Measuring Operational and Reputational Risk: A Practitioner's Approach


“Measuring Operational and Reputational Risk: A PractitionerApproach will be a milestone contribution to the industry’sdebates on operational and reputational risk. It is important andtimely, providing a detailed, pragmatic and highly usefuldiscussion  —David Schraa, Director, RegulatoryAffairs Dept, Institute of International Finance

“The book is comprehensive and as such is an excellentoverview of the journey of putting into practice an operationalrisk program from scratch to full implementation. The descriptionsof the challenges and their ...

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“Measuring Operational and Reputational Risk: A PractitionerApproach will be a milestone contribution to the industry’sdebates on operational and reputational risk. It is important andtimely, providing a detailed, pragmatic and highly usefuldiscussion  —David Schraa, Director, RegulatoryAffairs Dept, Institute of International Finance

“The book is comprehensive and as such is an excellentoverview of the journey of putting into practice an operationalrisk program from scratch to full implementation. The descriptionsof the challenges and their resolutions are informative for boththose just starting and are seeing guidance as well as expertslooking to learn how a leading bank solved a particularissue.”  —Tony Peccia, Operational Risk Policyand Implementation, Citigroup

“The most interesting topics in the operational risk fieldare tackled rigorously but at the same time with a genuinepractitioner’s passion.  In this book, I found the samespirit that gave life to DIPO, our loss data consortium: sharingideas and experiences is the only way to improve.” —Claudia Pasquini, Secretary General, DIPO, ItalianOperational Risk Data Consortium

“The chapter The Development of ORM in UniCredit Group isa practical guide that discusses Operational Risk methodologyincluding comprehensive detail on statistical methods forcalculating operational risk capital. A 'real world' example of howto approach the implementation of an Operational Risk practice forfinancial institutions." - Laura Polak, Director, ProductManagement, Algorithmics, Incorporated

"I believe that this work by Aldo Soprano, Bert Crielaard, FabioPiacenza and Daniele Ruspantini represents a compulsory tool forrisk managers, regulators and academics doing research in the areaof operational and reputational risk." - Andrea Sironi,Professor of Finance, Universita Bocconi

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Product Details

  • ISBN-13: 9780470517703
  • Publisher: Wiley
  • Publication date: 3/23/2009
  • Series: Wiley Finance Series, #448
  • Edition number: 1
  • Pages: 226
  • Product dimensions: 5.90 (w) x 9.00 (h) x 1.10 (d)

Meet the Author

ALDO SOPRANO is Managing Director, UniCredit Basel 2 projectmanager for Central and Eastern European countries. Previously hewas Group Head of Operational Risk Management. A Graduate inEconomics, he holds a Master in Finance. In his career he has alsobeen responsible for market risk management, credit risk control,capital allocation and more recently Chief Risk Officer ofUniCredit Kazakhstan. He is the author of several articles on riskmanagement and was the Chairman of the International Institute ofFinance’s Working Group on Operational Risk.

BERT CRIELAARD works in the Operational Risk Department ofUniCredit (Holding) and is group-wide responsible for operationalrisk management in the Corporate, Private Banking and AssetManagement business divisions. Previously he worked in theinsurance and asset management industry in Italy and theNetherlands. He is (co-)author of articles on insurance in riskmanagement.

FABIO PIACENZA is a senior quantitative analyst at UniCreditGroup Operational Risk Management in Milan. Graduated inmathematics, he is author of several articles on operational riskrelated topics.

DANIELE RUSPANTINI works in UniCredit Group Milan in theOperational Risk Management team, graduated in mathematics, he isco author of articles on quantitative risk management.

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Table of Contents

Foreword (Andrea Sironi).



