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Risk Measurement and risk modelling using applications of Vine Copulas

Author

Listed:
  • David E. Allen

    () (School of Accounting, Finance and Economics Edith Cowan University, Australia.)

  • Michael McAleer

    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute, The Netherlands, Department of Quantitative Economics, Complutense University of Madrid, and Institute of Economic Research, Kyoto University.)

  • Abhay K. Singh

    (School of Accounting, Finance and Economics, Edith Cowan University)

Abstract

This paper features an application of Regular Vine copulas which are a novel and recently developed statistical and mathematical tool which can be applied in the assessment of composite financial risk. Copula-based dependence modelling is a popular tool in financial applications, but is usually applied to pairs of securities. By contrast, Vine copulas provide greater flexibility and permit the modelling of complex dependency patterns using the rich variety of bivariate copulas which may be arranged and analysed in a tree structure to explore multiple dependencies. The paper features the use of Regular Vine copulas in an analysis of the co-dependencies of 10 major European Stock Markets, as represented by individual market indices and the composite STOXX 50 index. The sample runs from 2005 to the end of 2011 to permit an exploration of how correlations change indiferent economic circumstances using three diferent sample periods: pre-GFC pre-GFC (Jan 2005- July 2007), GFC (July 2007-Sep 2009), and post-GFC periods (Sep 2009 - Dec 2011). The empirical results suggest that the dependencies change in a complex manner, and are subject to change in diferent economic circumstances. One of the attractions of this approach to risk modelling is the flexibility in the choice of distributions used to model co-dependencies. The practical application of Regular Vine metrics is demonstrated via an example of the calculation of the VaR of a portfolio made up of the indices.

Suggested Citation

  • David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Risk Measurement and risk modelling using applications of Vine Copulas," Documentos de Trabajo del ICAE 2014-09, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  • Handle: RePEc:ucm:doicae:1409
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    References listed on IDEAS

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    1. David E. Allen & Mohammad A. Ashraf & Michael McAleer & Robert J. Powell & Abhay K. Singh, 2013. "Financial dependence analysis: applications of vine copulas," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 403-435, November.
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    1. repec:gam:jsusta:v:10:y:2018:i:6:p:1809-:d:149805 is not listed on IDEAS
    2. repec:spr:empeco:v:53:y:2017:i:3:d:10.1007_s00181-016-1165-6 is not listed on IDEAS
    3. Alghalith, Moawia, 2016. "Novel and simple non-parametric methods of estimating the joint and marginal densities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 94-98.
    4. Huang, Wanling & Mollick, André Varella & Nguyen, Khoa Huu, 2016. "U.S. stock markets and the role of real interest rates," The Quarterly Review of Economics and Finance, Elsevier, vol. 59(C), pages 231-242.

    More about this item

    Keywords

    Regular Vine Copulas; Tree structures; Co-dependence modelling; European stock markets.;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics

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