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Pavel V. Shevchenko - One of the best experts on this subject based on the ideXlab platform.
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Should the Advanced Measurement Approach be Replaced with the Standardized Measurement Approach for Operational Risk
The Journal of Operational Risk, 2016Co-Authors: Gareth W. Peters, Pavel V. Shevchenko, Bertrand K. Hassani, Ariane ChapelleAbstract: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.
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a toy model for Operational Risk quantification using credibility theory
arXiv: Risk Management, 2009Co-Authors: Hans Buhlmann, Pavel V. Shevchenko, Mario V WuthrichAbstract:To meet the Basel II regulatory requirements for the Advanced Measurement Approaches in Operational Risk, the bank's internal model should make use of the internal data, relevant external data, scenario analysis and factors reflecting the business environment and internal control systems. One of the unresolved challenges in Operational Risk is combining of these data sources appropriately. In this paper we focus on quantification of the low frequency high impact losses exceeding some high threshold. We suggest a full credibility theory approach to estimate frequency and severity distributions of these losses by taking into account bank internal data, expert opinions and industry data.
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addressing the impact of data truncation and parameter uncertainty on Operational Risk estimates
Research Papers in Economics, 2009Co-Authors: Xiaolin Luo, Pavel V. Shevchenko, John B DonnellyAbstract:Typically, Operational Risk losses are reported above some threshold. This paper studies the impact of ignoring data truncation on the 0.999 quantile of the annual loss distribution for Operational Risk for a broad range of distribution parameters and truncation levels. Loss frequency and severity are modelled by the Poisson and Lognormal distributions respectively. Two cases of ignoring data truncation are studied: the "naive model" - fitting a Lognormal distribution with support on a positive semi-infinite interval, and "shifted model" - fitting a Lognormal distribution shifted to the truncation level. For all practical cases, the "naive model" leads to underestimation (that can be severe) of the 0.999 quantile. The "shifted model" overestimates the 0.999 quantile except some cases of small underestimation for large truncation levels. Conservative estimation of capital charge is usually acceptable and the use of the "shifted model" can be justified while the "naive model" should not be allowed. However, if parameter uncertainty is taken into account (in practice it is often ignored), the "shifted model" can lead to considerable underestimation of capital charge. This is demonstrated with a practical example.
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estimation of Operational Risk capital charge under parameter uncertainty
Social Science Research Network, 2008Co-Authors: Pavel V. ShevchenkoAbstract:Many banks adopt the Loss Distribution Approach to quantify the Operational Risk capital charge under Basel II requirements. It is common practice to estimate the capital charge using the 0.999 quantile of the annual loss distribution, calculated using point estimators of the frequency and severity distribution parameters. The uncertainty of the parameter estimates is typically ignored. One of the unpleasant consequences for the banks accounting for parameter uncertainty is an increase in the capital requirement. This paper demonstrates how the parameter uncertainty can be taken into account using a Bayesian framework that also allows for incorporation of expert opinions and external data into the estimation procedure.
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the quantification of Operational Risk using internal data relevant external data and expert opinion
2007Co-Authors: Dominik D Lambrigger, Pavel V. Shevchenko, Mario V WuthrichAbstract:To quantify an Operational Risk capital charge under Basel II, many banks adopt a Loss Distribution Approach. Under this approach, quantification of the frequency and severity distributions of Operational Risk involves the bank's internal data, expert opinions and relevant external data. In this paper we suggest a new approach, based on a Bayesian inference method, that allows for a combination of these three sources of information to estimate the parameters of the Risk frequency and severity distributions.
Murat Mazibas - One of the best experts on this subject based on the ideXlab platform.
