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Measuring Risk of Portfolio : GARCH-Copula Model

Author

Listed:
  • Aloui, Chaker

    (College of Business Administration)

  • Messaoud, Samia Ben

    (International Finance Grouup-Tunisia)

Abstract

In this paper, we use the copulas functions in financial application, namely to examine the assumption of asymmetric dependence and to calculate some measures of risk. The first step consists of deducing filtered residuals for each return series by an asymmetric Glosten-Jagannathan-Runkle Generalized Autoregressive conditional Hetero skedasticity (GJR-GARCH) model. For the second step, we use an estimation of a Generalized Pareto Distribution for the upper and lower tails to determine the empirical semiparametric marginal Cumulative Distribution Function. In our approach, we propose to use a portfolio consisting of increments from five countries. The GJR-GARCH copula is then applied to the data and used to reduce correlation between the simulated residuals of each series. The marginal distributions of filtered residuals are fitted with a semi-parametric Cumulative Distribution Function using the copulas’ functions and Generalized Pareto Distribution for tails. For each series, we compute Value-at-Risk and Conditional Value-at-Risk.

Suggested Citation

  • Aloui, Chaker & Messaoud, Samia Ben, 2015. "Measuring Risk of Portfolio : GARCH-Copula Model," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 30, pages 172-205.
    • Handle: RePEc:ris:integr:0656
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    Citations

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    Cited by:

    1. Nurul Hanis Aminuddin Jafry & Ruzanna Ab Razak & Noriszura Ismail*, 2018. "Time-Varying Copula Modelling Between Malaysia and Major Stock Markets," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 646-652:6.
    2. So, Mike K.P. & Chan, Thomas W.C. & Chu, Amanda M.Y., 2022. "Efficient estimation of high-dimensional dynamic covariance by risk factor mapping: Applications for financial risk management," Journal of Econometrics, Elsevier, vol. 227(1), pages 151-167.
    3. Caio Mário Mesquita & Cristiano Arbex Valle & Adriano César Machado Pereira, 2024. "Scenario Generation for Financial Data with a Machine Learning Approach Based on Realized Volatility and Copulas," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1879-1919, May.

    More about this item

    Keywords

    GJR-GARCH; Copula Model; Portfolio Risk; Value at Risk; Conditional Value at Risk;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • F15 - International Economics - - Trade - - - Economic Integration
    • F42 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - International Policy Coordination and Transmission

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