Free Access
Volume 18, 2014
Page(s) 667 - 685
Published online 15 October 2014
  1. B.C. Arnold and S.J. Press, Bayesian-inference for Pareto populations. J. Econom. 21 (1983) 287–306. [CrossRef] [Google Scholar]
  2. A. Balkema and E. Pancheva, Decomposition of multivariate extremal processes. Commun. Stat Theory Methods 25 (1996) 737–758. [CrossRef] [Google Scholar]
  3. V. Barnett, Some outlier tests for multivariate samples. South African Stat. J. 13 (1979) 29–52. [Google Scholar]
  4. N. Bingham, C. Goldie and J. Teugels, Regular Variation. Cambridge University Press, Cambridge (1987). [Google Scholar]
  5. E. Bouyé, Multivariate extremes at work for portfolio risk measurement. Finance 23 (2002) 125–144. [Google Scholar]
  6. A.C. Cebrián, M. Denuit and P. Lambert, Analysis of bivariate tail dependence using extreme value copulas: An application to the SOA medical large claims database. Belgian Actuarial Bull. 3 (2003) 33–41. [Google Scholar]
  7. S.G. Coles and J.A. Tawn, Statistical methods for multivariate extremes: An application to structural design (with discussion). J. Appl. Stat. 43 (1994) 1–48. [CrossRef] [Google Scholar]
  8. S. Demarta and A.J. McNeil, The t copula and related copulas. Int. Stat. Rev. 73 (2005) 111–129. [Google Scholar]
  9. A. Dias and P. Embrechts, Dynamic copula models for multivariate high-frequency data in finance. Working Paper, ETH Zurich (2003). [Google Scholar]
  10. P. Embrechts and M. Maejima, Self-similar Processes. Princeton University Press, Princeton (2002). [Google Scholar]
  11. S. Eryilmaz and F. Iscioglu, Reliability evaluation for a multi-state system under stress-strength setup. Commun. Stat. Theory Methods 40 (2011) 547–558. [Google Scholar]
  12. L. Fawcett and D. Walshaw, Markov chain models for extreme wind speeds. Environmetrics 17 (2006) 795–809. [CrossRef] [Google Scholar]
  13. J. Galambos, Order statistics of samples from multivariate distributions. J. Amer. Stat. Assoc. 70 (1975) 674–680. [Google Scholar]
  14. A. Ghorbel and A. Trabelsi, Measure of financial risk using conditional extreme value copulas with EVT margins. J. Risk 11 (2009) 51–85. [Google Scholar]
  15. I.S. Gradshteyn and I.M. Ryzhik, Table of Integrals, Series, and Products, 7th edition. Academic Press, San Diego (2007). [Google Scholar]
  16. W. Hürlimann, Fitting bivariate cumulative returns with copulas. Comput. Stat. Data Anal. 45 (2004) 355–372. [CrossRef] [Google Scholar]
  17. J. Hüsler and R.-D. Reiss, Maxima of normal random vectors: Between independence and complete dependence. Stat. Probab. Lett. 7 (1989) 283–286. [CrossRef] [Google Scholar]
  18. T.P. Hutchinson, Latent structure models applied to the joint distribution of drivers’ injuries in road accidents. Stat. Neerlandica 31 (1977) 105–111. [CrossRef] [Google Scholar]
  19. Hutchinson, T.P. and Satterthwaite, S.P. (1977). Mathematical-models for describing clustering of sociopathy and hysteria in families. British J. Psychiatry 130 294–297. [CrossRef] [MathSciNet] [Google Scholar]
  20. D. Jansen, and C. de Vries, On the frequency of large stock market returns: Putting booms and busts into perspective. Rev. Econ. Stat. 23 (1991) 18–24. [CrossRef] [Google Scholar]
  21. S. Jäschke, Estimation of risk measures in energy portfolios using modern copula techniques. Discussion Paper No. 43, Dortmund (2012). [Google Scholar]
