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    Extreme Risk Forecast for Quantitative Financial Risk Management

    Zhang L 2022 Public.pdf (851.6Kb)
    Access Status
    Open access
    Authors
    Zhang, Lequn
    Date
    2022
    Supervisor
    Honglei Xu
    Song Wang
    Quanxi Shao
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Science and Engineering
    School
    School of Electrical Engineering, Computing and Mathematical Sciences
    URI
    http://hdl.handle.net/20.500.11937/89362
    Collection
    • Curtin Theses
    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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