Ozun, Alper and Cifter, Atilla (2007): Portfolio Value-at-Risk with Time-Varying Copula: Evidence from the Americas.
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Model risk in the estimation of value-at-risk is a challenging threat for the success of any financial investments. The degree of the model risk increases when the estimation process is constructed with a portfolio in the emerging markets. The proper model should both provide flexible joint distributions by splitting the marginality from the dependencies among the financial assets within the portfolio and also capture the non-linear behaviours and extremes in the returns arising from the special features of the emerging markets. In this paper, we use time-varying copula to estimate the value-at-risk of the portfolio comprised of the Bovespa and the IPC Mexico in equal and constant weights. The performance comparison of the copula model to the EWMA portfolio model made by the Christoffersen back-test shows that the copula model captures the extremes most successfully. The copula model, by estimating the portfolio value-at-risk with the least violation number in the back-tests, provides the investors to allocate the minimum regulatory capital requirement in accordance with the Basel II Accord.
|Item Type:||MPRA Paper|
|Original Title:||Portfolio Value-at-Risk with Time-Varying Copula: Evidence from the Americas|
|Keywords:||Time-varying Copula; portfolio value-at-risk; Latin American equity markets; portfolio GARCH|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
G - Financial Economics > G1 - General Financial Markets
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
|Depositing User:||Atilla Cifter|
|Date Deposited:||13. Apr 2007|
|Last Modified:||24. Aug 2015 01:05|
ANG, A. and CHEN, J. 2002. “Asymmetric Correlations of Equity Portfolios,” Journal of Financial Economics, 63, 443-494
BAUWENS, L., LAURENT, S., and ROMBOUTS, J.V.K. 2006. “Multivariate Garch Models,” Journal of Applied Econometrics, 21, 79 - 109.
CAMPBELL, R. A., KOEDIJK, C. G., AND KOFMAN, P. 2002. “Increased correlation in bear markets,” Financial Analysts Journal, 58(1), 87-94.
CHEN X. and FAN, Y. 2002. “Estimation of Copula-Based Semiparametric Time Series Models,” Working Papers, 0226, Department of Economics, Vanderbilt University
CHEN, X. and FAN, Y. 2006. “Estimation and Model Selection of Semiparametric Copula-Based Multivariate Dynamic Models Under Copula Misspecification,” Journal of Econometrics, 135(1-2), 125-154.
CHERUBINI , U., LUCIANO, E. and VECCHIATO, W. 2004. Copula Methods in Finance, John Wiley, NY.
CHRISTOFFERSEN, P.F. 1998. “Evaluating Interval Forecasts,” International Economic Review, 39, 841-862.
DOWD, K. 2004. “FOMC Forecasts of Macroeconomic Risks,” Occasional Papers 12, Industrial Economics Division.
EMBRECHTS, P., MCNEIL, A. and STRAUMANN, D. 2002. “Correlation and dependence in risk management: properties and pitfalls,” In Risk Management Value at Risk and Beyond (Edited bu M. Dempster), Cambridge University Press, 176-223
EMBRECHTS, P., HOING, and JURI, A. 2003. “Using copulae to bound the value-at-risk for functions of dependent risks,” Finance and Stochastics, 7, 145-167.
EMBRECHTS, P. HOING, A. and PUCHETTI, G. 2005. “Worst VaR scenarios,” Insurance: Mathematics and Economics , 37(1), 115-134.
FERMANIAN S. and SCAILLET, R. 2003. “Nonparametric Tests for Positive Quadrant Dependence,” Journal of Fınancıal Econometrıcs, 2, 422-450
FORTIN, I. and KUZMICS, C. 2002. “Tail dependence in stock return pairs,” International Journal of Intelligent Systems in Accounting, Finance & Management, 11, 89-107.
FREY, R. and MCNEIL, A. 2003. “Dependent Defaults in Models of Portfolio Credit Risk,” Journal of Risk, 6/1, 59-92.
