Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification

Journal of Econometrics

Volumn 135, Issue 1-2, 2006, Pages 125-154

  Chen, Xiaohong ^a   Fan, Yanqin ^b  

^a NEW YORK UNIVERSITY   (United States)

^b VANDERBILT UNIVERSITY   (United States)

Author keywords

Misspecified copulas; Mixture copulas; Multiple model selection; Multivariate dynamic models; Semiparametric inference

Indexed keywords

COMPUTER SIMULATION; MATHEMATICAL MODELS; PARAMETER ESTIMATION; TIME SERIES ANALYSIS;

MISSPECIFIED COPULAS; MIXTURE COPULAS; MULTIPLE MODEL SELECTION; MULTIVARIATE DYNAMIC MODELS; SEMIPARAMETRIC INFERENCE;

MULTIVARIABLE SYSTEMS;

EID: 33748595542     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jeconom.2005.07.027     Document Type: Article

References (40)

1. Breymann W., Dias A., and Embrechts P. Dependence structures for multivariate high-frequency data in finance. Quantitative Finance 3 1 (2003) 1-16
2. Chen, X., Fan, Y., 2004. Asymptotic properties of several estimators for semiparametric copula-based multivariate dynamic models. Manuscript, New York University and Vanderbilt University.
3. Chen, X., Fan, Y., 2005. Pseudo-likelihood ratio tests for model selection in semiparametric multivariate copula models. Canadian Journal of Statistics 33 (3), 389-414.
4. Chen, X., Fan, Y., Patton, A., 2003. Simple tests for models of dependence between multiple financial time series: with applications to US equity returns and exchange rates. Manuscript, New York University, Vanderbilt University and London School of Economics.
5. Clements M., and Hendry D. Forecasting Economic Time Series (1998), Cambridge University Press, Cambridge
6. Corradi, V., Swanson, N., 2003. A test for comparing multiple misspecified conditional distributions. Manuscript, Rutgers University.
7. Corradi, V., Swanson, N., 2004. Predictive density accuracy tests. Manuscript, Rutgers University.
8. Dias, A., Embrechts, P., 2004. Dynamic copula models for multivariate high-frequency data in finance. Manuscript, ETH Zurich.
9. Diebold F. Forecast combination and encompassing: reconciling two divergent literature. International Journal of Forecasting 5 (1989) 589-592
10. Diebold F., Hahn J., and Tay A. Multivariate density forecast evaluation and calibration in financial risk management: high frequency returns on foreign exchange. Review of Economics and Statistics 81 (1999) 661-673
11. Elliott, G., Timmermann, A., 2002. Optimal forecast combinations under general loss functions and forecast error distributions. Manuscript, UCSD.
12. Engle, R., 2002. Dynamic conditional correlation-a simple class of multivariate GARCH models. Journal of Business and Economic Statistics 20 (3), 339-350.
13. Engle, R., McFadden, D., 1994. Handbook of Econometrics, vol. 4. Elsevier, New York.
14. Engle, R., Sheppard, K., 2001. Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH. Working paper, UCSD and New York University.
15. Fermanian, J., 2003. Goodness of fit tests for copulas. Mimeo, CREST.
16. Genest C., Ghoudi K., and Rivest L. A semiparametric estimation procedure of dependence parameters in multivariate families of distributions. Biometrika 82 3 (1995) 543-552
17. Giacomini, R., White, H., 2004. Tests of conditional predictive ability. Manuscript, UCSD.
18. Granger, C.W.J., 2002. Time series concept for conditional distributions. Manuscript, UCSD.
19. Granger C.W.J., and Teräsvirta T. Modelling Nonlinear Economic Relationships (1993), Oxford University Press, Oxford
20. Granger, C.W.J., Teräsvirta, T., Patton, A., 2003. Common factors in conditional distributions for bivariate time series. Journal of Econometrics, in press, doi:10.1016/j.jeconom.2005.01.022.
21. Hamilton J. Time Series Analysis (1994), Princeton University Press, Princeton
22. Hansen, P., 2003. A test for superior predictive ability. Manuscript, Brown University.
23. Hendry D., and Richard J. On the formulation of empirical models in dynamic econometrics. Journal of Econometrics 20 (1982) 3-33
24. Hull J., and White A. Value at risk when daily changes in market variables are not normally distributed. Journal of Derivatives 5 (1998) 9-19
25. Joe H. Multivariate Models and Dependence Concepts (1997), Chapman & Hall/CRC, London
26. Junker, M., May, A., 2002. Measurement of Aggregate Risk With Copulas. Preprint, Caesar.
27. Machina, M., Granger, C., 2000. Forecast comparison without the use of cost functions. Working paper, UCSD.
28. Marcellino, M., 2002. Model selection for nonlinear dynamic models. Manuscript.
29. Mizon G., and Richard J. The encompassing principle and its application to testing non-nested hypotheses. Econometrica 54 (1986) 657-678
30. Nelsen R. An Introduction to Copulas (1999), Springer, Berlin
31. Patton, A., 2002. Modeling time-varying exchange rate dependence using the conditional copula. Working Paper 01-09, University of California, San Diego.
32. Patton A. On the out-of-sample importance of skewness and asymmetric dependence for asset allocation. Journal of Financial Econometrics 2 1 (2004) 130-168
33. Rivers, D., Vuong, Q., 2002. Model selection tests for nonlinear dynamic models. The Econometrics Journal 5 (1), 1-39.
34. Sin C., and White H. Information criteria for selecting possibly misspecified parametric models. Journal of Econometrics 71 (1996) 207-225
35. 2 and resampling methods. Working paper, UCSD.
36. Tsay R. Analysis of Financial Time Series (2002), Wiley Interscience, New York
37. Vuong Q. Likelihood ratio test for model selection and non-nested hypotheses. Econometrica 57 (1989) 307-333
38. West K. Encompassing tests when no model is encompassing. Journal of Econometrics 105 (2001) 287-308
39. White H. Estimation, Inference and Specification Analysis (1994), Cambridge University Press, Cambridge, UK
40. White H. A reality check for data snooping. Econometrica 68 (2000) 1097-1126