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.
Alshenawy, Fatma. (2025). Quantile Regression for VaR Estimation in Egyptian Inflation Rate: A Comparative Analysis with EWMA and t-GARCH. المجلة العلمية للدراسات والبحوث المالية والتجارية, 6(1), 655-685. doi: 10.21608/cfdj.2024.325360.2063
MLA
Fatma Alshenawy. "Quantile Regression for VaR Estimation in Egyptian Inflation Rate: A Comparative Analysis with EWMA and t-GARCH", المجلة العلمية للدراسات والبحوث المالية والتجارية, 6, 1, 2025, 655-685. doi: 10.21608/cfdj.2024.325360.2063
HARVARD
Alshenawy, Fatma. (2025). 'Quantile Regression for VaR Estimation in Egyptian Inflation Rate: A Comparative Analysis with EWMA and t-GARCH', المجلة العلمية للدراسات والبحوث المالية والتجارية, 6(1), pp. 655-685. doi: 10.21608/cfdj.2024.325360.2063
VANCOUVER
Alshenawy, Fatma. Quantile Regression for VaR Estimation in Egyptian Inflation Rate: A Comparative Analysis with EWMA and t-GARCH. المجلة العلمية للدراسات والبحوث المالية والتجارية, 2025; 6(1): 655-685. doi: 10.21608/cfdj.2024.325360.2063
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