Value-at-Risk Estimation Under Shifting Volatility

Sammanfattning: Due to the Basel III regulations, Value-at-Risk (VaR) as a risk measure has become increasingly important in Europe for financial institutions. But even though it has become an important risk measure, both internally within company reporting and externally due to legislation, there is no one single way to forecast VaR that has yet proven to be superior. The aim of this paper is to examine different models of VaR estimation on the OMXS30 and FTSE100 indices. I divided the in-sample time periods into one period of low volatility and one period of high volatility. From there, I have calculated VaR with different underlying GARCH models, both symmetrical and asymmetrical. To evaluate the different Value-at-Risk models, the Christoffersen test was used. For the two time series where the in-sample period had high volatility and the out-of-sample period has low volatility, the asymmetric GARCH models seemed to perform best at estimating Value-at-Risk. The converse relationship was found for the time series where the in-sample had low volatility. Furthermore, an assumption of t-distributed returns worked better than the normal distribution.