Artikel
Bootstrapping time-varying uncertainty intervals for extreme daily return periods
This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which are based on a weighted threshold and quantile of a continuously ranked probability score, are developed. Developed backtesting procedures revealed that an estimated Seasonal autoregressive integrated moving average-generalized autoregressive score-generalized extreme value distribution (SARIMA-GAS-GEVD) with a skewed student-t distribution had the best prediction performance in forecasting and bootstrapping VaR and ES. Extension of this non-stationary distribution in literature is quite complicated since it requires specifications not only on how the usual Bayesian parameters change over time but also those with bulk distribution components. This implies that the combination of a stochastic econometric model with extreme value theory (EVT) procedures provides a robust basis necessary for the statistical backtesting and bootstrapping density predictions for VaR and ES.
- Sprache
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Englisch
- Erschienen in
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Journal: International Journal of Financial Studies ; ISSN: 2227-7072 ; Volume: 10 ; Year: 2022 ; Issue: 1 ; Pages: 1-23 ; Basel: MDPI
- Klassifikation
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Wirtschaft
Econometric and Statistical Methods and Methodology: General
Econometric and Statistical Methods: Special Topics: General
Econometric Modeling: Other
Financial Markets and the Macroeconomy
- Thema
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expected shortfall
extreme value theory
generalized autoregressive score
generalized extreme value distribution
stock returns
time-varying
Value-at-Risk
- Ereignis
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Geistige Schöpfung
- (wer)
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Makatjane, Katleho
- Ereignis
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Veröffentlichung
- (wer)
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MDPI
- (wo)
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Basel
- (wann)
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2022
- DOI
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doi:10.3390/ijfs10010010
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:41 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Makatjane, Katleho
- MDPI
Entstanden
- 2022