Should the Advanced Measurement Approach be Replaced with the Standardized Measurement Approach for Operational Risk?

Recently, Basel Committee for Banking Supervision proposed to replace all approaches, including Advanced Measurement Approach (AMA), for operational risk capital with a simple formula referred to as the Standardised Measurement Approach (SMA). This paper discusses and studies the weaknesses and pitfalls of SMA such as instability, risk insensitivity, super-additivity and the implicit relationship between SMA capital model and systemic risk in the banking sector. We also discuss the issues with closely related operational risk Capital-at-Risk (OpCar) Basel Committee proposed model which is the precursor to the SMA. In conclusion, we advocate to maintain the AMA internal model framework and suggest as an alternative a number of standardization recommendations that could be considered to unify internal modelling of operational risk. The findings and views presented in this paper have been discussed with and supported by many OpRisk practitioners and academics in Australia, Europe, UK and USA, and recently at OpRisk Europe 2016 conference in London.

[1]  R. Rigby,et al.  Generalized Additive Models for Location Scale and Shape (GAMLSS) in R , 2007 .

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

[3]  Bertrand K. Hassani,et al.  Using a time series approach to correct serial correlation in Operational Risk capital calculation , 2012 .

[4]  G. Crockford.  The Bibliography and History of Risk Management: Some Preliminary Observations , 1982 .

[5]  Rodrigo S. Targino,et al.  Understanding Operational Risk Capital Approximations: First and Second Orders , 2013, 1303.2910.

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

[7]  Paul Embrechts,et al.  An Extreme Value Approach for Modeling Operational Risk Losses Depending on Covariates , 2016 .

[8]  Amandha Ganegoda,et al.  A scaling model for severity of operational losses using generalized additive models for location scale and shape (GAMLSS) , 2012, Annals of Actuarial Science.

[9]  Gareth W. Peters,et al.  Advances in Heavy Tailed Risk Modeling: A Handbook of Operational Risk , 2015 .

[10]  Benoit Genest.  Comments on the 'Standardised Measurement Approach' for Operational Risk , 2016 .

[11]  Gareth W. Peters,et al.  Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk , 2015 .

[12]  Pavel V. Shevchenko,et al.  Modelling Operational Risk Using Bayesian Inference , 2011 .

[13]  Richard Gerlach,et al.  Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-Moments , 2016 .

[14]  Carolyn Moclair.  Dynamic operational risk: modeling dependence and combining different sources of information , 2009 .

[15]  G. Peters,et al.  RISK MARGIN QUANTILE FUNCTION VIA PARAMETRIC AND NON-PARAMETRIC BAYESIAN APPROACHES , 2015, ASTIN Bulletin.