Quantile Regression for VaR Estimation in Egyptian Inflation Rate: A Comparative Analysis with EWMA and t-GARCH | ||||
| المجلة العلمية للدراسات والبحوث المالية والتجارية | ||||
| Article 21, Volume 6, Issue 1, January 2025, Page 655-685 PDF (2.92 MB) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/cfdj.2024.325360.2063 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Fatma Alshenawy  
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| جامعة المنصورة- كلية التجارة - قسم الاحصاء التطبيقي | ||||
| Abstract | ||||
| 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. | ||||
| Keywords | ||||
| Quantile Regression; Value at Risk (VaR); EWMA; t-GARCH; Back-testing; Kupiec's Test | ||||
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