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operasyonel Risk veritabani modellemesi Operational Risk database modelling
Social Science Research Network, 2006Co-Authors: Murat MazibasAbstract:Turkish Abstract: Operasyonel Risklerin yonetilebilmesi icin nitelikli ve analize uygun verilerin mevcut olmasi ve bu verilerin sistematik bir veri tabaninda toplanmasi onemli bir on kosuldur. Operasyonel Risk yonetim sisteminin kurulmasina yonelik calismalardaki en onemli asamalardan birisi de operasyonel Risk veri tabaninin olusturulmasidir. Operasyonel Risk veri tabaninin olusturulmasi surecindeki en temel bolum Riskin tanimlanmasi ve veri tabani mimarisinin tasarlanmasidir. Risk verilerinin belirlenmesi, bankanin maruz bulundugu Risklerin dikkatli bir sekilde analiz edilmesini gerektirmektedir. Bu surec, Risk etkenlerinin ve kaynaklarinin kapsamli taramasi ile baslamaktadir. Bu surecte, cevaplandirilmasi gereken en temel soru operasyonel Riskin ve operasyonel kayip olayinin ne oldugudur. Bir diger onemli gorev ise Risk verilerinin sahip olmasi gereken temel ozelliklerin tanimlanmasi, olcum yontemleri icin gerekli Risk verilerinin ve bu verilerin elde edilecegi kaynaklarin belirlenmesidir. Operasyonel Risk veri tabaninin dogru ve uygun bir temel uzerine insasi, veri tabaninin uzun yillar amaca hizmet edebilmesi icin onemlidir. Tum calismalarin ve veri tabaninin dogru ve uygun temel uzerine bina edilebilmesi icin planlama ve on hazirlik asamalari kapsamli bir sekilde ele alinmalidir. Bankanin ihtiyaclarina en uygun veri tabaninin tasarimini gerceklestirmek onemlidir. Bu cercevede, kavramsal, mantiksal ve fiziksel tasarim surecleri cok onemli hale gelmektedir. Uygulama sureci, veri tabaninin gercek performansinin test edilme ve varsa gerekli duzeltmelerin yapilmasina imkân saglayan bir surectir. Calismanin temel amaci, Risk verilerine ve veri tabani modellemesine iliskin temel bilgileri aktarmak suretiyle bankalarin bu konudaki calismalarina katkida bulunmaktir. Calisma, operasyonel Risk veri tabani sisteminin olusturulmasi esnasinda gerekli asgari duzeydeki bilgilerin aktarimina yonelik olarak bolumlere ayrilmistir. Bu amacla, ilk bolumde, operasyonel Risk verilerinin sahip olmasi gereken temel ozelliklerin neler oldugu, Basel-II’ de yer alan ileri olcum yontemleri ile bunlar icin gerekli veriler, operasyonel Risk veri cesitlerinin neler oldugu ve bunlarin nerelerden elde edilebilecegi konulari ele alinmistir. Ikinci bolumde, veri tabani modellemesine iliskin temel bilgilerin verilmesi amaciyla veri tabani modellemesi, veri modellemesi, veri tabani tasarimi gibi temel kavramlarla birlikte veri tabani sisteminin isleyis yapisi ve olusturulma sureci konulari ele alinmistir. Ucuncu bolumde, operasyonel Risk veri tabaninin olusturulmasi sureci planlama, kavramsal tasarim, mantiksal tasarim, fiziksel tasarim ve uygulamadan olusan bes asamada ele alinmistir. English Abstract: For Operational Risk management, the existence of qualified and analytically tractable data, and collection of this data into a systematic database is a mandate. An important phase in studies on developing an Operational Risk management system is the construction of an Operational Risk database. In the development process of an Operational Risk database, the most fundamental parts are defining the Risk data and designing the architecture of the database. Defining the Risk data requires careful analysis of Risks that bank is exposed to. This process begins with a comprehensive search for elements and sources of Risks. In this process, the most fundamental question to be answered is what is the Operational Risk and Operational loss. Another central task is defining the basic properties and qualities of data, determining the data needs of measurement methodologies, identifying types and sources of data. For an Operational Risk database, a proper foundation is crucial to ensure that the structure is built to last. Therefore, each step in the construction has to be handled with care. In this manner, in order to begin with a proper foundation, planning and preparation have to be comprehensive. Designing a database architecture best fitting the bank’s needs is crucial. Thereof, the conceptual, logical and physical design phases are immensely important. The implementation phase is the phase where actual performance of the database is tested. Through giving essential information on Operational Risk data and database modeling, the main aim of this paper is to contribute to the banks’ efforts on this topic. In this manner, this paper is organized for providing the most essential and if necessary detailed technical knowledge on developing an Operational Risk loss database. In the first part, it is discussed that what are the fundamental properties of Operational Risk data to be eligible for inclusion in the database, what are the data types and data requirements of Basel-II Operational Risk advanced measurement approaches, and how and from where shall these data be obtained. In the second part, basic information about the building of a database (database, database modeling, database system etc.) is given. The third part is devoted to the development of an Operational Risk database and in this part; stages of planning, conceptual design, logical design, physical design and implementation are discussed.