GIESECKE, K and GOLDBERG, L. 2004. “Fitness tests for multi-firm default models”, Working Paper, Cornell University
GOORBERGH, R., GENEST, W. J., C. and WERKER, B. J. M. 2005. “Bivariate option pricing using dynamic copula models,” Insurance, Mathematics and Economics, 37, 101-114.
HAMERLE, A. and ROSCH, D. 2005. “Misspecified Copulas in Credit Risk Models: How Good is Gaussian?,” Journal of Risk, 8(1), 35-47.
JONDEA, U., E. and ROCKINGER, M. 2006. “The Copula-GARCH model of conditional dependencies: An international stock market application,” Journal of International Money and Finance 25(5), 827-853.
JUNKER, M., SZIMAYER, A. and WAGNER, N. 2006. “Nonlinear Term Structure Dependence: Copula Functions, Empirics, and Risk Implications,” Journal of Banking and Finance, 30, 1171-1199.
JURI, A and WUTHRICHTS, M.V. 2002. “Copula convergence theorems for tail events,” Insurance: Mathematics and Economics 30/3, 405-420.
LEE, T.-H. and LONG, X. 2005. “Copula-based Multivariate GARCH Model with Uncorrelated Dependent Errors,” UCR Working Paper 2005-16. http://economics.ucr.edu/papers/
LI, D.X., 2000. “On default correlation: A copula approach,” Journal of Fixed Income, 9, 43-54.
LONGIN, F. and SOLNIK, B. 2001. “Extreme correlation of international equity markets,” Journal of Finance, 56, 649-676
LORETAN, M., and ENGLISH, W.B. 2000, “Evaluating Correlation Breakdowns during Periods of Market Volatility”, International Finance Discussion Papers, No. 658, Board of Governors of the Federal Reserve System
MENDES, B.V.M., and SOUZA, R.M. 2004. “Measuring Financial Risks wit Copulas,” International Review of Financial Analysis, 13, 27-45.
MENEGUZZO D. and VECCHIATO W. 2004. “Copula Sensitivity in Collateralized Debt Obligations and Basket Default Swaps,” The Journal of Future Markets, 1, 37-70.
NESLEHOVA, J., EMBRECHTS, P. and DEMOULIN C. 2006. “Infinite-mean Models and the LDA for Operational Risk,” Journal of Operational Risk 1/1, 3-25.
PALARO H., and HOTTA, L K. 2006. “Using Conditional Copulas to Estimate Value at Risk,” Journal of Data Science 4(1), 93-115.
PATTON, A. J. 2001. “Applications of Copula Theory in Financial Econometrics,” Unpublished Ph.D. dissertation, University of California, San Diego.
PATTON, A. 2006a. “Modelling Asymetric Exchange Rate Dependence,” International Economic Review, 47(2), 527-556
PATTON, A. 2006b. “Estimation of Multivariate Models for Time Series of Possibly Different Lenghts,” Journal of Applied Econometrics, 21(2), 147-173
POON, S.-H., ROCKİNGER, M., and TAWN, J. 2004. “Extreme value dependence in Financial markets: Diagnostics, models and ¯nancial implications,” The Review of Financial Studies, 17(2), 586-610
ROCKINGER, M. and JONDEAU, E, 2001. “Conditional dependency of financial series : an application of copulas,” Les Cahiers de Recherche, 723, Groupe HEC.
ROCKINGER, M. and JONDEAU, E. 2006 “The Coplua-GARCH model of conditional dependencies: An international stock market application,” Journal of International Money and Finance, 25(3), 827-853.
ROSENBERG, J.Y. 2003. “Nonparametric pricing of multivariate contingent claims,” Staff Reports, 162, Federal Reserve Bank of New York
SALTOGLU, B. 2003. A High Frequency Analysis of Financial Risk and Crisis: An Empirical Study on Turkish Financial Market, Yaylım Publishing, Istanbul.
SHEPPARD, K. 2006. UCSD Garch Toolbox, http://www.kevinsheppard.com [15.02.2007]
SKLAR, A. 1959. “Fonctions de répartition à n dimensions et leurs marges,” Publications de l’ Institut Statistique de l.Universite´ de Paris, 8, 229-231