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

Author

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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

    as
    1. Fermanian, Jean-David & Scaillet, Olivier, 2003. "Nonparametric estimation of copulas for time series," Working Papers unige:41797, University of Geneva, Geneva School of Economics and Management.
    2. 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.
    3. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    4. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
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    Cited by:

    1. See-Woo Kim & Yong-Ki Ma & Ciprian Necula, 2023. "Modeling Tail Dependence Using Stochastic Volatility Model," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 129-147, June.
    2. 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 and Business, vol. 31(2), pages 209-232.
    3. 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.
    4. Mitica Pepi, 2022. "The Interdependence of the Stock Markets Developed in Central and Eastern- European Stock Markets - Represented by the Stock Indices," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 995-1000, Decembrie.

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    More about this item

    Keywords

    copula functions; copula mixtures; the efficient portfolio frontier; Conditional VAR; Monte Carlo simulation;
    All these keywords.

    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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