Extreme Risk Forecast for Quantitative Financial Risk Management
Access Status
Open access
Date
2022Supervisor
Honglei Xu
Song Wang
Quanxi Shao
Type
Thesis
Award
PhD
Metadata
Show full item recordFaculty
Science and Engineering
School
School of Electrical Engineering, Computing and Mathematical Sciences
Collection
Abstract
Value at Risk (VaR) is one of the key risk measures for quantitative financial risk management. VaR measures extreme risk, which has a small probability but a significant consequence to financial institutions. This thesis develops methods based on an extended extreme value approach to improve the forecast skill of VaR. The proposed methods improve the forecasting accuracy, robustness, efficiency and outperform the existing methods in the literature.
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