Application of extreme value theory in the financial industry

Abstract

This project explores the application of Extreme Value Theory (EVT) for developing risk management frameworks in the cryptocurrency market a domain characterized by high volatility and frequent extreme price movements. Recognizing the limitations of traditional risk models in capturing rare but impactful market events - this study aims to employ the Generalized Pareto Distribution (GPD) to model tail risks effectively. By applying EVT to financial datasets the research identifies extreme events and quantifies their likelihood and potential impact. The proposed framework enhances the accuracy of Value at Risk (VaR) and Expected Shortfall (ES) calculations providing financial institutions with more reliable risk assessments. The findings contribute to improved decision-making in portfolio management and stress testing. This research also offers actionable insights for financial practitioners seeking to mitigate extreme market risks through more resilient risk management strategies and practices.