This dissertation focuses on the estimation of Value at Risk in six European Stock
Exchanges from the beginning of the millennium. It presents the theoretical framework
regarding the VaR techniques as well as the ARCH models which are commonly used
in the estimation of market risk. On the empirical part, the dissertation provides an
insight into parametric models like Risk Metrics and non parametric like Historical
Simulation and in order to evaluate their predictive ability during the recent global
financial crisis they are backtested. In addition, models of the ARCH family are being
presented extensively since they are commonly used in the VaR forecasting procedure.
The Akaike’s Information as well as the Schwarz’s Bayesian Information Criterion are
examined so as to be concluded if the aforementioned models are trustworthy and could
predict VaR accurately.
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