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Forecasting value at risk and expected shortfall using a semiparametric approach based on the asymmetric laplace distribution

Abstract:

Value at Risk (VaR) forecasts can be produced from conditional autoregressive VaR models, estimated using quantile regression. Quantile modeling avoids a distributional assumption, and allows the dynamics of the quantiles to differ for each probability level. However, by focusing on a quantile, these models provide no information regarding Expected Shortfall (ES), which is the expectation of the exceedances beyond the quantile. We introduce a method for predicting ES corresponding to VaR fore...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1080/07350015.2017.1281815

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Institution:
University of Oxford
Oxford college:
St Cross College
Role:
Author
Publisher:
Taylor and Francis
Journal:
Journal of Business and Economic Statistics More from this journal
Volume:
37
Issue:
1
Pages:
121-133
Publication date:
2017-05-19
Acceptance date:
2016-12-31
DOI:
EISSN:
1537-2707
ISSN:
0735-0015
Keywords:
Pubs id:
pubs:667991
UUID:
uuid:0ac756b0-214e-40f0-acbf-3effeb65ab54
Local pid:
pubs:667991
Source identifiers:
667991
Deposit date:
2017-01-04

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