Estimation of copula-based semiparametric time series models

Journal of Econometrics

Volumn 130, Issue 2, 2006, Pages 307-335

  Chen, Xiaohong ^a   Fan, Yanqin ^b  

^a NEW YORK UNIVERSITY   (United States)

^b VANDERBILT UNIVERSITY   (United States)

Author keywords

Conditional moment; Conditional quantile; Copula; Nonlinear Markov models; Semiparametric estimation

Indexed keywords

MARKOV PROCESSES; MATHEMATICAL MODELS; NONLINEAR EQUATIONS; TIME SERIES ANALYSIS;

CONDITIONAL MOMENT; CONDITIONAL QUANTILE; COPULA; NONLINEAR MARKOV MODELS; SEMIPARAMETRIC ESTIMATIONS;

PARAMETER ESTIMATION;

EID: 31344466558     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jeconom.2005.03.004     Document Type: Article

References (54)

1. D. Andrews Heteroskedasticity and autocorrelation consistent covariance matrix estimation Econometrica 59 1991 817 858
2. D. Andrews Testing when a parameter is on the boundary of the maintained hypothesis Econometrica 69 2001 683 734
3. P. Bickel, C. Klaassen, Y. Ritov, and J. Wellner Efficient and Adaptive Estimation for Semiparametric Models 1993 The Johns Hopkins University Press
4. Bouyé, E., Salmon, M., 2002. Dynamic copula quantile regression and tail area dynamic dependence in forex markets. Manuscript, Financial Econometrics Research Center.
5. Bouyé, E., Gaussel, N., Salmon, M., 2002. Investigating dynamic dependence using copulae. Manuscript, Financial Econometrics Research Center.
6. R. Bradley Basic properties of strong mixing conditions E. Eberlein M.S. Taqqu Dependence in Probability and Statistics 1986 Birkhauser Boston 165 192
7. X. Chen, and Y. Fan Evaluating density forecasts via the copula approach Finance Research Letters 1 2004 74 84
8. Chen, X., Fan, Y., 2004b. Pseudo-likelihood ratio tests for model selection in semiparametric multivariate copula models. Canadian Journal of Statistics, forthcoming.
9. Chen, X., Hansen, L.P., Carrasco, M., 1998. Nonlinearity and temporal dependence. Working Paper, University of Chicago.
10. Chen, X., Fan, Y., Patton, A., 2003. Simple tests for models of dependence between multiple financial time series, with applications to U.S. equity returns and exchange rates. Manuscript, London School of Economics.
11. Cherubini, U., Luciano, E., 2002. Multivariate option pricing with copulas. Applied Mathematical Finance, forthcoming.
12. Costinot, A., Roncalli, T., Teiletche, J., 2000. Revisiting the dependence between financial markets with copulas. Working Paper, Crédit Lyonnais.
13. W. Darsow, B. Nguyen, and E. Olsen Copulas and Markov processes Illinois Journal of Mathematics 36 1992 600 642
14. Demarta, S., McNeil, A.J., 2004. The t copula and related copulas. Manuscript.
15. L. Devroye Non-Uniform Random Variate Generation 1986 Springer New York
16. P. Doukhan, P. Massart, and E. Rio Invariance principles for absolutely regular empirical processes Ann. Inst. Henri Poincare 31 1995 393 427
17. D. Down, S.P. Meyn, and R.L. Tweedie Exponential and uniform ergodicity of Markov processes The Annals of Probability 23 1995 1671 1691
18. D. Duffie, and J. Pan An overview of value at risk Journal of Derivatives 4 1997 7 49
19. P. Embrechts, A. McNeil, and D. Straumann Correlation and dependence properties in risk management: properties and pitfalls M. Dempster Risk Management Value at Risk and Beyond 2002 Cambridge University Press Cambridge 176 223
20. P. Embrechts, A. Hoing, and A. Juri Using copulae to bound the value-at-risk for functions of dependent risks Finance and Stochastics 7 2 2003 145 167
21. Engle, R., Manganelli, S., 2002. CAViaR: conditional autoregressive value at risk by regression quantiles. Journal of Business and Economic Statistics, forthcoming.
22. Fermanian, J.-D., 2003. Goodness of fit tests for copulas. Manuscript, CREST.
23. E.W. Frees, and E.A. Valdez Understanding relationships using copulas North American Actuarial Journal 2 1998 1 25
