Empirical likelihood intervals for conditional Value‐at‐Risk in ARCH/GARCH models

Value‐at‐Risk (VaR) is a simple, but useful measure in risk management. When some volatility model is employed, conditional VaR is of importance. As autoregressive conditional heteroscedastic (ARCH) and generalized ARCH (GARCH) models are widely used in modelling volatilities, in this article, we propose empirical likelihood methods to obtain an interval estimation for the conditional VaR with the volatility model being an ARCH/GARCH model.