Forecasting value at risk in the Swedish stock market - an investigation of GARCH volatility models

The purpose of this thesis was to investigate various conditional volatility models commonly used in forecasting financial risk within the field of Financial Econometrics. The GARCH, the GJR-GARCH and the T-GARCH models were examined. The models ability to forecast the conditional variance was investigated by forecasting the conditional volatility in four of the major Swedish stock indices, the OMXS30, Large Cap, Medium Cap and the Small Cap. The forecasted conditional volatility was then used to compute Value at Risk measurements, a measurement of risk that today is used in the risk management of most financial houses around the globe. The models ability to forecast the Value at Risk was then tested with Kupiecs unconditional coverage test. Support was found for the GJR-GARCH and T-GARCH models with Gaussian distributions producing the most satisfactory Value at Risk measures. KEYWORDS: ARCH, GARCH, GJR-GARCH, T-GARCH, Conditional Volatility, Value at Risk, Stock Indices.