Dynamic Operational Risk: Modeling Dependence and Combining Different Sources of Information

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[1]  Paul Embrechts,et al.  Panjer recursion versus FFT for compound distributions , 2009, Math. Methods Oper. Res..

[2]  Jeremy E. Oakley,et al.  Uncertain Judgements: Eliciting Experts' Probabilities , 2006 .

[3]  Harry H. Panjer,et al.  Recursive Evaluation of a Family of Compound Distributions , 1981, ASTIN Bulletin.

[4]  Richard Warnung,et al.  A comparison of loss aggregation methods for operational risk , 2008 .

[5]  W. Gilks,et al.  Adaptive Rejection Metropolis Sampling Within Gibbs Sampling , 1995 .

[6]  By W. R. GILKSt,et al.  Adaptive Rejection Sampling for Gibbs Sampling , 2010 .

[7]  G. Parisi,et al.  Simulated tempering: a new Monte Carlo scheme , 1992, hep-lat/9205018.

[8]  Pavel V. Shevchenko,et al.  Computing Tails of Compound Distributions Using Direct Numerical Integration , 2009 .

[9]  Pavel V. Shevchenko.  Estimation of Operational Risk Capital Charge under Parameter Uncertainty , 2008 .

[10]  Marcelo Cruz,et al.  Operational Risk Modelling and Analysis: Theory and Practice , 2004 .

[11]  M. Bee.  Copula-Based Multivariate Models with Applications to Risk Management and Insurance , 2005 .

[12]  Michael K. Ong.  The Basel Handbook: A Guide for Financial Practitioners , 2003 .

[13]  Paul Embrechts,et al.  Aggregating operational risk across matrix structured loss data , 2008 .

[14]  Claudia Klüppelberg,et al.  Modelling and measuring multivariate operational risk with Lévy copulas , 2008 .

[15]  J. L. Nolan.  Stable Distributions. Models for Heavy Tailed Data , 2001 .

[16]  Radford M. Neal.  Sampling from multimodal distributions using tempered transitions , 1996, Stat. Comput..

[17]  Pavel V. Shevchenko,et al.  The Quantification of Operational Risk Using Internal Data, Relevant External Data and Expert Opinion , 2007 .

[18]  Paul Embrechts,et al.  For example, , 2022 .

[19]  Radford M. Neal,et al.  ANALYSIS OF A NONREVERSIBLE MARKOV CHAIN SAMPLER , 2000 .

[20]  Hoon Kim,et al.  Monte Carlo Statistical Methods , 2000, Technometrics.

[21]  L. Tierney,et al.  Efficiency and Convergence Properties of Slice Samplers , 2002 .

[22]  Thierry Roncalli,et al.  The Correlation Problem in Operational Risk , 2007 .

[23]  A. Doucet,et al.  Simulation of the annual loss distribution in operational risk via Panjer recursions and Volterra integral equations for value-at-risk and expected shortfall estimation , 2007 .

[24]  Diane Reynolds,et al.  Dependent Events and Operational Risk , 2002 .

[25]  Philippe Artzner,et al.  Coherent Measures of Risk , 1999 .

[26]  Christine M. Anderson-Cook,et al.  Book review: quantitative risk management: concepts, techniques and tools, revised edition, by A.F. McNeil, R. Frey and P. Embrechts. Princeton University Press, 2015, ISBN 978-0-691-16627-8, xix + 700 pp. , 2017, Extremes.

[27]  Pavel V. Shevchenko,et al.  Implementing Loss Distribution Approach for Operational Risk , 2009, 0904.1805.

[28]  P. V. Shevchenko,et al.  The Structural Modelling of Operational Risk Via Bayesian Inference: Combining Loss Data with Expert Opinions , 2006, 0904.1067.

[29]  Mario V. Wuthrich,et al.  A 'Toy' Model for Operational Risk Quantification Using Credibility Theory , 2007 .

[30]  P. Embrechts,et al.  Quantitative models for operational risk: Extremes, dependence and aggregation , 2006 .

[31]  Mario R. Melchiori.  Tools for Sampling Multivariate Archimedean Copulas , 2006 .

[32]  Carolyn Moclair,et al.  The structural modeling of operational risk via Bayesian inference: combining loss data with expert opinions , 2006 .

[33]  Michael Kalkbrener,et al.  LDA at work: Deutsche Bank's approach to quantifying operational risk , 2006 .

[34]  Svetlozar T. Rachev,et al.  Aggregation issues in operational risk , 2008 .

[35]  Mark J. Brewer,et al.  A comparison of hybrid strategies for Gibbs sampling in mixed graphical models , 1996 .

[36]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[37]  Paul Embrechts,et al.  Additivity properties for Value-at-Risk under Archimedean dependence and heavy-tailedness , 2009 .

[38]  Emiliano A. Valdez,et al.  Understanding Relationships Using Copulas , 1998 .

[39]  C. Geyer,et al.  Annealing Markov chain Monte Carlo with applications to ancestral inference , 1995 .

[40]  Thierry Roncalli,et al.  Loss Distribution Approach in Practice , 2007 .

[41]  A. McNeil,et al.  Common Poisson Shock Models: Applications to Insurance and Credit Risk Modelling , 2003, ASTIN Bulletin.

[42]  J. Rosenthal,et al.  On adaptive Markov chain Monte Carlo algorithms , 2005 .

[43]  Robert B. Gramacy,et al.  Importance tempering , 2007, Stat. Comput..