Mária Bohdalová, Michal Greguš


The paper gives stochastic assessments of the financial crisis and discusses the Value at Risk European stocks from the point of view of copula based approach. Copula techniques can be based on the connection between rank correlation and certain one–parameter bivariate copulas. This relation allows easy calibration of the parameters. We use more general numerical calibration techniques that are based on maximum likelihood estimation (MLE). Using this approach we want to estimate VaR of the EU stocks portfolio using Monte Carlo simulation. The focus will be on modelling the interdependence between two risk factor returns. We suppose that the risk factor returns have some assumed marginal distributions, which need not be identical, and their dependency is modelled with copulas. We find that standard parametric copula functions (such as Gaussian) are not able to provide a good fit to the data. This is especially true when one or more of the marginal distributions has fat tails. We overcome this problem by fitting a t–copula with different marginal which can approximate any possible shape for the joint density.

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