Document Type : Research Paper
Author
Young Researchers and Elite Club, Maragheh branch, Islamic Azad University, Maragheh, Iran.
Abstract
In this study the main endeavor is to model dependence structure between crude oil prices of West Texas Intermediate (WTI) and Brent - Europe. The main activity is on concentrating copula technique which is powerful technique in modeling dependence structures. Beside several well known Archimedean copulas, three new Archimedean families are used which have recently presented to the literature. Moreover, convex combination of these copulas also are investigated on modeling of the mentioned dependence structure. Modeling process is relied on 318 data which are average of the monthly prices from Jun-1992 to Oct-2018.
Keywords
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[27] H. Tsukahara, Semiparametric estimation in copula models, Canad. J. Stat., 33 (2005), pp. 357-375.
[2] T. Bacigal and M. Komornikova, Fitting Archimedean copulas to bivariate geodetic data, COMPSTAT, (2006), pp. 649-656.
[3] H. Bal and V. Najjari, Archimedean copulas family via hyperbolic generator, GU J. Sci., 26 (2013), pp. 195-200.
[4] P. Bertail, P. Doukhan and P. Soulier, Dependence in Probability and Statistics, Springer Science, LLC, (2006).
[5] R.T. Clemen and T. Reilly, Correlations and Copulas for Decision and Risk Analysis, Management Science, 45 (1999), pp. 208-224.
[6] S. Celebioglu, Archimedean copulas And An Application, Selcuk University Journal of Science, 22 (2003), pp. 43-52.
[7] E.W. Frees and E.A. Valdez, Understanding Relationships Using Copulas, N. Am. Actuar. J., 2 (1998), pp. 1-25.
[8] A. Friend and E. Rogge, Correlation at First Sight, Economic Notes: Review of Banking, Finance and Monetary Economics, (2004).
[9] C. Genest and J. MacKay, Copules archimedienneset familles de loisbi dimensionnelles dont les margessontdonnees, Canad. J. Stat., 14 (1986a), pp. 145-159.
[10] C. Genest and J. MacKay, The joy of copula, Bivariate distributions with uniformmarginals, Amer. Stat., 40 (1986b), pp. 280-285.
[11] C. Genest, K. Ghoudi and L.P. Rivest, A semiparametric estimation procedure of dependence parameters in multivariate families of distributions, Biometrika, 82 (1995), pp. 543-552.
[12] C. Genest and L.P. Rivest, Statistical Inference Procedures for Bivariate Archimedean copulas, J. Am. Stat. Assoc., 88:423 (1993), pp. 1034-1043.
[13] C. Genest, B. Remillard and D. Beaudion, Goodness-of-fit tests for copulas: A review and a power study, Insurance: Mathematics and Economics, 44 (2009), pp. 199-213.
[14] L. Hua and H. Joe, Tail order and intermediate tail dependence of multivariate copulas, J. Multivariate Anal., 102 (2011), pp. 1454-1471.
[15] Z. Hui-Ming, L. Rong and L. Sufang, Modelling dynamic dependence between crude oil prices and Asia-Pacific stock market returns, International Review of Economics and Finance, 29 (2014), pp. 208-223.
[16] H. Joe, Asymptotic efficiency of the two-stage estimation method for copula-based models, J. Multivariate Anal., 94 (2005), pp. 401-419.
[17] H. Joe, Multivariate Models and Dependence Concepts, Chapman-Hall, London, (1997).
[18] I. Kojadinovic and J. Yan, Tests of serial independence for continuous multivariate time series based on a Mobius decomposition of the independence empirical copula process, Ann. Inst. Stat. Math., 63 (2011), pp. 347-373.
[19] D.D. Mari and S. Kotz, Correlation and Dependence, Imperial College Press, London, U.K, (2001).
[20] V. Najjari, T. Bacigal and H. Bal, An Archimedean copula family with hyperbolic cotangent generator, Int. J. Unc. Fuzz. Knowl. Based Syst., 22 (2014).
[21] V. Najjari, H. Bal and S. celebioglu, Modeling of Dependence Structures in Meteorological Data Via Archimedean Copulas, U.P.B. Sci. Bull., Series D, 75:3: (2013), pp. 131-138.
[22] V. Najjari, H. Bal, F. Ozturk and I. Alp, Stochastic Frontier Models By Copulas and An Application, U.P.B. Sci. Bull., Series A, 78 (2016).
[23] R.B. Nelsen, An introduction to copulas, Springer, New York, (2006), (Second edition).
[24] A. Pirmoradian and A. Hamzah, Simulation of Tail Dependence in cot-copula, Proceedings of the 58th WSC of the ISI, (2011).
[25] B. Schweizer, Thirty years of copulas. In: DallAglio G, Kotz S, Salinetti G (eds) Advances in Probability Distributions with Given Marginals, Kluwer, Dordrecht, (1991), pp. 13-50.
[26] P.X-K. Song, Correlated Data Analysis: Modeling, Analytics, and Applications, Springer Science, LLC, (2007).
[27] H. Tsukahara, Semiparametric estimation in copula models, Canad. J. Stat., 33 (2005), pp. 357-375.
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