Multivariate option pricing using copulae

Applied Stochastic Models in Business and Industry

Volumn 29, Issue 5, 2013, Pages 509-526

  Bernard, Carole ^a   Czado, Claudia ^b  

^a UNIVERSITY OF WATERLOO   (Canada)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

basket options; multivariate derivatives; pair copula construction; pricing

Indexed keywords

BASKET OPTION; FINANCIAL PRODUCTS; GARCH (1 ,1); HISTORICAL ASSETS; MULTIPLE INDEX; OPTION PRICING; PAIR COPULAS; RISK-NEUTRAL PROBABILITY;

COSTS; ECONOMIC AND SOCIAL EFFECTS;

ECONOMICS;

EID: 84885604559     PISSN: 15241904     EISSN: 15264025     Source Type: Journal    
DOI: 10.1002/asmb.1934     Document Type: Article

References (32)

1. Galichon A,. Modelling correlation between two Markov diffusion processes: a copula approach, with application to stochastic correlation modelling, 2006. Working paper.
2. Dupire B,. Pricing with a smile. Risk 1994; 7: 18-20.
3. Langnau A,. Introduction into 'local correlation' modelling, 2009. ArXiv Working Paper 0909:3441v3.
4. Rosenberg J,. Nonparametric pricing of multivariate contingent claims. Journal of Derivatives 2003; 10 (3): 9-26.
5. Cherubini U, Luciano E,. Bivariate option pricing with copulas. Applied Mathematical Finance 2002; 9 (2): 69-85.
6. Van den Goorbergh R, Genest C, Werker B,. Bivariate option pricing using dynamic copula models. Insurance: Mathematics and Economics 2005; 37 (1): 101-114.
7. Duan J-C,. The GARCH option pricing model. Mathematical Finance 1995; 5 (1): 13-32.
8. Aas K, Czado C, Frigessi A, Bakken H,. Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 2009; 44 (2): 182-198.
9. Sklar A,. Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut de Statistique de l'Université de Paris 1959; 8: 229-231.
10. Joe H,. Multivariate Models and Dependence Concepts. Chapman & Hall: New York, 1997.
11. Brechmann EC, Schepsmeier U,. CDVine: Statistical inference of C- and D-vine copulas, 2011. Vignette for R package version 1.1, http://cran.r-project. org/web/packages/CDVine/
12. Chiou S, Tsay R,. A copula-based approach to option pricing and risk assessment. Journal of Data Science 2008; 6: 273-301.
13. Ausin MC, Lopes HF,. Time-varying joint distribution through copulas. Computational Statistics & Data Analysis 2010; 54 (11): 2383-2399.
14. Hafner C, Manner H,. Dynamic stochastic copula models: estimation, inference and applications, 2008. METEOR Research Memorandum (RM) 07/043.
15. Almeida C, Czado C,. Efficient Bayesian inference for stochastic time-varying copula model. Forthcoming in Computational Statistics & Data Analysis 2012; 56 (6): 1511-1527.
16. Joe H,. Families of m-variate distributions with given margins and m(m-1) â̂ 2 bivariate dependence parameters. In In Distributions with Fixed Marginals and Related Topics., Rüschendorf L, Schweizer B, Taylor MD, (eds). Institute of Mathematical Statistics: Hayward, California, 1996.
17. Bedford T, Cooke RM,. Probability density decomposition for conditionally dependent random variables modeled by vines. Annals of Mathematics and Artificial Intelligence 2001; 32 (1-4): 245-268.
18. Bedford T, Cooke RM,. Vines-a new graphical model for dependent random variables. Annals of Statistics 2002; 30 (4): 1031-1068.
19. Czado C,. Pair-copula constructions of multivariate copulas. In In Workshop on Copula Theory and its Applications., Durante F, Härdle W, Jaworki P, Rychlik T, (eds). Springer: Dortrech. 2010.
20. Kurowicka D, Joe H,. Dependence Modeling-Handbook on Vine Copulae. World Scientific Publishing Co.: Singapore, 2011.
21. Czado C, Schepsmeier U, Min A,. Maximum likelihood estimation of mixed C-vines with application to exchange rates. Forthcoming in Statistical Modelling 2012; 12 (3): 229-255.
22. Dissmann J, Brechmann E, Czado C, Kurowicka D,. Selecting and Estimating Regular Vine Copulae and Application to Financial Returns, 2011. http://arxiv.org/abs/1202.2002v2.
23. Heinen A, Valdesogo A,. Asymmetric CAPM Dependence for Large Dimensions: The Canonical Vine Autoregressive Copula Model, 2009. Preprint, Available at SSRN: http://ssrn.com/abstract=1297506
24. Brechmann E, Czado C, Aas K,. Truncated regular vines in high dimensions with application to financial data. Forthcoming in the Canadian Journal of Statistics 2012; 40 (1): 68-85.
25. Brechmann EC, Czado C,. Extending the CAPM Using Pair Copulas: The Regular Vine Market Sector Model, 2011. Submitted for publication.
26. Haff IH,. Parameter estimation for pair-copula constructions. Forthcoming in Bernoulli Journal 2012. http://www.bernoulli-society.org/index.php/ publications/bernoulli-journal/bernoulli-journal-papers.
27. Joe H,. Asymptotic efficiency of the two-stage estimation method for copula-based models. Journal of Multivariate Analysis 2005; 94 (2): 401-419.
28. Joe H, Xu J,. The estimation method of inference functions for margins for multivariate models, Technical Report no. 166, Department of Statistics, University of British Columbia, 1996.
29. Kim G, Silvapulle MJ, Silvapulle P,. Comparison of semiparametric and parametric methods for estimating copulas. Computational Statistics and Data Analysis 2007; 51 (6): 2836-2850.
30. Nikoloulopoulos A, Joe H, Li H,. Vine copulas with asymmetric tail dependence and applications to financial return data. forthcoming in Computational Statistics and Data Analysis 2012.
31. Bernard C, Boyle P, Gornall W,. Locally-capped investment products and the retail investor. Journal of Derivatives 2011; 18 (4): 72-88.
32. Brechmann E,. Truncated and simplified regular vines and their applications. Diploma thesis, Technische Universität München, 2010.