A Piecewise-Defined Severity Distribution-Based Loss Distribution Approach to Estimate Operational Risk: Evidence from Chinese National Commercial Banks

Following the Basel II Accord, with the increased focus on operational risk as an aspect distinct from credit and market risk, quantification of operational risk has been a major challenge for banks. This paper analyzes implications of the advanced measurement approach to estimate the operational risk. When modeling the severity of losses in a realistic manner, our preliminary tests indicate that classic distributions are unable to fit the entire range of operational risk data samples (collected from public information sources) well. Then, we propose a piecewise-defined severity distribution (PSD) that combines a parameter form for ordinary losses and a generalized Pareto distribution (GPD) for large losses, and estimate operational risk by the loss distribution approach (LDA) with Monte Carlo simulation. We compare the operational risk measured with piecewise-defined severity distribution based LDA (PSD-LDA) with those obtained from the basic indicator approach (BIA), and the ratios of operational risk regulatory capital of some major international banks with those of Chinese commercial banks. The empirical results reveal the rationality and promise of application of the PSD-LDA for Chinese national commercial banks.

[1]  Paul Embrechts,et al.  The Quantitative Modeling of Operational Risk: Between G-and-H and EVT , 2007, ASTIN Bulletin.

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

[3]  L. Kalyvas,et al.  DOES THE APPLICATION OF INNOVATIVE INTERNAL MODELS DIMINISH REGULATORY CAPITAL , 2006 .

[4]  Vivek Trikha,et al.  Clinical Study , 1961, Acta neurologica Scandinavica.

[5]  J. Rosenberg,et al.  A General Approach to Integrated Risk Management with Skewed, Fat-Tailed Risk , 2004 .

[6]  Kabir K. Dutta,et al.  A Tale of Tails: An Empirical Analysis of Loss Distribution Models for Estimating Operational Risk Capital , 2006 .

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

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

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

[10]  Paul Embrechts,et al.  Smooth Extremal Models in Finance and Insurance , 2004 .

[11]  Atsutoshi Mori,et al.  The Effect of the Choice of the Loss Severity Distribution and the Parameter Estimation Method on Operational Risk Measurement , 2007 .

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

[13]  Eric S. Rosengren,et al.  Capital and Risk: New Evidence on Implications of Large Operational Losses , 2003 .

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

[15]  Paul Embrechts,et al.  AGGREGATING RISK ACROSS MATRIX STRUCTURED LOSS DATA: THE CASE OF OPERATIONAL RISK , 2007 .

[16]  Eric S. Rosengren,et al.  Using Loss Data to Quantify Operational Risk , 2003 .

[17]  Fred W. Glover,et al.  Simulation Optimization: Applications in Risk Management , 2008, Int. J. Inf. Technol. Decis. Mak..

[18]  C. Klüppelberg,et al.  Modelling Extremal Events , 1997 .

[19]  Ellen Leander Davis,et al.  Operational Risk: Practical Approaches to Implementation , 2005 .

[20]  Lev V. Utkin,et al.  Risk Analysis under Partial Prior Information and Nonmonotone Utility Functions , 2007, Int. J. Inf. Technol. Decis. Mak..

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

[22]  Chiara Cornalba,et al.  Statistical models for operational risk management , 2004 .

[23]  Paul Embrechts,et al.  Aggregating risk capital, with an application to operational risk , 2006 .

[24]  Y. Crama,et al.  Practical methods for measuring and managing operational risk in the financial sector: a clinical study , 2008 .