This paper investigates the efficacy of Quantile Regression, Exponentially Weighted Moving Average (EWMA), and t-GARCH models in estimating Value at Risk (VaR) for Egyptian inflation rate. Through empirical analysis and Back-testing, we demonstrate that Quantile Regression outperforms the other models in accuracy and reliability for capturing tail risks. By directly modelling the quantiles of return distributions, Quantile Regression provides a robust framework for VaR estimation, effectively addressing non-linearities and outliers in financial data. The model's ability to directly estimate quantiles allows for a nuanced understanding of extreme inflationary movements.  Our findings suggest that Quantile Regression is a superior tool for risk management, offering significant advantages in precision and adaptability compared to traditional methods, which is offering valuable insights for risk managers and policymakers.