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turk bankacilik sisteminde operasyonel Risk veri tabaninin olusturulmasi developing an Operational Risk database in turkish banking system
Social Science Research Network, 2006Co-Authors: Murat MazibasAbstract:Turkish Abstract: Turk bankacilik sisteminde, operasyonel Risk yonetimi sistemlerinin gelistirilmesine yonelik calismalarda onemli bircok sorunla karsi karsiya kalinmaktadir. Bu sorunlarin buyuk cogunlugu, mevcut Risk yonetimi uygulamalari ile modern Risk yonetiminin temel unsurlari arasindaki mesafeden kaynaklanmaktadir. Modern operasyonel Risk yonetimi, bankalarin herhangi bir Risk yonetimi tedbiri almadan veya Risk yonetimi faaliyeti gerceklestirmeden evvel Risklerini tanimlamalarini, sayisallastirmalarini ve olcmelerini zorunlu gormektedir. Operasyonel Risk yonetimi sisteminin olusturulmasina yonelik calismalar, teknik konularda yapilacak calismalarin yaninda, Risk yonetimini bankada icsellestirecek faaliyetlerle baslamalidir. Risk yonetiminin icsellestirilmesi banka yonetimi hayati bir rol ustlenmektedir. Bu nedenle, banka yonetimi Risk yonetimine sahip cikmali, banka calisanlarini ve ilgili kesimleri Risk yonetiminin modern bankacilik yonetiminin vazgecilmez bir unsuru oldugu konusunda ikna etmeli ve bunu temin etmelidir. Operasyonel Risk veri tabani, operasyonel Risk olcumu ve yonetimi sureclerinin vazgecilmez bir parcasidir. Uygulama kapsami ve diger Risklere iliskin veri tabanlari ile arasindaki farkliliklar nedeniyle operasyonel Risk veri tabanina ozel bir onem verilmesi gerekmektedir. Calismada, operasyonel Risk veri tabani konusu Turk bankalarinin karsi karsiya bulunduklari sorunlar goz onunde bulundurularak ele alinmaktadir. Bu kapsamda, operasyonel Risk veri tabani sisteminin olusturulmasi esnasinda en gerekli gorulen ve gerektiginde detaylandirilmis olarak; genel hususlara, yol gosterici nitelikteki standartlara, veri tabaninin olusturulmasi surecinde banka icindeki fonksiyonlarla gorev ve sorumluluk dagilimina, veri tabani projesi esnasinda dikkat edilmesi gereken konular ile operasyonel Risk verilerinden kaynaklanan sorunlar ve bu sorunlarin ne sekilde asilabilecegine dair degerlendirmelere yer verilmektedir. English Abstract: In Turkish banking system, efforts on developing Operational Risk management systems face significant challenges. These challenges take their sources from the extent of current practices from the fundamentals of a modern Risk management system. Modern Operational Risk management requires banks identify, quantify and measure Risks before taking any Risk management measures. Efforts on establishing an Operational Risk management system along with technical studies should start with activities on internalizing the Risk management. In the process of internalizing Risk management, bank management has a vital role. Therefore, bank management should take Risk management issues seriously, convince and assure people that Risk management is an indispensable part of modern bank management. Operational Risk database is a necessary part of Operational Risk measurement and management process. Depending on its scope of application and dissimilarities with other Risk databases, it requires careful attention. In this paper, Operational Risk database issue is discussed with taking special consideration on the challenges Turkish banks have to confront with. In this manner, the paper is organized for providing the most essential and, if necessary, detailed knowledge on guiding standards on data collection; functions, tasks, and responsibilities in database development process; database development project; potential problems and suggestions on their solutions.
