Copula-GARCH versus dynamic conditional correlation: an empirical study on VaR and ES forecasting accuracy

Aas K, Berg D (2009) Models for construction of multivariate dependence—a comparison study. Eur J Financ 15:639–659 Aas K, Czado C, Frigessi A, Bakken H (2009) Pair-copula constructions of multiple dependence. Insur Math Econ 44:182–198 Alexander C, Sheedy E (2008) Developing a stress testing framework based on market risk models. J Bank Financ 32:2220–2236 Ausin MC, Lopes HF (2010) Time-varying joint distribution through copulas. Comput Stat Data An 54:2383–2399 Bartram S, Taylor S, Wang Y-H (2007) The Euro and European financial market dependence. J Bank Financ 51:1461–1481 Berg D (2009) Copula goodness-of-fit testing: an overview and power comparison. Eur J Financ 15:675–701 Breymann W, Dias A, Embrechts P (2003) Dependence structures for multivariate high-frequency data in finance. Quant Financ 3:1–14 Cappiello L, Engle RF, Sheppard K (2006) Asymmetric dynamics in the correlations of global equity and bond returns. J Financ Economet 4:537–572 Chen X, Fan Y (2006) Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification. J Econom 135:125–154 Chen S, Poon S-H (2011) Modeling international financial markets contagion: using copula and risk appetite. In: Robert K (eds) Financial contagion: the viral threat to the wealth of nations, 1st edn. Wiley, New York, pp 45–56 Christoffersen P (1998) Evaluating interval forecasts. Int Econ Rev 39:841–862 Christoffersen P, Pelletier D (2004) Backtesting value-at-risk: a duration-based approach. J Financ Economet 2:84–108 Deheuvels P (1979) La fonction de dépendance empirique et ses propriétés—un test non paramétrique d’indépendance. Académie Royale de Belgique Bulletin de la Classe des Sciences 5e Série 65:274–292 Deheuvels P (1981) A nonparametric test for independence. Université de Paris, Institut de Statistique, Paris Di Clemente A, Romano C (2005) Measuring portfolio value-at-risk by a copula-EVT-based approach. Studi Economici 85:29–57 Diks C, Panchenko V, van Dijk D (2010) Out of sample comparison of copula specifications in multivariate density forecasts. J Econ Dyn Control 34:1596–1609 Embrechts P, McNeil A, Straumann D (2002) Correlation and dependence in risk management: properties and pitfalls. http://www.math.ethz.ch/~strauman/preprints/pitfalls.pdf. Accessed 24 April 2012 Engle RF (2002) Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Statist 20:339–350 Engle RF, Kelly B (2008) Dynamic equicorrelation. NYU Stern School of Business. http://pages.stern.nyu.edu/~rengle/Dynamic%20Equicorrelation.pdf. Accessed 24 April 2012 Fantazzini D (2008) Dynamic copula modelling for value at risk. Front Financ Econ 31:161–180 Fantazzini D (2009) The effects of misspecified marginals and copulas on computing the value at risk: a Monte Carlo study. Comput Stat Data An 53:2168–2188 Fantazzini D (2009) Market risk management for emerging markets: evidence from the Russian stock market. In: Gregoriou G (ed) Emerging markets: performance, analysis and innovation. Chapman and Hall/CRC Finance, London, pp 533–554 Fantazzini D (2009) Value at risk for high-dimensional portfolios: a dynamic grouped-T copula approach. In: Gregoriou G (ed) The var implementation handbook. McGraw-Hill, New York, pp 253–282 Fischer M, Köck C, Schlüter S, Weigert F (2009) An empirical analysis of multivariate copula models. Quant Financ 7:839–854 Genest C, Rémillard B, Beaudoin D (2009) Goodness-of-fit tests for copulas: a review and a power study. Insur Math Econ 44:199–213 Genest C, Rémillard B (2008) Validity of the parametric bootstrap for goodness-of-fit testing in semiparametric models. Ann de l’Inst Henri-Poincaré 44:1096–1127 Genest C, Quessy J-F, Rémillard B (2006) Goodness-of-fit procedures for copula models based on the probability integral transform. Scand J Stat 33:337–366 Genest C, Rivest L-P (1993) Statistical inference procedures for bivariate archimedean copulas. J Am Stat Assoc 88:1034–1043 