Upper risk bounds in internal factor models with constrained specification sets View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-05-19

AUTHORS

Jonathan Ansari, Ludger Rüschendorf

ABSTRACT

For the class of (partially specified) internal risk factor models we establish strongly simplified supermodular ordering results in comparison to the case of general risk factor models. This allows us to derive meaningful and improved risk bounds for the joint portfolio in risk factor models with dependence information given by constrained specification sets for the copulas of the risk components and the systemic risk factor. The proof of our main comparison result is not standard. It is based on grid copula approximation of upper products of copulas and on the theory of mass transfers. An application to real market data shows considerable improvement over the standard method. More... »

PAGES

3

References to SciGraph publications

  • 2017-05-10. Risk bounds for factor models in FINANCE AND STOCHASTICS
  • 2017-10-29. Risk Bounds and Partial Dependence Information in FROM STATISTICS TO MATHEMATICAL FINANCE
  • 2013. Mathematical Risk Analysis, Dependence, Risk Bounds, Optimal Allocations and Portfolios in NONE
  • 2013-05-14. Duality Theory and Transfers for Stochastic Order Relations in STOCHASTIC ORDERS IN RELIABILITY AND RISK
  • 2015-09-17. Aggregation-robustness and model uncertainty of regulatory risk measures in FINANCE AND STOCHASTICS
  • 2007. Stochastic Orders in NONE
  • 2017-01-03. Ordering Results for Risk Bounds and Cost-efficient Payoffs in Partially Specified Risk Factor Models in METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY
  • 2006-04-25. Bounds for Functions of Dependent Risks in FINANCE AND STOCHASTICS
  • 2017-10-14. Improved Hoeffding–Fréchet bounds and applications to VaR estimates in COPULAS AND DEPENDENCE MODELS WITH APPLICATIONS
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    URI

    http://scigraph.springernature.com/pub.10.1186/s41546-020-00045-y

    DOI

    http://dx.doi.org/10.1186/s41546-020-00045-y

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    https://app.dimensions.ai/details/publication/pub.1127695960


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