A Review of Methods for Combining Internal and External Data

In recent years, the occurrence of operational losses in financial institutions has increased the interest of academics and policy makers in operational risk. One of the main problems regarding the economic capital required to cover operational risk is the lack of sufficiently large databases. We present a set of models mixing internal and external data to predict both the severity and the frequency of operational losses. We show that, rather than a one-size-fits-all solution, there are several approaches, each presenting opportunities and limitations in the logical framework. Our findings offer useful insights for enhanced risk practice and prudential supervision.

[1]  W. Greene,et al.  Censored Data and Truncated Distributions , 2005 .

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

[3]  Richard T. Carson,et al.  Models for truncated counts , 1991 .

[4]  P. Schmidt,et al.  Limited-Dependent and Qualitative Variables in Econometrics. , 1984 .

[5]  William H. Greene,et al.  Estimation of limited dependent variable models by ordinary least squares and the method of moments , 1983 .

[6]  Shiferaw Gurmu,et al.  Tests for Detecting Overdispersion in the Positive Poisson Regression Model , 1991 .

[7]  J. Berg,et al.  An econometric model to scale operational losses , 2006 .

[8]  Marcelo Cruz.  Modeling, Measuring and Hedging Operational Risk , 2002 .

[9]  Robert A. Moffitt,et al.  The Uses of Tobit Analysis , 1980 .

[10]  C. Gourieroux,et al.  Pseudo Maximum Likelihood Methods: Applications to Poisson Models , 1984 .

[11]  Z. Griliches,et al.  Econometric Models for Count Data with an Application to the Patents-R&D Relationship , 1984 .

[12]  P. Trivedi,et al.  Overdispersion tests for truncated Poisson regression models , 1992 .

[13]  R. W. Wedderburn.  Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method , 1974 .

[14]  Thierry Roncalli,et al.  Loss Distribution Approach for Operational Risk , 2001 .

[15]  A. Cameron,et al.  Econometric models based on count data. Comparisons and applications of some estimators and tests , 1986 .

[16]  C. Gouriéroux,et al.  PSEUDO MAXIMUM LIKELIHOOD METHODS: THEORY , 1984 .

[17]  G. Dionne,et al.  What about Underevaluating Operational Value at Risk in the Banking Sector? , 2007 .

[18]  J. T. Wulu,et al.  Regression analysis of count data , 2002 .

[19]  Thierry Roncalli,et al.  How to Avoid Over-Estimating Capital Charge for Operational Risk? , 2007 .

[20]  Phhilippe Jorion.  Value at Risk: The New Benchmark for Managing Financial Risk , 2000 .

[21]  P. McCullagh.  Quasi-Likelihood Functions , 1983 .