Using Loss Data to Quantify Operational Risk

Fontnouvelle, Patrick de; Jesus-Rueff, Virginia De; Jordan, John S.; Rosengren, Eric S.

doi:10.2139/ssrn.395083

Cited by 73 publications

(35 citation statements)

References 12 publications

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“…Operational risk, by far, had not received the same amount of attention as credit and market risk until recently. De Fontnouvelle, DeJesus-Rueff, Jordan, and Rosengren (De Fontnouvelle et al 2003) suggested that operational risk has been impeded by the lack of internal or external data on operational losses. Operational losses are an important source of risk, and the capital charge for operational risk often exceeds the charge for market risk.…”

Section: Hypothesis 5 (H5) There Is a Significant Interrelation Betwmentioning

confidence: 99%

The Impact of Risk Management in Credit Rating Agencies

Seetharaman¹,

Sahu²,

Saravanan

et al. 2017

Risks

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An empirical study was conducted to determine the impact of different types of risk on the performance management of credit rating agencies (CRAs). The different types of risks were classified as operational, market, business, financial, and credit. All these five variables were analysed to ascertain their impact on the performance of CRAs. In addition, apart from identifying the significant variables, the study focused on setting out a structured framework for future research. The five independent variables were tested statistically using structural equation modelling (SEM). The results indicated that market risk, financial risk, and credit risk have a significant impact on the performance of CRAs, whereas operational risk and business risk, though important, do not have a significant influence. This finding has a significant implication for the examination and inter-firm evaluation of CRAs.

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Section: Hypothesis 5 (H5) There Is a Significant Interrelation Betwmentioning

confidence: 99%

The Impact of Risk Management in Credit Rating Agencies

Seetharaman¹,

Sahu²,

Saravanan

et al. 2017

Risks

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“…A previous study by de Fontnouvelle et al (2003) analyzes both the OpVar database and a similar database from a competing vendor, OpRisk Analytics. 3 Their primary objective is to quantify operational risk and to provide guidance to managers and regulators about the magnitude of operational risk capital in the banking industry.…”

Section: Prior Literaturementioning

confidence: 99%

The market value impact of operational loss events for US banks and insurers

Cummins

Lewis²,

Wei

2006

Journal of Banking & Finance

214

113

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“…The closest work in this respect is a study by Chavez-Demoulin, Embrechts and Neslehova (2006) in which the authors focus on individual statistical modelling issues and illustrate them using transformed operational risk data, a framework which prevents them from discussing the underlying practical issues in great detail. Other related investigations are reported by Fontnouvelle, Jordan and Rosengren (2003), who rely on a public operational loss database (which is not exhaustive and restricted to large losses), and by Moscadelli (2004), who uses loss data gathered during the 2002 Loss Data Collection Exercise carried out by the Basel Committee. The paper by Di Clemente and Romano (2004) performs its analysis on catastrophe insurance data.…”

mentioning

confidence: 99%

Practical methods for measuring and managing operational risk in the financial sector: A clinical study

Chapelle

Crama

Hübner

et al. 2008

Journal of Banking & Finance

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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. for the assessment of operational risk. Through a clinical case study of a loss event matrix concerning two business lines and two event types for 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. Keywords2

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Using Loss Data to Quantify Operational Risk

Cited by 73 publications

References 12 publications

The Impact of Risk Management in Credit Rating Agencies

The Impact of Risk Management in Credit Rating Agencies

The market value impact of operational loss events for US banks and insurers

Practical methods for measuring and managing operational risk in the financial sector: A clinical study

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