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Data and uncertainty in extreme risks: a nonlinear expectations approach

Abstract:

Estimation of tail quantities, such as expected shortfall or Value at Risk, is a difficult problem. We show how the theory of nonlinear expectations, in particular the Data-robust expectation introduced in [5], can assist in the quantification of statistical uncertainty for these problems. However, when we are in a heavy-tailed context (in particular when our data are described by a Pareto distribution, as is common in much of extreme value theory), the theory of [5] is insufficient, and requ...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1142/9789813272569_0006

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Mathematical Institute
Oxford college:
Wadham College
ORCID:
0000-0003-0539-6414

Contributors

Role:
Editor
Role:
Editor
Role:
Editor
Role:
Editor
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Editor
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Publisher:
World Scientific Publishing Publisher's website
Pages:
135–162
Publication date:
2018-11-01
Acceptance date:
2018-02-16
DOI:
Pubs id:
pubs:826185
URN:
uri:557b5ad5-6819-421b-87ff-0fff36e039a5
UUID:
uuid:557b5ad5-6819-421b-87ff-0fff36e039a5
Local pid:
pubs:826185
ISBN:
978-981-3272-55-2

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