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~isPartOf:"Applied economics letters"
~isPartOf:"Economic modelling"
~isPartOf:"Journal of forecasting"
~isPartOf:"The journal of operational risk"
~subject:"Statistical distribution"
~subject:"Systemrisiko"
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TWO-COMPONENT EXTREME VALUE DI...
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Statistical distribution
Systemrisiko
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Applied economics letters
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Journal of forecasting
The journal of operational risk
Insurance / Mathematics & economics
78
Journal of banking & finance
45
Finance research letters
37
Risks : open access journal
30
Discussion paper / Tinbergen Institute
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International journal of forecasting
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International review of financial analysis
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SFB 649 discussion paper
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Journal of business & economic statistics : JBES ; a publication of the American Statistical Association
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Journal of risk management in financial institutions
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ECONIS (ZBW)
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1
Bayesian operational risk models
Figini, Silvia
;
Gao, Lijun
;
Giudici, Paolo
- In:
The journal of operational risk
10
(
2015
)
2
,
pp. 45-60
Persistent link: https://www.econbiz.de/10011298859
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2
Approximations of value-at-risk as an extreme quantile of a random sum of heavy-tailed random variables
Hannah, Lincoln
;
Puza, Borek
- In:
The journal of operational risk
10
(
2015
)
2
,
pp. 1-21
Persistent link: https://www.econbiz.de/10011298869
Saved in:
3
Testing for Granger causality in distribution tails : an application to oil markets integration
Candelon, Bertrand
;
Joëts, Marc
;
Tokpavi, Sessi
- In:
Economic modelling
31
(
2013
),
pp. 276-285
Persistent link: https://www.econbiz.de/10009729103
Saved in:
4
Testing the Gaussian and Student's t copulas in a risk management framework
Lourme, Alexandre
;
Maurer, Frantz
- In:
Economic modelling
67
(
2017
),
pp. 203-214
Persistent link: https://www.econbiz.de/10011813813
Saved in:
5
Modeling multivariate operational losses via copula-based distributions with g-and-h marginals
Bee, Marco
;
Hambuckers, Julien
- In:
The journal of operational risk
17
(
2022
)
1
,
pp. 81-111
Persistent link: https://www.econbiz.de/10014546257
Saved in:
6
Improving the accuracy of tail risk forecasting models by combining several realized volatility estimators
Naimoli, Antonio
;
Gerlach, Richard
;
Storti, Giuseppe
- In:
Economic modelling
107
(
2022
),
pp. 1-19
Persistent link: https://www.econbiz.de/10013367470
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7
Do realized higher moments have information content? : VaR forecasting based on the realized GARCH-RSRK model
Wang, Tianyi
;
Liang, Fang
;
Huang, Zhuo
;
Yan, Hong
- In:
Economic modelling
109
(
2022
),
pp. 1-13
Persistent link: https://www.econbiz.de/10013348237
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8
A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting
Wang, Chao
;
Gerlach, Richard
- In:
Journal of forecasting
43
(
2024
)
1
,
pp. 40-57
Persistent link: https://www.econbiz.de/10014443184
Saved in:
9
Modeling uncertainty in financial tail risk : a forecast combination and weighted quantile approach
Storti, Giuseppe
;
Wang, Chao
- In:
Journal of forecasting
42
(
2023
)
7
,
pp. 1648-1663
Persistent link: https://www.econbiz.de/10014432743
Saved in:
10
Forecasting volatility and value-at-risk for cryptocurrency using GARCH-type models : the role of the probability distribution
Chen, Qihao
;
Huang, Zhuo
;
Liang, Fang
- In:
Applied economics letters
31
(
2024
)
18
,
pp. 1907-1914
Persistent link: https://www.econbiz.de/10015084570
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