Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/257829 
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Erscheinungsjahr: 
2022
Quellenangabe: 
[Journal:] International Journal of Financial Studies [ISSN:] 2227-7072 [Volume:] 10 [Issue:] 1 [Article No.:] 10 [Publisher:] MDPI [Place:] Basel [Year:] 2022 [Pages:] 1-23
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
expected shortfall
extreme value theory
generalized autoregressive score
generalized extreme value distribution
stock returns
time-varying
Value-at-Risk
JEL: 
C10
C40
C59
E44
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