  22. H. Joe, and H. Li, Tail risk of multivariate regular variation. Methodology Comput. Appl. Probab. 13 (2011) 671–693. [CrossRef] [Google Scholar]
  23. H. Joe, R.L. Smith and I. Weissman, Bivariate threshold methods for extremes. J. R. Stat. Soc. B 54 (1992) 171–183. [Google Scholar]
  24. R.B. Langrin, Measuring extreme cross-market dependence for risk management: The case of Jamaican equity and foreign exchange markets. Financial Stability Department, Research and Economic Program. Division, Bank of Jamaica (2004). [Google Scholar]
  25. L. Lescourret and C. Robert, Estimating the probability of two dependent catastrophic events. ASTIN Colloquium. International Acturial Association, Brussels, Belgium (2004). [Google Scholar]
  26. L. Lescourret, and C.Y. Robert, Extreme dependence of multivariate catastrophic losses. Scandinavian Actuarial J. (2006) 203–225. [CrossRef] [Google Scholar]
  27. K.-G. Lim, Global financial risks, CVaR and contagion management. J. Business Policy Res. 7 (2012) 115–130. [Google Scholar]
  28. D.V. Lindley and N.D. Singpurwalla, Multivariate distributions for the life lengths of components of a system sharing a common environment. J. Appl. Probab. 23 (1986) 418–431. [CrossRef] [Google Scholar]
  29. X. Luo and P.V. Shevchenkoa, The t copula with multiple parameters of degrees of freedom: Bivariate characteristics and application to risk management. Quant. Finance 10 (2010) 1039–1054. [CrossRef] [MathSciNet] [Google Scholar]
  30. M. Ma, S. Song, L. Ren, S. Jiang and J. Song, Multivariate drought characteristics using trivariate Gaussian and Student t copulas. Hydrological Proc. 27 (2013) 1175–1190. [CrossRef] [Google Scholar]
  31. K.V. Mardia, Multivariate Pareto distributions. Ann. Math. Stat. 33 (1962) 1008–1015. [CrossRef] [Google Scholar]
  32. M.F. Mcgrath, D. Gross and N.D. Singpurwalla, A subjective Bayesian approach to the theory of queues I Modeling. Queueing Systems 1 (1987) 317–333. [CrossRef] [Google Scholar]
  33. M.M. Meerschaert, and E. Scalas, Coupled continuous time random walks in finance. Phys. A: Stat. Mech. Appl. 370 (2006) 114–118. [Google Scholar]
  34. M.M. Meerschaert and H.-P. Scheffler, Limit Distributions for Sums of Independent Random Vectors: Heavy Tails Theory Practice. Wiley, New York (2001). [Google Scholar]
  35. M.M. Meerschaert and H.-P. Scheffler, Portfolio modeling with heavy tailed random vectors, in Handbook of Heavy-Tailed Distributions in Finance, edited by S.T. Rachev. Elsevier, New York (2003) 595–640. [Google Scholar]
  36. B.V.M. Mendes and A.R. Moretti, Improving financial risk assessment through dependency. Stat. Model. 2 (2002) 103–122. [CrossRef] [MathSciNet] [Google Scholar]
  37. I. Mitov, S. Rachev and F. Fabozzi, Approximation of aggregate and extremal losses within the very heavy tails framework. Technical Report, University of Karlsrhue, University of California, Santa Barbara, submitted to Quant. Finance (2008). [Google Scholar]
  38. M. Mohsin, G. Spöck and J. Pilz, On the performance of a new bivariate pseudo Pareto distribution with application to drought data. Stochastic Environmental Research and Risk Assessment 26 (2011) 925–945. [CrossRef] [Google Scholar]
  39. M. Motamedi and R.Y. Liang, Probabilistic landslide hazard assessment using Copula modeling technique. Landslides 11 (2013) 565–573. [CrossRef] [Google Scholar]
