An Empirical Investigation of Value at Risk (VaR) Forecasting Based on Range-Based Conditional Volatility Models

Value at Risk (VaR) is a widely used measure of market risk. Precision in the estimation volatility leads to accurate VaR forecasts. As time-varying and has clustering effect, GARCH class models helpful modeling more precisely. Studies have also shown that range-based estimates are efficient than traditional use only closing prices. Therefore, this study uses family model forecast VaR. The compares conventional prices alone, like TARCH, with RGARCH RTARCH models, where range defined as daily high price minus low introduced an exogenous variable, explore if latter provides better predictive accuracy. All back-tested using Kupiec (1995) unconditional coverage Christoffersen (1998) conditional tests. data period ranges from 2003-2021, we consider five BRICS indices three major developed economies, namely, USA, UK, Germany. An empirical investigation shows do job forecasting it information content prices, thereby giving estimates. hopes finding will greatly help stakeholders financial institutions, regulators, practitioners effective risk management.