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Efficient estimation of copula-GARCH models

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  • Liu, Yan
  • Luger, Richard

Abstract

An iterative (fixed-point) algorithm for the maximum-likelihood estimation of copula-based models that circumvents the need to compute second-order derivatives of the full likelihood function is adapted and examined. The algorithm exploits the structure of copula-based models that yield a natural decomposition of a potentially complicated likelihood function into two parts. The first part is a working likelihood that only involves the parameters of the marginals and the residual part is used to update estimates from the first part. A modified algorithm based on a working likelihood that accounts for some degree of correlation between the marginals is proposed. Compared to the original algorithm based on the working likelihood with the independent correlation, the modified one provides a better approximation to the full likelihood and overcomes convergence difficulties. A numerical example illustrates the efficiency gains of the estimation algorithms in the context of a benchmark copula-GARCH model. The modified algorithm is illustrated by an application to daily returns on two major stock market indices.

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Bibliographic Info

Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 53 (2009)
Issue (Month): 6 (April)
Pages: 2284-2297

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Handle: RePEc:eee:csdana:v:53:y:2009:i:6:p:2284-2297

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  1. H. D. Vinod & B. D. McCullough, 1999. "The Numerical Reliability of Econometric Software," Journal of Economic Literature, American Economic Association, vol. 37(2), pages 633-665, June.
  2. Peter Xue-Kun Song, 2000. "Multivariate Dispersion Models Generated From Gaussian Copula," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(2), pages 305-320.
  3. Ma Jun & Nelson Charles R & Startz Richard, 2007. "Spurious Inference in the GARCH (1,1) Model When It Is Weakly Identified," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(1), pages 1-27, March.
  4. Sébastien Laurent, 2004. "Analytical Derivates of the APARCH Model," Computational Economics, Society for Computational Economics, vol. 24(1), pages 51-57, 08.
  5. Nelson, Charles R. & Startz, Richard, 2007. "The zero-information-limit condition and spurious inference in weakly identified models," Journal of Econometrics, Elsevier, vol. 138(1), pages 47-62, May.
  6. H. D. Vinod & B. D. McCullough, 1999. "Corrigenda: The Numerical Reliability of Econometric Software," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1565-1565, December.
  7. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
  8. Jondeau, Eric & Rockinger, Michael, 2006. "The Copula-GARCH model of conditional dependencies: An international stock market application," Journal of International Money and Finance, Elsevier, vol. 25(5), pages 827-853, August.
  9. Fiorentini,G. & Calzolari,G. & Panattoni,L., 1995. "Analytic Derivatives and the Computation of Garch Estimates," Papers 9519, Centro de Estudios Monetarios Y Financieros-.
  10. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173.
  11. Bartram, Sohnke M. & Taylor, Stephen J. & Wang, Yaw-Huei, 2007. "The Euro and European financial market dependence," Journal of Banking & Finance, Elsevier, vol. 31(5), pages 1461-1481, May.
  12. Ling Hu, 2006. "Dependence patterns across financial markets: a mixed copula approach," Applied Financial Economics, Taylor & Francis Journals, vol. 16(10), pages 717-729.
  13. Song, Peter X.K. & Fan, Yanqin & Kalbfleisch, John D., 2005. "Maximization by Parts in Likelihood Inference," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1145-1158, December.
  14. Roch, Oriol & Alegre, Antonio, 2006. "Testing the bivariate distribution of daily equity returns using copulas. An application to the Spanish stock market," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1312-1329, November.
  15. Michael R. King & Dan Segal, 2004. "International Cross-Listing and the Bonding Hypothesis," Working Papers 04-17, Bank of Canada.
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Cited by:
  1. Ausin, M. Concepcion & Lopes, Hedibert F., 2010. "Time-varying joint distribution through copulas," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2383-2399, November.
  2. Nikoloulopoulos, Aristidis K. & Joe, Harry & Li, Haijun, 2012. "Vine copulas with asymmetric tail dependence and applications to financial return data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3659-3673.
  3. Bodnar, Taras & Hautsch, Nikolaus, 2013. "Copula-based dynamic conditional correlation multiplicative error processes," CFS Working Paper Series 2013/19, Center for Financial Studies (CFS).
  4. Silva Filho, Osvaldo Candido da & Ziegelmann, Flavio Augusto & Dueker, Michael J., 2012. "Modeling dependence dynamics through copulas with regime switching," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 346-356.
  5. Almeida, Carlos & Czado, Claudia, 2012. "Efficient Bayesian inference for stochastic time-varying copula models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1511-1527.
  6. Gregor Weiß, 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.
  7. Brechmann, Eike C. & Hendrich, Katharina & Czado, Claudia, 2013. "Conditional copula simulation for systemic risk stress testing," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 722-732.
  8. Alp, Tansel & Demetrescu, Matei, 2010. "Joint forecasts of Dow Jones stocks under general multivariate loss function," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2360-2371, November.

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