Sources of uncertainty in modeling operational risk losses

Operational risk quantification techniques have been rapidly evolving since the first attempts in early 2000, when it appeared clear that this kind of risk would attract a specific capital requirement in the new prudential regulation. The basic component of most models developed by the industry is historical (or scenario) loss data. The modeling techniques used to obtain the risk measures are generally well developed and understood. In this work, assuming a simple but rigorous modeling framework, containing the basic features of the models which are generally observed in the industry, focus will be placed on the uncertainty of the model outputs. The sources of this uncertainty will be analyzed in a systematic way.

[1]  Lena Jaeger,et al.  Loss Models: From Data to Decisions , 2006 .

[2]  Søren Asmussen,et al.  Ruin probabilities , 2001, Advanced series on statistical science and applied probability.

[3]  Stuart A. Klugman,et al.  Loss Models: From Data to Decisions , 1998 .

[4]  Paul Embrechts,et al.  Infinite-mean models and the LDA for operational risk , 2006 .

[5]  Markus Junker,et al.  Estimating the tail-dependence coefficient: Properties and pitfalls , 2005 .

[6]  W. David Kelton,et al.  Quantile and histogram estimation , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[7]  Paul Embrechts,et al.  QUANTIFYING REGULATORY CAPITAL FOR OPERATIONAL RISK , 2003 .

[8]  Cynthia C. Lichtenstein,et al.  Bank for International Settlements: Committee on Banking Regulations and Supervisory Practices’ Consultative Paper on International Convergence of Capital Measurement and Capital Standards , 1991, International Legal Materials.

[9]  Ralph B. D'Agostino,et al.  Goodness-of-Fit-Techniques , 2020 .

[10]  Paul Embrechts,et al.  S.A. Klugman, H.H. Panjer and G.E. Willmot (1998): Loss Models: From Data to Decisions. Wiley, New York , 1998, ASTIN Bulletin.

[11]  Claudia Klüppelberg,et al.  Operational VaR: a closed-form approximation , 2005 .

[12]  Adrian E. Raftery,et al.  Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors , 1999 .

[13]  R. Grübel,et al.  Computation of Compound Distributions I: Aliasing Errors and Exponential Tilting , 1999 .

[14]  Rudolf Grübel,et al.  Computation of Compound Distributions II: Discretization Errors and Richardson Extrapolation , 2000, ASTIN Bulletin.

[15]  A. McNeil,et al.  The Peaks over Thresholds Method for Estimating High Quantiles of Loss Distributions , 1998 .

[16]  F. James Statistical Methods in Experimental Physics , 1973 .

[17]  G. Schwarz Estimating the Dimension of a Model , 1978 .