| Summary: | Risk management methods in finance have put a lot of weight on the
Value-at-Risk, making it the most popular risk measurement tools. The
main purpose of VaR models is to capture future market risks accurately.
Thus it is important to gauge the predictive ability of VaR estimates.
Consequently, backtesting methods have been developed in order to
systematically compare VaR estimates to actual market losses and profits.
It is important that the appropriate backtest techniques are used.
Subsequently a model is accepted or rejected based on these tests. This
thesis consists mainly of a contribution to empirical studies of VaR. The
empirical research of this thesis focuses on the performance of the FTSE
index during years leading to the 2008 Global Financial Crisis till after the
crisis. The main aim of this study is to evaluate the predictive ability of
VaR models in foreseeing the 2008 crisis which affected the UK. VaR
forecasts are estimated by various VaR models in three periods of
different market conditions. The secondary aim attempts to choose the
most reliable backtest. A third and sub-objective, is to provide a
benchmark study on the performance of VaR models when time horizon is
varied. This may provide valuable information on the rigidity or flexibility
of a model and thus its role in other topics of risk management e.g crisis
management. Tests of unconditional and conditional coverage are applied
together with tests of independence. Empirical Coverage Probability and
Basel Traffic Light test are also employed. This thesis is a univariate
analysis focused in the UK. 1-day, 5-day and 10-day VaR estimates for a
one year time period are used in the backtesting process. Results from
the backtesting shed light on potential problems within the VaR system.
As time horizons increase, severe underestimation and dependence of risk
are observed. Furthermore, unstable market conditions cause problems in
backtesting evaluation since VaR models are commonly recognized to
perform well only under normal market conditions.
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