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Document type:
Zeitschriftenaufsatz 
Author(s):
Mai, J.-F.; Schenk, S.; Scherer, M. 
Non-TUM Co-author(s):
ja 
Cooperation:
national 
Title:
Analyzing model robustness via distortion of the stochastic root: A Dirichlet prior approach 
Abstract:
It is standard in quantitative risk management to model a random vector X of consecutive log-returns in order to ultimately analyze the probability law of the accumulated return. By the Markov regression representation, see [Rüschendorf, de Valk (1993)], any model for X can be decomposed into a vector U of i.i.d. random variables - accounting for the randomness in the model - and a multivariate function f - representing the economic reasoning behind. For most models, f is known explicitly and U...    »
 
Keywords:
model robustness, model uncertainty, value-at-risk models, Dirichlet copula 
Intellectual Contribution:
Discipline-based Research 
Journal title:
Statistics & Risk Modeling 
Year:
2016 
Journal volume:
32 
Journal issue:
3-4 
Pages contribution:
177–195 
Reviewed:
ja 
Language:
en 
Publisher:
Statistics & Risk Modeling 
Print-ISSN:
2193-1402 
E-ISSN:
2196-7040 
Status:
Verlagsversion / published 
TUM Institution:
Lehrstuhl für Finanzmathematik 
Key publication:
Nein 
Peer reviewed:
Nein 
International:
Ja 
Book review:
Nein 
Commissioned:
not commissioned 
Professional Journal:
Nein 
Mission statement:
versions