The LSTM approach for Value at Risk prediction

Goreglyad, Nikanor

LSTM metoda pro předpověď Value at Risk

bachelor thesis (DEFENDED)

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Permanent link

http://hdl.handle.net/20.500.11956/147936

Identifiers

Study Information System: 213914

Referee

Červinka, Michal

Faculty / Institute

Faculty of Social Sciences

Discipline

Economics and Finance

Department

Institute of Economic Studies

Date of defense

8. 9. 2021

Publisher

Univerzita Karlova, Fakulta sociálních věd

Language

English

Grade

Very good

Keywords (Czech)

Value at risk, VaR, Rekurentní neuronové síte, Value at risk forecasting, Visegrádská skupina, LSTM, Joint Supervision, V4

Keywords (English)

Value at risk, VaR, Recurrent Neural Networks, Value at risk forecasting, Visegrád Four, LSTM, Joint Supervision, V4

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