Título Inglês:
Wavelet smoothed empirical copula estimators
Resumo:
O objetivo desse artigo é introduzir estimadores suavizados de cópulas via ondaletas para o caso de séries temporais. As propriedades dos estimadores são avaliadas por meio de simulações e seu desempenho comparado com outros estimadores. Aplicações a dados reais também são feitas.
Resumo Inglês:
We introduce copula estimators based on wavelet smoothing of empirical copulas for the case of time series data. We then study the properties of this estimator via simulations and compare its performance with other estimators. Applications to real data are also given.
Citação ABNT:
MORETTIN, P. A.; TOLOI, C. M. C.; CHIANN, C.; MIRANDA, J. C. S. Wavelet smoothed empirical copula estimators. Revista Brasileira de Finanças, v. 8, n. 3, art. 121, p. 263-281, 2010.
Citação APA:
Morettin, P. A., Toloi, C. M. C., Chiann, C., & Miranda, J. C. S. (2010). Wavelet smoothed empirical copula estimators. Revista Brasileira de Finanças, 8(3), 263-281.
Link Permanente:
https://www.spell.org.br/documentos/ver/4521/wavelet-smoothed-empirical-copula-estimators/i/pt-br
Referências:
Autin, F., Lepennec, E., & Tribouley, K. (2008). Thresholding methods to estimate the copula density. Available at www.cmi.univ-mrs.fr/autin.
Charpentier, A., Fermanian, J.-D., & Scaillet, O. (2006). The estimation of copulas: Theory and practice. In Copulas: From Theory to Application in Finance. Risk Book.
Chen, X. & Fan, Y. (2006). Estimation of copula-based semiparametric time series models. Journal of Econometrics, 130:307–335.
Daubechies, I. (1992). Ten lectures on wavelets. Philadelphia: SIAM.
Deheuvels, P. (1979). La fonction de dépendance empirique et ses propriétés – Un test non param´etrique díndepéndance. Academie Royale de Belgique-Bulletin de la Classe des Sciences-5e Serie, 65:274–292.
Deheuvels, P. (1981a). A Kolmogorov-Smirnov type test for independence and multivariate samples. Revue Roumaine de Mathematiques Pures et Appliqués, 26:213–226.
Deheuvels, P. (1981b). A non parametric test for independence. Publications de l’Institut de Statistique de l’Universite de Paris, 26:29–50.
Dias, A. & Embrechts, P. (2009). Testing for structural changes in exchange rates dependence beyond linear correlation. European Journal of Finance, 15:619– 637.
Dias, A. & Embrechts, P. (2010). Modeling exchange rate dependence at different time horizons. Journal of International Money and Finance. To appear.
Donoho, D. L., Johnstone, I., Kerkyacharian, G., & Picard, D. (1995). Wavelet shrinkage: Asymptopia? Journal of the Royal Statistical Society, 57:301–369.
Fermanian, J.-D., Radulovic, D., & Wegkamp, M. (2004). Weak convergence of empirical copula processes. Bernoulli, 10:847–860.
Fermanian, J.-D. & Scaillet, O. (2003). Nonparametric estimation of copulas for time series. Journal of Risk, 5:25–54.
Gençay, R., Selçuk, F., & Witcher, B. (2002). An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Academic Press, New York.
Genest, C., Masiello, E., & Tribouley, K. (2009). Estimating copula densities through wavelets. Insurance: Mathematics and Economics, 44:170–181.
Genest, C. & Rivest, L.-P. (1993). Statistical inference procedures for bivariate Archimedian copulas. Journal of the American Statistical Association, 88:1034–1043.
Ibragimov, R. (2005). Copula-based dependence characterizations and modelling for time series. Discussion paper N. 2094, Harvard Institute of Economic Research.
Joe, H. & Xu, J. J. (1996). The estimation method of inference functions for margins for multivariate models. TR 166, Dept. Statistics, Univ. British Columbia.
Kolev, N. W., Mendes, B. V. M., & Anjos, U. (2006). Copulas: A review and recent developments. Communications in Statistics – Stochastic Models, 22:617–660.
Meyer, Y. (1993). Wavelets: Algorithms and applications. Philadelphia: SIAM. Mikosch, T. (2006). Copulas: Tales and facts. Extremes, 9:3–20.
Mikosch, T. (2006). Copulas: Tales and facts. Extremes, 9:3–20.
Morettin, P. A., Toloi, C. M. C., Chiann, C., & de Miranda, J. C. S. (2008). Wavelet estimation of copulas for time series. Under revision, J. Time Series Econometrics.
Nelsen, R. (2006). An Introduction to Copulas. Springer, New York, second edition. Lecture Notes in Statistics.
Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International Economic Revue, 47:527–556.
Ramsey, J. B. (2002). Wavelets in economics and finance: Past and future. RR # 2002-02, V.V. Starr Center for Applied Economics, New York University.
Rockinger, M. & Jondeau, E. (2001). Conditional dependency of financial series: An application of copulas. Working paper, HEC-Schol of Management.
Schweizer, B. (1991). Thirty years of copulas. In Dall’Aglio, G., Kotz, S., & Salinetti, G., editors, Advances in Probability Distributions with Given Marginals. Dordrecht, Kluwer.
Shih, J. & Louis, T. (1995). Inferences on the association parameter in copula models for bivariate survival data. Biometrika, 51:1384–1399.
Sklar, A. (1959). Fonctions de répartition `a n dimensions et leurs marges. Publications de l´Institut de Statistique de l´Universite de Páris, 8:229–231.
Vidakovic, B. (1999). Statistical Modeling by Wavelets. Wiley, New York.