Jeanphilippe Peters - One of the best experts on this subject based on the ideXlab platform.
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practical methods for measuring and managing Operational Risk in the financial sector a clinical study
Journal of Banking and Finance, 2008Co-Authors: Ariane Chapelle, Yves Crama, Georges Hubner, Jeanphilippe PetersAbstract:Abstract This paper analyzes the implications of the advanced measurement approach (AMA) for the assessment of Operational Risk. Through a clinical case study on a matrix of two selected business lines and two event types of a large financial institution, we develop a procedure that addresses the major issues faced by banks in the implementation of the AMA. For each cell, we calibrate two truncated distributions functions, one for “normal” losses and the other for the “extreme” losses. In addition, we propose a method to include external data in the framework. We then estimate the impact of Operational Risk management on bank profitability, through an adapted measure of RAROC. The results suggest that substantial savings can be achieved through active management techniques.
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basle ii and Operational Risk implications for Risk measurement and management in the financial sector
Social Science Research Network, 2004Co-Authors: Ariane Chapelle, Yves Crama, Georges Hubner, Jeanphilippe PetersAbstract:This paper proposes a methodology to analyze the implications of the Advanced Measurement Approach (AMA) for the assessment of Operational Risk put forward by the Basel II Accord. The methodology relies on an integrated procedure for the construction of the distribution of aggregate losses, using internal and external loss data. It is illustrated on a 2x2 matrix of two selected business lines and two event types, drawn from a database of 3000 losses obtained from a large European banking institution. For each cell, the method calibrates three truncated distributions functions for the body of internal data, the tail of internal data, and external data. When the dependence structure between aggregate losses and the non-linear adjustment of external data are explicitly taken into account, the regulatory capital computed with the AMA method proves to be substantially lower than with less sophisticated approaches allowed by the Basel II Accord, although the effect is not uniform for all business lines and event types. In a second phase, our models are used to estimate the effects of Operational Risk management actions on bank profitability, through a measure of RAROC adapted to Operational Risk. The results suggest that substantial savings can be achieved through active management techniques, although the estimated effect of a reduction of the number, frequency or severity of Operational losses crucially depends on the calibration of the aggregate loss distributions.
Ariane Chapelle - One of the best experts on this subject based on the ideXlab platform.
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Should the advanced measurement approach be replaced with the standardized measurement approach for Operational Risk?
2016Co-Authors: Gareth Peters, Pavel Shevchenko, Bertrand Hassani, Ariane ChapelleAbstract:Recently, Basel Committee for 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.
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Should the Advanced Measurement Approach be Replaced with the Standardized Measurement Approach for Operational Risk
The Journal of Operational Risk, 2016Co-Authors: Gareth W. Peters, Pavel V. Shevchenko, Bertrand K. Hassani, Ariane ChapelleAbstract: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.
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practical methods for measuring and managing Operational Risk in the financial sector a clinical study
Journal of Banking and Finance, 2008Co-Authors: Ariane Chapelle, Yves Crama, Georges Hubner, Jeanphilippe PetersAbstract:Abstract This paper analyzes the implications of the advanced measurement approach (AMA) for the assessment of Operational Risk. Through a clinical case study on a matrix of two selected business lines and two event types of a large financial institution, we develop a procedure that addresses the major issues faced by banks in the implementation of the AMA. For each cell, we calibrate two truncated distributions functions, one for “normal” losses and the other for the “extreme” losses. In addition, we propose a method to include external data in the framework. We then estimate the impact of Operational Risk management on bank profitability, through an adapted measure of RAROC. The results suggest that substantial savings can be achieved through active management techniques.