  40. A. Nazemi and A. Elshorbagy, Application of copula modelling to the performance assessment of reconstructed watersheds. Stochastic Environmental Research and Risk Assessment 26 (2013) 189–205. [CrossRef] [Google Scholar]
  41. M.W. Ng and H.K. Lo, Regional air quality conformity in transportation networks with stochastic dependencies: A theoretical copula-based model. Networks and Spatial Economics 13 (2013) 373–397. [CrossRef] [MathSciNet] [Google Scholar]
  42. E. Pancheva and P. Jordanova, A functional extremal criterion. J. Math. Sci. 121 (2004) 2636–2644. [CrossRef] [MathSciNet] [Google Scholar]
  43. E. Pancheva and P. Jordanova, Functional transfer theorems for maxima of iid random variables. C. R. Acad. Bulgare Sci. 57 (2004b) 9–14. [Google Scholar]
  44. E. Pancheva, E. Kolkovska and P. Jordanova, Random time-changed extremal processes. Theory Probab. Appl. 51 (2006) 752–772. [Google Scholar]
  45. E. Pancheva, I. Mitov and Z. Volkovich, Sum and extremal processes over explosion area. C. R. Acad. Bulgare Sci. 59 (2006) 19–26. [MathSciNet] [Google Scholar]
  46. E. Pancheva, I. Mitov and Z. Volkovich, Relationship between extremal and sum processes generated by the same point process. Serdika 35 (2009) 169–194. [Google Scholar]
  47. E.N. Papadakis and E.G. Tsionas, Multivariate Pareto distributions: Inference and financial applications. Commun. Stat. Theory Methods 39 (2010) 1013–1025. [CrossRef] [Google Scholar]
  48. J. Pickands, Multivariate extreme value distributions (with a discussion). In: Proc. of the 43rd Session of the International Statistical Institute, Bull. Int. Stat. Institute 49 (1981) 859–878, 894–902. [Google Scholar]
  49. A.P. Prudnikov, Y.A. Brychkov and O.I. Marichev, Integrals and Series, vols. 1, 2 and 3. Gordon and Breach Science Publishers, Amsterdam (1986). [Google Scholar]
  50. S. Rachev and S. Mittnik, Stable Paretian Models in Finance. Wiley, Chichester (2000). [Google Scholar]
  51. J.F. Rosco and H. Joe, Measures of tail asymmetry for bivariate copulas. Stat. Papers 54 (2013) 709–726. [CrossRef] [Google Scholar]
  52. J.A. Tawn, Bivariate extreme value theory: Models and estimation. Biometrika 75 (1988) 397–415. [CrossRef] [Google Scholar]
  53. T. Tokarczyk and W. Jakubowsk, Temporal and spatial variability of drought in mountain catchments of the Nysa Klodzka basin. In: Climate Variability and Change – Hydrological Impacts, vol 308, Proc. of 15th FRIEND world conference held at Havana. Edited by S. Demuth, A. Gustard, E. Planos, F. Scatena and E. Servat. 308 (2006) 139–144. [Google Scholar]
  54. X. Yang, E.W. Frees and Z. Zhang, A generalized beta copula with applications in modeling multivariate long-tailed data. Insurance: Math. Econ. 49 (2011) 265–284. [CrossRef] [Google Scholar]
  55. M.A. Youngren, Dependence in target element detections induced by the environment. Naval Research Logistics 38 (1991) 567–577. [CrossRef] [Google Scholar]
  56. S. Yue, The Gumbel mixed model applied to storm frequency analysis. Water Resources Management 14 (2000) 377–389. [CrossRef] [Google Scholar]
  57. Q. Zhang, V.P. Singh, J. Lia, F. Jiang and Y. Bai, Spatio-temporal variations of precipitation extremes in Xinjiang, China. J. Hydrology 434-435 (2012) 7–18. [CrossRef] [Google Scholar]

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