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Dynamic Copula Modelling for Value at Risk

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  • Dean Fantazzini

    ()
    (Moscow State University)

Abstract

This paper proposes dynamic copula and marginals functions to model the joint distribution of risk factor returns affecting portfolios profit and loss distribution over a specified holding period. By using copulas, we can separate the marginal distributions from the dependence structure and estimate portfolio Value-at-Risk, assuming for the risk factors a multivariate distribution that can be different from the conditional normal one. Moreover, we consider marginal functions able to model higher moments than the second, as in the normal. This enables us to better understand why VaR estimates are too aggressive or too conservative. We apply this methodology to estimate the 95%, 99% VaR by using Monte-Carlo simulation, for portfolios made of the SP500 stock index, the Dax Index and the Nikkei225 Index. We use the initial part of the sample to estimate the models, and the the remaining part to compare the out-of-sample performances of the different approaches, using various back-testing techniques.

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Bibliographic Info

Article provided by SKEMA Business School in its journal Frontiers in Finance and Economics.

Volume (Year): 5 (2008)
Issue (Month): 2 (October)
Pages: 72-108

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Handle: RePEc:ffe:journl:v:5:y:2008:i:2:p:72-108

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Web page: http://www.ffe.esc-lille.com

Related research

Keywords: Copulae; Value at Risk; Dynamic Modelling;

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Cited by:
  1. Penikas, Henry & Simakova, Varvara, 2009. "Interest Rate Risk Management Based on Copula-GARCH Models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 13(1), pages 3-36.
  2. Bologov , Yaroslav, 2013. "A copula-based approach to portfolio credit risk modeling," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 29(1), pages 45-66.
  3. Fantazzini, Dean, 2008. "Econometric Analysis of Financial Data in Risk Management (continuation). Section III: Managing Operational Risk," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 11(3), pages 87-122.
  4. Gregor WeiƟ, 2013. "Copula-GARCH versus dynamic conditional correlation: an empirical study on VaR and ES forecasting accuracy," Review of Quantitative Finance and Accounting, Springer, vol. 41(2), pages 179-202, August.
  5. Xisong Jin & Francisco Nadal De Simone, 2013. "Banking Systemic Vulnerabilities: A Tail-risk Dynamic CIMDO Approach," BCL working papers 82, Central Bank of Luxembourg.
  6. Xisong Jin & Francisco Nadal De Simone, 2012. "An Early-warning and Dynamic Forecasting Framework of Default Probabilities for the Macroprudential Policy Indicators Arsenal," BCL working papers 75, Central Bank of Luxembourg.
  7. Fantazzini, Dean, 2008. "An Econometric Analysis of Financial Data in Risk Management," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 10(2), pages 91-137.

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