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  • Μικρογραφία εικόνας
    Τεκμήριο
    Machine learning methods for measuring extreme risks and an empirical application on financial markets
    (2022-03-16) Kosti, Konstantina; Κωστή, Κωνσταντίνα; Athens University of Economics and Business, Department of Economics; Antoniou, Fabio; Varthalitis, Petros; Dendramis, Yiannis
    In this paper I try to present the most common financial and non-financial risks that a Bank or anyFinancial Institution face and which originate from their sector of activity itself. Moreover, there isan analytical presentation of the statistical tools and machine learning methods that are used for theirdetection and calculation. Afterwards, my attention focuses on several techniques such as Value atRisk, Expected Shortfall and Backtesting that Banks and Financial Institutions are obliged toperform, alongside with a very detailed presentation of the main Machine Learning Tools that existand that help with the realization of these techniques. Finally, in my empirical analysis I use high-frequency financial data to perform these techniques and to provide an illustration of the sectionsabove.