Value-at-Risk for Greek Stocks

This paper analyses the application of several volatility models to forecast daily Value-at-Risk (VaR) both for single assets and portfolios. We calculate the VaR number for 4 Greek stocks, 2 portfolios based on these securities and for the Athens Stock Exchange General Index. We model VaR for long and short trading positions by employing non-parametric methods, such as historical and filtered historical simulation, as well as parametric ones. Especially for the later techniques we use a collection of ARCH models (GARCH, EGARCH and TARCH) based on three distributional assumptions (Normal, Student-T and Skewed Student-T), while we combine the Extreme Value Theory with a volatility updating technique (via GARCH type-modeling). In order to choose one model among the various forecasting methods, we employ a two-stage backtesting procedure. In the first one, we implement two backtesting criteria (unconditional and conditional coverage) to test the statistical accuracy of the models.