Aggregation of dependent risks using the koehler-Symanowski copula function

Computational Economics

Volumn 25, Issue 1-2, 2005, Pages 189-205

  Palmitesta, Paola ^a   Provasi, Corrado ^b  

^a UNIVERSITY OF SIENA   (Italy)

^b UNIVERSITY OF PADOVA   (Italy)

Author keywords

Asset returns; IFM method; Measures of dependence; Minimum distance estimation; Skew distributions

Indexed keywords

EID: 24144438572     PISSN: 09277099     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10614-005-6282-9     Document Type: Article

References (22)

1. Ayebo, A. and Kozubowski, T.J. (2003). An asymmetric generalization of gaussian and Laplace laws. Journal of Probability and Statistical Science, 1 (2), 187-210.
2. Caputo, A. (1998). Some properties of the family of Koehler-Symanowski distributions. The Collaborative Research Center (SBF) 386, Discussion Paper No. 103, University of Munich.
3. U. Cherubini E. Luciano W. Vecchiato 2004 Copula Methods in Finance Wiley New York
4. Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1, 223-236. 10.1088/ 1469-7688/1/2/304
5. Cook, R.D. and Johnson, M.E. (1981). A family of distributions for modelling non-elliptically symmetric multivariate data. Journal of the Royal Statististical Society B, 43, 210-218.
6. Cook, R.D. and Johnson, M.E. (1987). Generalized Burr-Pareto-logistic distributions with applications to a uranium exploration data set. Technometrics, 28, 123-131.
7. Embrechts, P., McNeil, A. and Straumann, D. (1999). Correlation: Pitfalls and alternatives. RISK Magazine, May, 69-71.
8. P. Embrechts A. McNeil D. Straumann 2002 Correlation and dependence in risk management: Properties and pitfalls In: M.A.H. Dempster (ed.), Risk Management: Value at Risk and Beyond Cambridge University Press Cambridge pp. 176-223
9. Embrechts, P., Lindskog, F. and McNeil, A. (2003). Modelling dependence with copulas and applications to risk management. In S. Rachev (ed.), Handbook of Heavy Tailed Distributions in Finance, Chapter 8, 329-384. Elsevier.
10. Fernández, C. and Steel, M.F.J. (1998). On bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93, 359-371.
11. M.E. Johnson 1987 Multivariate Statistical Simulation Wiley New York
12. Koehler, K.J. and Symanowski, J.T. (1995). Constructing multivariate distributions with specific marginal distributions, Journal of Multivariate Analysis, 55, 261-282.
13. Joe, H. (1997). Multivariate Models and Dependence Concepts. Chapman & Hall, London.
14. Kodde, D., Palm, F. and Pfann, G. (1990). Asymptotic least squares estimation efficiency considerations and applications. Journal of Applied Econometrics, 5, 229-243.
15. Lambert, P. and Laurent, S. (2001). Modelling Skewness Dynamics in Series of Financial Data Using Skewed Location-Scale Distributions. Discussion Paper No. 01-25, Institut de Statistique, Université Catholique de Louvain, Louvain-la-Neuve, Belgium.
16. Manomaiphiboon, K. and Russel, A.G. (2003). Formulation of joint probability density functions of velocity for turbolent flows: An alternative approach. Atmospheric Environment, 37, 4917-4925. 10.1016/ j.atmosenv.2003.08.022
17. R.B. Nelsen 1999 An Introduction to Copulas Springer New York
18. Palmitesta, P. and Provasi, C. (2004). GARCH-type models with generalized secant hyperbolic innovations. Studies in Nonlinear Dynamics & Econometrics, 8 (2), Article 7. http://www.bepress.com/snde/vol8/iss2/art7
19. Rosenberg, J.V. and Schuermann, T. (2004). A General Approach to Integrated Risk Management with Skewed, Fat-Tailed Risk. FRB of New York Staff Report No. 185.
20. Sheather, S.J. and Jones, M.C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B, 53, 683-690.
21. Siegel, S. (1956). Non-Parametric Statistics for the Behavioral Sciences. McGraw-Hill.
22. S. Wolfram 1999 The Mathematica Book 4th edn. Wolfram Media/Cambridge University Press Cambridge