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Bayesian Risk Aggregation: Correlation Uncertainty and Expert Judgement
the marginal distribution functions are as in Table 2.1. Now, observing that the inverses F−1i (·), ... BR F (x) = Φ [ x−µ σ ] , x ∈ R µ = 0, σ = 4.56 Table 2.1: Marginal distributions for market risk (MR) ...- Authors: Klaus Bocker
- Date: Jan 2011
- Competency: External Forces & Industry Knowledge>Actuarial theory in business context
- Topics: Enterprise Risk Management>Capital management - ERM; Finance & Investments>Economic capital; Modeling & Statistical Methods>Bayesian methods
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2007 Enterprise Risk Management Symposium: Multivariate Operational Risk: Dependence Modelling with Lévy Copulas
bank’s total operational risk is then given as S+(t) := S1(t) + S2(t) + · · ·+ Sd(t) , t ≥ 0 . (1.2) ... construct a d-dimensional compound Poisson process S = (S1, S2, . . . , Sd) with in general dependent ...- Authors: Klaus Bocker, Claudia Kluppelberg
- Date: Mar 2007
- Competency: External Forces & Industry Knowledge>Actuarial theory in business context
- Topics: Enterprise Risk Management>Operational risks
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Interaction of Market and Credit Risk: An Analysis of Inter-Risk Correlation and Risk Aggregation
the normal factor model (4.7). For this purpose, Table 4.1 as well as Figures 1 and 2 compare the inter-risk ... 37 (0.82) 0.22 (0.50) 0.27 (0.59) 0.33 (0.75) Table 4.1: LHP approximation for inter-risk correlation ...- Authors: Klaus Bocker, Martin Hillebrand
- Date: Apr 2008
- Competency: External Forces & Industry Knowledge>Actuarial theory in business context
- Topics: Economics>Financial markets; Enterprise Risk Management>Financial management