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Estimating Value-at-Risk using a Multivariate Copula-Based Volatility Model

Sampid, M and Hasim, H 'Estimating Value-at-Risk using a Multivariate Copula-Based Volatility Model.' The European Journal of Finance. ISSN 1351-847X (Submitted)

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This paper proposes a multivariate copula-based volatility model for estimating value-at-Risk in banks of some selected European countries by combining Dynamic Conditional Correlation (DCC) multivariate GARCH (M-GARCH) volatility model and copula functions. Nonnormality in multivariate models is associated with the joint probability of the univariate models? marginal probabilities ? the joint probability of large market movements, referred to as tail dependence. In this paper, we use copula functions to model the tail dependence of large market movements and test the validity of our results by performing back-testing techniques. The results show that the copula-based approach provides better estimates than the common methods currently used and captures VaR well based on the differences in the numbers of exceptions produced during different observation periods at the same confidence level.

Item Type: Article
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HG Finance
Depositing User: Haslifah Hasim
Date Deposited: 22 Jun 2017 10:57
Last Modified: 08 Jan 2018 11:15

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