24. Frey, R., McNeil, A., 2001. Modeling dependent defaults. Working Paper, Department of Mathematics, ETHZ.
25. Gagliardini, P., Gourieroux, C., 2002. Duration time series models with proportional hazard. Manuscript, CREST and University of Toronto.
26. C. Genest, K. Ghoudi, and L. Rivest A semiparametric estimation procedure of dependence parameters in multivariate families of distributions Biometrika 82 3 1995 543 552
27. Gourieroux, C., Jasiak, J., 2002. Value at risk. Manuscript, University of Toronto.
28. Granger, C.W.J., Terasvirta, T., Patton, A., 2003, Common factors in conditional distributions. Journal of Econometrics, forthcoming.
29. J.J. Heckman, and B.E. Honoré The identifiability of the competing risks model Biometrika 76 2 1989 325 330
30. Hu, H., 1998. Large sample theory for pseudo-maximum likelihood estimates in semiparametric models. Ph.D. Thesis, University of Washington.
31. Hu, L., 2002. Dependence patterns across financial markets: methods and evidence. Manuscript, Yale University.
32. J. Hull, and A. White Value at risk when daily changes in market variables are not normally distributed Journal of Derivatives 5 1998 9 19
33. T. Hutchinson, and C. Lai Continuous Bivariate Distributions, Emphasizing Applications 1990 Rumsby Scientific Publishing Adelaide
34. Jarner, S., Robert, G., 2001. Polynomial convergence rates of Markov chains. Working Paper, Lancaster University.
35. H. Joe Multivariate Models and Dependence Concepts 1997 Chapman & Hall/CRC
36. M. Johnson Multivariate Statistical Simulation 1987 Wiley New York
37. R. Koenker, and G. Bassett Regression quantiles Econometrica 46 1978 33 50
38. L. Lee Some approaches to the correction of selectivity bias Review of Economic Studies 49 1982 355 372
39. L. Lee Generalized econometric models with selectivity Econometrica 51 1983 507 512
40. D. Li On default correlation: a copula function approach Journal of Fixed Income 9 2000 43 54
41. R. Nelsen An Introduction to Copulas 1999 Springer
42. W.K. Newey The asymptotic variance of semiparametric estimators Econometrica 62 1994 1349 1382
43. W. Newey, and K. West Hypothesis testing with efficient method of moments estimation International Economic Review 28 1987 777 787
44. Patton, A.J., 2002a. Modeling time-varying exchange rate dependence using the conditional copula. Working Paper 01-09, Department of Economics, University of California, San Diego.
45. Patton, A.J., 2002b. Estimation of copula models for time series of possibly different lengths. Working Paper 01-17, Department of Economics, University of California, San Diego.
46. Patton, A.J., 2004. On the importance of skewness and asymmetric dependence in stock returns for asset allocation. Journal of Financial Econometrics, forthcoming.
47. J. Powell Estimation of monotonic regression models under quantile restriction W. Barnett J. Powell G. Tauchen Non-parametric and Semi-parametric Methods in Econometrics and Statistics 1991 Cambridge University Press New York
48. E. Rio A maximal inequality and dependent Marcinkiewicz-Zygmund strong laws Annals of Probability 23 1995 918 937
49. P. Robinson Nonparametric estimators for time series Journal of Time Series Analysis 4 1983 185 207
50. Rockinger, M., Jondeau, E., 2002. Conditional dependency of financial series: an application of copulas. Manuscript, HEC School of Management, France.
51. Rosenberg, J., 1999. Semiparametric pricing of multivariate contingent claims. Working Paper, Department of Finance, Stern School of Business, New York University.
52. Q. Shao, and H. Yu Weak convergence for weighted empirical processes of dependent sequences The Annals of Probability 24 1996 2098 2127
53. J. Shih, and T. Louis Inferences on the association parameter in copula models for bivariate survival data Biometrics 51 1995 1384 1399
54. A. Sklar Fonctions de r'epartition 'a n dimensionset leurs marges Publ. Inst. Statis. Univ. Paris 8 1959 229 231