The article introduces a new model, RHAR-QREG, for predicting Value-at-Risk (VaR) in financial markets. This model outperforms popular methods like Historical Simulation and GARCH(1,1) in forecasting one-day-ahead VaR. The study shows that capturing the distribution of returns is more crucial than volatility clustering for accurate VaR estimation.