Evaluating Operational Risk Exposure Using Fuzzy umber Approach to Scenario Analysis

Antonina Durfee, A. Tselykh
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The Bank for International Settlements, an interna-tional organization which fosters international monetary and financial cooperation and serves as a bank for cen-tral banks, had realized that developing banking prac-tices such as securitization, outsourcing, specialized processing operations and reliance on technology had introduced substantial risks which need to be reflected in credible capital assessments. As a result, Basel II Ac-cord that includes risks other than credit and market was proposed to be “a comprehensive framework for improving bank safety and soundness by more closely linking regulatory capital requirements with bank risk, by improving the ability of supervisors and financial markets to assess capital adequacy”, according to Fed-eral Reserve Chairman Ben S. Bernanke in a May 2006 speech at the Federal Reserve Bank of Chicago’s 42nd Annual Conference on Bank Structure and Competition. All the largest banks in the world that carry internation-al component are mandated to become Basel certified. Resulting international risk management practice for financial institutions focuses on three main risk catego-ries: Market Risk, Credit Risk and Operational Risk (OR). OR is a relatively newly defined category of risk which includes many different types of risk, from the simple operations risks of transaction processing, unau-thorized activities and systems risks to other types of risk that are not included in market or credit risks, such as human, legal, informational and reputational risks with the exception of strategic risk [2]. By contrast with credit and market risks where acceptable models and measurements exist, it is relatively difficult to identify and evaluate levels of operational risk due to the com-plexity of its origin, lack of existing historic quantita-tive data and explanation. One of the best publicized OR risk events are rouge trading in the French Societe Generale bank, BP oil spill, and American Maddoff “ponzi” scheme causing billions of dollars to a global financial system. Scenario analysis as a decision making tool has been in existence for several decades and has been used in various disciplines, including management, engineer-ing, defense, medicine, finance and economics [6]. Sce-nario analysis is one of the four required data elements that banks must incorporate into their Advanced Model-ing Approach to adequately measure their operational risk levels. Scenario analysis brings a forward looking element into OR assessment since it describes future plausible severe OR loss events. The current range of practice published in 2009 by Basel Committee of Banking Supervision [16] identifies a lack of consistent controls to address scenario analysis bias noting that scenario analysis is subjective in nature. Chosen and estimated scenarios become an effective technique for stimulating management to identify key strategic opera-tional risks facing their businesses. Scenario workshops are used as the preferred method to develop a set of scenarios. During scenario workshop, experts are sup-plied with a library of plausible extreme operational events that have or may have happened in peer financial organization and are asked to assess the likelihood and severity of similar events if they occur in their business, as described by the Bank of Japan [25]. Scenarios as a framework offer the ability to bring qualitative business intuition data into a systematic assessment of OR which is increasingly important in an era of stress testing of international financial sector. Making point estimates in","PeriodicalId":403191,"journal":{"name":"EUSFLAT Conf.","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EUSFLAT Conf.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2991/eusflat.2011.55","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

Abstract Risks and losses arising from system failure, unautho-rized activity, fraud and other operational errors are postulated to be one of the primary banking risks. Ex-pert-based scenario analysis aims at describing the fre-quency and severity of extreme operational losses. Ex-perts are not exact in predicting quantitative estimates for a distant future. The predicted scenarios are de-scribed by range estimates and qualitative storylines. This study introduces and applies the methodology for evaluation of the operational risk levels derived from the experts opinions collected from scenarios. The me-thodology is based on the use of fuzzy numbers to ex-press subjective probability of expert estimates. Keywords : operational risk, scenario analysis, fuzzy decision making, fuzzy number 1. Introduction In the light of the global financial crisis, both media, leading policy makers, economists and practitioners are questioning the appropriate levels of risk in the interna-tional financial system. Decision makers and banking practitioners are struggling to evaluate the risk attri-buted to the globalization of modern banking, the fast evolving information technology, new economic theo-ries, opaque financial instruments, and complex ma-thematical modeling. International banking industry had been trying to balance the existing risks in the financial systems for quite some time by regulating the ap-proaches to measure and manage risks. Although the latest financial crisis made us question some regulatory requirements, it had accentuated the necessity to eva-luate risks more holistically. The Bank for International Settlements, an interna-tional organization which fosters international monetary and financial cooperation and serves as a bank for cen-tral banks, had realized that developing banking prac-tices such as securitization, outsourcing, specialized processing operations and reliance on technology had introduced substantial risks which need to be reflected in credible capital assessments. As a result, Basel II Ac-cord that includes risks other than credit and market was proposed to be “a comprehensive framework for improving bank safety and soundness by more closely linking regulatory capital requirements with bank risk, by improving the ability of supervisors and financial markets to assess capital adequacy”, according to Fed-eral Reserve Chairman Ben S. Bernanke in a May 2006 speech at the Federal Reserve Bank of Chicago’s 42nd Annual Conference on Bank Structure and Competition. All the largest banks in the world that carry internation-al component are mandated to become Basel certified. Resulting international risk management practice for financial institutions focuses on three main risk catego-ries: Market Risk, Credit Risk and Operational Risk (OR). OR is a relatively newly defined category of risk which includes many different types of risk, from the simple operations risks of transaction processing, unau-thorized activities and systems risks to other types of risk that are not included in market or credit risks, such as human, legal, informational and reputational risks with the exception of strategic risk [2]. By contrast with credit and market risks where acceptable models and measurements exist, it is relatively difficult to identify and evaluate levels of operational risk due to the com-plexity of its origin, lack of existing historic quantita-tive data and explanation. One of the best publicized OR risk events are rouge trading in the French Societe Generale bank, BP oil spill, and American Maddoff “ponzi” scheme causing billions of dollars to a global financial system. Scenario analysis as a decision making tool has been in existence for several decades and has been used in various disciplines, including management, engineer-ing, defense, medicine, finance and economics [6]. Sce-nario analysis is one of the four required data elements that banks must incorporate into their Advanced Model-ing Approach to adequately measure their operational risk levels. Scenario analysis brings a forward looking element into OR assessment since it describes future plausible severe OR loss events. The current range of practice published in 2009 by Basel Committee of Banking Supervision [16] identifies a lack of consistent controls to address scenario analysis bias noting that scenario analysis is subjective in nature. Chosen and estimated scenarios become an effective technique for stimulating management to identify key strategic opera-tional risks facing their businesses. Scenario workshops are used as the preferred method to develop a set of scenarios. During scenario workshop, experts are sup-plied with a library of plausible extreme operational events that have or may have happened in peer financial organization and are asked to assess the likelihood and severity of similar events if they occur in their business, as described by the Bank of Japan [25]. Scenarios as a framework offer the ability to bring qualitative business intuition data into a systematic assessment of OR which is increasingly important in an era of stress testing of international financial sector. Making point estimates in
用模糊数法评价操作风险暴露情景分析
情景作为一个框架提供了将定性商业直觉数据引入OR系统评估的能力,这在国际金融部门压力测试时代日益重要。进行点估计
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