Modelling and Estimating of VaR Through the GARCH Model

This study focuses on the analysis of fiscal series with time-varying conditional variance utilizing the ARIMA-GARCH with Value at Risk (VaR) model. ARIMA-GARCH can predict risk when stock variance is Heteroscedasticity. The price of the Reliance stock is analyzed for fifty months. This research indicates that the VaR is a useful technique to reduce risk exposure and perhaps avoid losses when investing in the Reliance stock. The findings show that ARIMA (0,0,0)-GARCH (1,1) has the best fit, with an Akaike information criterion (AIC) value of −5915.325, at a confidence level of 95%. The GARCH technique is used to determine the conditional variance of the residuals and contrasts it with the delta-normal method. At a 95% confidence level, the VaR is used to calculate the likelihood of losing an investment by 2.7% or more in a single day.