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Titel:

Analyzing model robustness via distortion of the stochastic root: A Dirichlet prior approach

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Mai, J.-F.; Schenk, S.; Scherer, M.
Nicht-TUM Koautoren:
ja
Kooperation:
national
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...     »
Stichworte:
model robustness, model uncertainty, value-at-risk models, Dirichlet copula
Intellectual Contribution:
Discipline-based Research
Zeitschriftentitel:
Statistics & Risk Modeling
Jahr:
2016
Band / Volume:
32
Heft / Issue:
3-4
Seitenangaben Beitrag:
177–195
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1515/strm-2015-0009
Verlag / Institution:
Statistics & Risk Modeling
Print-ISSN:
2193-1402
E-ISSN:
2196-7040
Status:
Verlagsversion / published
TUM Einrichtung:
Lehrstuhl für Finanzmathematik
Urteilsbesprechung:
0
Key publication:
Nein
Peer reviewed:
Nein
International:
Ja
Book review:
Nein
commissioned:
not commissioned
Professional Journal:
Nein
Technology:
Nein
Interdisziplinarität:
Nein
Leitbild:
;
Ethics und Sustainability:
Nein
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