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Bayesian Value-at-Risk for a Portfolio: Multi- and Univariate Approaches Using MSF-SBEKK Models

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  • Jacek Osiewalski

    ()
    (Cracow University of Economics)

  • Anna Pajor

    ()
    (Cracow University of Economics)

Abstract

The s-period ahead Value-at-Risk (VaR) for a portfolio of dimension n is considered and its Bayesian analysis is discussed. The VaR assessment can be based either on the n-variate predictive distribution of future returns on individual assets, or on the univariate Bayesian model for the portfolio value (or the return on portfolio). In both cases Bayesian VaR takes into account parameter uncertainty and non-linear relationship between ordinary and logarithmic returns. In the case of a large portfolio, the applicability of the n-variate approach to Bayesian VaR depends on the form of the statistical model for asset prices. We use the n-variate type I MSF-SBEKK(1,1) volatility model proposed specially to cope with large n. We compare empirical results obtained using this multivariate approach and the much simpler univariate approach based on modelling volatility of the value of a given portfolio.

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Bibliographic Info

Article provided by CEJEME in its journal Central European Journal of Economic Modelling and Econometrics.

Volume (Year): 2 (2010)
Issue (Month): 4 (September)
Pages: 253-277

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Handle: RePEc:psc:journl:v:2:y:2010:i:4:p:253-277

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Web page: http://cejeme.org/

Related research

Keywords: Bayesian econometrics; risk analysis; multivariate GARCH processes; multivariate SV processes; hybrid SV-GARCH models;

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  1. Susan Thomas & Mandira Sarma & Ajay Shah, 2003. "Selection of Value-at-Risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 337-358.
  2. Jacek Osiewalski & Anna Pajor, 2009. "Bayesian Analysis for Hybrid MSF-SBEKK Models of Multivariate Volatility," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 1(2), pages 179-202, November.
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Cited by:
  1. Ewa Ratuszny, 2013. "Robust Estimation in VaR Modelling - Univariate Approaches using Bounded Innovation Propagation and Regression Quantiles Methodology," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 5(1), pages 35-63, March.

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