," Econometrics 0401007, EconWPA.
  9. Junker, Markus & Szimayer, Alex & Wagner, Niklas, 2006. "Nonlinear term structure dependence: Copula functions, empirics, and risk implications," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1171-1199, April.
  10. Umberto Cherubini & Elisa Luciano, 2003. "Pricing and Hedging Credit Derivatives with Copulas," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 32(2), pages 219-242, 07.
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Cited by:
  1. 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.
  2. 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.
  3. Mike So & Alex Tse, 2009. "Dynamic Modeling of Tail Risk: Applications to China, Hong Kong and Other Asian Markets," Asia-Pacific Financial Markets, Springer, vol. 16(3), pages 183-210, September.
  4. 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.
  5. 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.
  6. 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.
  7. Michael C. M\"unnix & Rudi Sch\"afer, 2011. "A Copula Approach on the Dynamics of Statistical Dependencies in the US Stock Market," Papers 1102.1099, arXiv.org, revised Mar 2011.
  8. 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.

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