🎓 Bachelor Thesis - On Value at Risk Prediction Using Quantile Regression Methods
- 1 min🎓 Bachelor Thesis -On Value at Risk Prediction Using Quantile Regression Methods
Institution: Ludwig-Maximilians-Universität München, Munich, Germany 📍 Duration: June 2021 - September 2021 ⏳
🌐 A Changing Financial Landscape: The Evolution of Risk Management
Over the years, dramatic changes in trading volumes and risk management practices in financial markets have necessitated the development of robust risk estimation tools. Value at Risk (VaR) has emerged as a key tool for estimating financial risk, but traditional VaR estimators often fall short when dealing with real data. The focus has therefore shifted to the development of more robust tools such as quantile regression.
💡 Approach: Quantile Regression for Robust Risk Estimation
In this project, I delved deep into Quantile Regression, a technique perfectly suited for estimating VaR and known for its robustness to extreme shocks. The project covers the basics of quantile regression and discusses the quantile treatment effect and equivariance, providing a thorough understanding of this robust risk management tool.
🎯 Contributions: New Approaches and Future Directions
The project provides an in-depth exploration of different models and methodologies related to quantile regression and VaR. It presents and evaluates three key approaches: Exponential Weighted Moving Average (EWMA), Historical Simulation and the Quantile Regression approach. The thesis concludes with possible future research directions in this area.
🧠 Skills and Acquired Knowledge
Throughout this project I honed a range of skills including statistical modelling, financial analysis, R programming and risk management. The aim was to shed light on quantile regression as a robust risk management tool and its applications in financial markets.
Medina Feldl
Data Scientist and Biostatistician