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Modeling the Dependency Structure of Stock Index Returns using a Copula Function Approach

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  • Necula, Ciprian

    () (DOFIN, Academy of Economic Studies, Bucharest; Center for Advanced Research in Finance and Banking (CARFIB); Centrul de Analiza si Prognoza Economico-Financiara (CAPEF))

Abstract

In the present study we assess the dependency structure between stock indexes by econometrically estimating the empirical copula function and the parameters of various parametric copula functions. The main finding is that the t-copula and the Gumbel-Clayton mixture copula are the most appropriate copula functions to capture the dependency structure of two financial return series. With the dependency structure given by the estimated copula functions we quantify the efficient portfolio frontier using as a risk measure CVaR (Conditional VaR) computed by Monte Carlo simulation. We find that in the case of using normal distributions for modeling individual returns the market risk is underestimated no mater what copula function is employed to capture the dependency structure.

Suggested Citation

  • Necula, Ciprian, 2010. "Modeling the Dependency Structure of Stock Index Returns using a Copula Function Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 93-106, September.
  • Handle: RePEc:rjr:romjef:v::y:2010:i:3:p:93-106
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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. Jean-David FERMANIAN & Olivier SCAILLET, 2003. "Nonparametric Estimation of Copulas for Time Series," FAME Research Paper Series rp57, International Center for Financial Asset Management and Engineering.
    3. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
    4. Necula, Ciprian, 2009. "Modeling Heavy-Tailed Stock Index Returns Using the Generalized Hyperbolic Distribution," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 6(2), pages 118-131, June.
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    Cited by:

    1. Silvo Dajcman, 2013. "Dependence between Croatian and European stock markets – A copula GARCH approach," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics, vol. 31(2), pages 209-232.
    2. Dajcman, Silvio & Festic, Mejra, 2012. "The Interdependence of the Stock Markets of Slovenia, The Czech Republic and Hungary with Some Developed European Stock Markets – The Effects of Joining the European Union and the Global Financial Cri," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 163-180, December.

    More about this item

    Keywords

    copula functions; copula mixtures; the efficient portfolio frontier; Conditional VAR; Monte Carlo simulation;

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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