Hafner CM, Reznikova O (2010) Efficient estimation of a semiparametric dynamic copula model. Comput Stat Data An 54:2609–2627 Hansen PR, Lunde A (2005) A forecast comparison of volatility models: does anything beat a GARCH(1,1)?. J Appl Econom 20:873–889 Hobæk Haff I, Aas K, Frigessi A (2010) On the simplified pair-copula construction—simply useful or too simplistic? J Multivar Anal 101:1296–1310 Hoeffding W (1940) Scale invariant correlation theory. Schriften Math Inst Univ Berlin 5:181–233 Hsu CP, Huang CW, Chiou WJP (2011) Effectiveness of copula-extreme value theory in estimating value-at-risk: empirical evidence from Asian emerging markets. Rev Quant Financ Acc. doi:10.1007/s11156-011-0261-0 Huang YC, Lin BJ (2004) Value-at-risk analysis for Taiwan stock index futures: fat tails and conditional asymmetries in return innovations. Rev Quant Financ Acc 22:79–95 Joe H (1997) Multivariate models and dependence concepts. Chapman & Hall/CRC, London Jondeau E, Rockinger M (2006) The Copula-GARCH model of conditional dependencies: an international stock market application. J Int Money Financ 25:827–853 Junker M, May A (2005) Measurement of aggregate risk with copulas. Economet J 8:428–454 Kim G, Silvapulle M, Silvapulle P (2007) Comparison of semiparametric and parametric methods for estimating copulas. Comput Stat Data An 51:2836–2850 Kole E, Koedijk KCG, Verbeek M (2007) Selecting copulas for risk management. J Bank Financ 31:405–2423 Li DX (2000) On default correlation: a copula function approach. J Fixed Income 9:43–54 Lin CH, Chien CC, Chen S (2006) Incorporating the time-varying tail-fatness into the historical simulation method for portfolio value-at-risk. Rev Pac Basin Financ Mark Policies 9:257–274 Liu Y, Luger R (2009) Efficient estimation of copula-GARCH models. Comput Stat Data An 53:2284–2297 Lu C, Tse Y, Williams M (2012) Returns transmission, value at risk, and diversification benefits in international REITs: evidence from the financial crisis. Rev Quant Finan Acc. doi:10.1007/s11156-012-0274-3 Malevergne Y, Sornette D (2003) Testing the gaussian copula hypothesis for financial assets dependencies. Quant Financ 3:231–250 Manner H, Reznikova O (2012) A survey on time-varying copulas: specification, simulations and estimation. Economet Rev 31:654–687 Markwat T, Kole E, van Dijk D (2009) Time variation in asset return dependence: strength or structure? Erasmus University Rotterdam. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1460648. Accessed 24 April 2012 Marsh T, Pfleiderer P (2012) “Black Swans” and the financial crisis. Rev Pac Basin Financ Mark Policies. doi:10.1142/s0219091512500087 McNeil A, Frey R, Embrechts P (2005) Quantitative risk management. Princeton University Press, Princeton Nelsen RB (2006) An introduction to copulas. Lect Notes Stat, 2nd edn, vol 139. Springer, New York Nikoloulopoulos AK, Joe H, Li H (2010) Vine copulas with asymmetric tail dependence and applications to financial return data. Comput Stat Data An. doi:10.1016/j.csda.2010.07.016 Palaro HP, Hotta LK (2006) Using conditional copula to estimate value at risk. J Data Sci 4:93–115 Patton A (2006) Modelling asymmetric exchange rate dependence. Int Economic Rev 47:527–556 Pritsker M (2006) The hidden dangers of historical simulation. J Bank Financ 30:561–582 Q (2011) Are copula-GoF-tests of any practical use? Empirical evidence for stocks, commodities and FX futures. Q Rev Econ Financ 51:173–188 Rosenblatt M (1952) Remarks on a multivariate transformation. Ann Math Stat 23:470–472 Savu C, Trede M (2008) Goodness-of-fit tests for parametric families of archimedean copulas. Quant Financ 8:109–116 Sklar A (1959) Fonctions de répartition à n dimensions et leurs marges. Publications de l’Institut de Statstique de l’Université de Paris 8:229–231 Vuong Q (1989) Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica 57:307–333 Wong WK (2008) Backtesting trading risk of commercial banks using expected shortfall. J Bank Financ 32:1404–1415