Application of GARCH Type Models in Forecasting Value at Risk

Dynamic risk management requires the risk measures to adapt to information at different times, such that this dynamic framework takes into account the time consistency of risk measures interrelated at different times. The value-at-risk (VaR) is one of the most well-known downside risk measures due to its intuitive meaning and a broad range of applications in practice, however, the static version embraces more popularity. This study investigates dynamic VaR modeling using four conditional volatility forecasting models: GARCH, TGARCH, GJRGARCH, and IGARCH, and compares the forecasting output of the suggested GARCH-based volatility models. Since the predictive accuracy of Value-at-Risk (VaR) models is crucial for adequate capitalization, we perform backtesting on VaR forecasts and compare our suggested GARCH models, as well as different distributions for their innovations and confidence levels for VaR