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basle ii and Operational Risk implications for Risk measurement and management in the financial sector
Social Science Research Network, 2004Co-Authors: Ariane Chapelle, Yves Crama, Georges Hubner, Jeanphilippe PetersAbstract:This paper proposes a methodology to analyze the implications of the Advanced Measurement Approach (AMA) for the assessment of Operational Risk put forward by the Basel II Accord. The methodology relies on an integrated procedure for the construction of the distribution of aggregate losses, using internal and external loss data. It is illustrated on a 2x2 matrix of two selected business lines and two event types, drawn from a database of 3000 losses obtained from a large European banking institution. For each cell, the method calibrates three truncated distributions functions for the body of internal data, the tail of internal data, and external data. When the dependence structure between aggregate losses and the non-linear adjustment of external data are explicitly taken into account, the regulatory capital computed with the AMA method proves to be substantially lower than with less sophisticated approaches allowed by the Basel II Accord, although the effect is not uniform for all business lines and event types. In a second phase, our models are used to estimate the effects of Operational Risk management actions on bank profitability, through a measure of RAROC adapted to Operational Risk. The results suggest that substantial savings can be achieved through active management techniques, although the estimated effect of a reduction of the number, frequency or severity of Operational losses crucially depends on the calibration of the aggregate loss distributions.
Paolo Giudici - One of the best experts on this subject based on the ideXlab platform.
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a bayesian approach to estimate the marginal loss distributions in Operational Risk management
Computational Statistics & Data Analysis, 2008Co-Authors: Dalla L Valle, Paolo GiudiciAbstract:One of the main problems in Operational Risk management is the lack of loss data, which affects the parameter estimates of the marginal distributions of the losses. The principal reason is that financial institutions only started to collect Operational loss data a few years ago, due to the relatively recent definition of this type of Risk. Considering this drawback, the employment of Bayesian methods and simulation tools could be a natural solution to the problem. The use of Bayesian methods allows us to integrate the scarce and, sometimes, inaccurate quantitative data collected by the bank with prior information provided by experts. An original proposal is a Bayesian approach for modelling Operational Risk and for calculating the capital required to cover the estimated Risks. Besides this methodological innovation a computational scheme, based on Markov chain Monte Carlo simulations, is required. In particular, the application of the MCMC method to estimate the parameters of the marginals shows advantages in terms of a reduction of capital charge according to different choices of the marginal loss distributions.
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integration of qualitative and quantitative Operational Risk data a bayesian approach
2004Co-Authors: Paolo GiudiciAbstract:The aim of this chapter is to provide a Bayesian model that allows us to manage Operational Risk and measure internally the capital requirement, compliant with the Advanced Measurement Approaches (AMA) recommended by Basel Committee on Banking Supervision (Basel II) for internationally active banks (see, eg, Basel Committee on Banking Supervision, 2003). In general, the objective is to estimate a loss distribution and to derive functions of interest from it (such as the value-at-Risk, or VAR). More precisely, losses in Operational Risk are realisations of a convolution between a counting process (frequency) and a number of continuous ones (severities). For a review of statistical models used in Operational Risk management see, eg, Cruz (2002) and Cornalba and Giudici (2004). A general problem for such models is the lack of appropriate historical databases, which makes difficult to apply statistical inference techniques to “squeeze” in a correct manner information to check the tail of loss distribution. Bayesian networks offer a solution to this problem, combining in a coherent way qualitative and quantitative data, as well as Risk indicators and external databases. Indeed such an approach seems to well reflect the requirements of the AMA to measuring Operational Risk. Consider the following quotation from the 2001 working paper on the regulatory 6