AN ALGORITHM FOR CONDITIONAL EXTREME VALUE THEORY GARCH-EVT TECHNIQUE FOR ESTIMATING VALUE AT RISK

Extreme events in financial time series are characterized by their low probability yet high impact and they pose significant challenges in financial risk management. This study aims to model and forecast extreme events, with a particular emphasis on Value at Risk (VaR) estimation. It explores the concept of conditional Extreme Value Theory (EVT) for modeling volatility series to estimate VaR by integrating Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with EVT, forming the GARCH-EVT approach. An automated algorithm was developed to optimize both model selection and threshold determination, ensuring accurate estimation of VaR. This automated procedure enhances the model selection process by identifying the optimal GARCH model and the most appropriate EVT threshold, addressing the complexities inherent in modeling extreme events. The comprehensive backtesting procedures are used to assess the effectiveness and precision of the algorithm in forecasting VaR, along with a simulation that evaluates both in-sample and out-of-sample performance of the model and candidate thresholds across various methods. The automated GARCH-EVT approach demonstrates effectiveness in accurately estimating VaR, providing a reliable and efficient method for extreme risk assessment in financial markets. This method streamlines the process of model selection and threshold optimization, contributing to improved risk management in financial markets.