Artikel
Markov Chain Monte Carlo methods for estimating systemic risk allocations
In this paper, we propose a novel framework for estimating systemic risk measures and risk allocations based on Markov Chain Monte Carlo (MCMC) methods. We consider a class of allocations whose jth component can be written as some risk measure of the jth conditional marginal loss distribution given the so-called crisis event. By considering a crisis event as an intersection of linear constraints, this class of allocations covers, for example, conditional Value-at-Risk (CoVaR), conditional expected shortfall (CoES), VaR contributions, and range VaR (RVaR) contributions as special cases. For this class of allocations, analytical calculations are rarely available, and numerical computations based on Monte Carlo (MC) methods often provide inefficient estimates due to the rare-event character of the crisis events. We propose an MCMC estimator constructed from a sample path of a Markov chain whose stationary distribution is the conditional distribution given the crisis event. Efficient constructions of Markov chains, such as the Hamiltonian Monte Carlo and Gibbs sampler, are suggested and studied depending on the crisis event and the underlying loss distribution. The efficiency of the MCMC estimators is demonstrated in a series of numerical experiments.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Risks ; ISSN: 2227-9091 ; Volume: 8 ; Year: 2020 ; Issue: 1 ; Pages: 1-33 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
- Thema
-
capital allocation
conditional Value-at-Risk (CoVaR)
copula models
quantitative risk management
systemic risk measures
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Koike, Takaaki
Hofert, Marius
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2020
- DOI
-
doi:10.3390/risks8010006
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Koike, Takaaki
- Hofert, Marius
- MDPI
Entstanden
- 2020
Ähnliche Objekte (12)
Labor Allocations
Allocations Militaires
Newsprint Allocations
Markov Chain Monte Carlo Technology
Monte Carlo sampling processes and incentive compatible allocations in large economies
Requirements & Allocations Commerce
Proportional Borda allocations
Reversible Jump Markov Chain Monte Carlo
Proximal Markov chain Monte Carlo algorithms
Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models
Centrality-based capital allocations
Do permit allocations matter?
Labor Allocations
Allocations Militaires
Newsprint Allocations
Markov Chain Monte Carlo Technology
Monte Carlo sampling processes and incentive compatible allocations in large economies
Requirements & Allocations Commerce
Proportional Borda allocations
Reversible Jump Markov Chain Monte Carlo
Proximal Markov chain Monte Carlo algorithms
Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models
Centrality-based capital allocations
Do permit allocations matter?
Labor Allocations
Allocations Militaires
Newsprint Allocations
Markov Chain Monte Carlo Technology
Monte Carlo sampling processes and incentive compatible allocations in large economies
Requirements & Allocations Commerce
Proportional Borda allocations
Reversible Jump Markov Chain Monte Carlo
Proximal Markov chain Monte Carlo algorithms
Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models
Centrality-based capital allocations