Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257829 
Year of Publication: 
2022
Citation: 
[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
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
expected shortfall
extreme value theory
generalized autoregressive score
generalized extreme value distribution
stock returns
time-varying
Value-at-Risk
JEL: 
C10
C40
C59
E44
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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