1 The Development of ORM in UniCredit Group.

1.1 A brief history of a fast-growing group.

1.2 Creating a new function.

1.3 Developing the new control system.

1.4 Challenges in the early stages.

1.5 Methodology to measure operational risk.

1.6 Training and internal communication focus.

1.7 International regulatory challenges.

1.8 Reputational risk management.

2 The Calculation Dataset.

2.1 Definitions.

2.2 Rules of thumb.

2.3 Internal loss data.

2.3.1 Business line mapping.

2.3.2 Event type classifications.

2.3.3 Data quality analysis.

2.3.4 Special cases.

2.4 Minimum loss threshold.

2.5 External data.

2.5.1 Public or external data sources.

2.5.2 Consortium data.

2.5.3 Scenario data.

2.6 Business environment and internal control factors.

2.7 Scenarios.

2.8 Insurance information.

2.9 Scaling data.

2.10 The Unicredit Group Operational Risk databaseEvolution.

2.11 Final considerations.

3 Loss Distribution Approaches.

3.1 Calculation dataset building.

3.1.1 Internal calculation dataset.

3.1.2 External calculation dataset.

3.1.3 Scenario-generated calculation dataset.

3.1.4 Risk indicators calculation dataset.

3.2 General LDA framework.

3.3 Operational risk classes.

3.3.1 Identically distributed risk classes.

3.3.2 Inflation adjustment.

3.3.3 Data independence.

3.4 Parametric estimation and goodness-of-fit Techniques.

3.4.1 Severity distributions.

3.4.2 Graphical methods.

3.4.3 Analytical methods.

3.4.4 Frequency distributions.

3.5 Applying extreme value theory.

3.6 g-and-h distribution theory.

3.7 Calculating operational capital at risk.

3.7.1 Loss severity distribution.

3.7.2 Loss frequency distribution.

3.7.3 Annual loss distribution.

3.7.4 Single class capital at risk.

3.8 Insurance modeling.

3.8.1 Appropriate haircuts reflecting the policy’sdeclining residual term.

3.8.2 Payment uncertainty.

3.8.3 Counterparty risk.

3.8.4 Application of insurance.

3.9 Adjustment for risk indicators.

3.10 Operational risk classes aggregation.

3.10.1 Copulae functions.

3.10.2 Elliptical copulae.

3.10.3 Archimedean copulae.

3.10.4 Choice of copula.

3.10.5 Correlation coefficients.

3.11 The closed-form approximation for OpVaR.

3.11.1 Effect of the minimum threshold on capital at risk.

3.12 Confidence band for capital at risk.

3.13 Stress testing.

3.14 Loss data minimum threshold setting.

3.15 Empirical application on Algo OpData.

3.15.1 Descriptive statistics.

3.15.2 Autocorrelation analysis.

3.15.3 Capital at risk estimates using parametric models.

3.15.4 Capital at risk estimates using EVT.

3.15.5 Capital at risk estimates using theg-and-h distribution.

3.15.6 Capital at risk estimates considering Correlation.

3.16 Regulatory capital requirement.

3.16.1 The consolidated capital requirement.

3.16.2 The individual capital requirement.

3.17 Economic capital requirement.

3.18 Integration of operational risk in the budgetingprocess.

4 Analyzing Insurance Policies.

4.1 Insurance management and risk transfer.

4.2 Qualifying criteria in the Basel 2 capitalFramework.

4.2.1 Rating of the insurance company.

4.2.2 Duration and residual term of the insurance contract.

4.2.3 Policy termination requisites.

4.2.4 Claims reimbursement uncertainty and ineffectivecoverage.

4.2.5 Conclusions.

4.3 A practical application to traditional insurance.

4.3.1 Insurance policies to cover financial institutions’operational risks.

4.3.2 Operational event types and available insurancecoverage.

5 Managing Reputational Risk.

5.1 Introducing reputational risk.

5.2 A financial institution’s reputational riskexposure.

5.3 Managing reputational risk: a matter of policy.

5.4 Reputational risk measurement.

5.4.1 Reputational risk as a function of share pricevolatility.

5.4.2 Measuring reputational risk using scenarios.

5.4.3 Scoring-card-based models for reputational riskassessment.

5.5 A recent example of reputational event.

5.5.1 A description of the event.

5.5.2 Background.

5.5.3 How the fake trading occurred.

5.5.4 The discovery and first reactions.

5.5.5 Measures planned and taken.

5.5.6 Immediate consequences for SocGen.

5.5.7 Reputational issues and comments.

5.5.8 The lessons learned – what can we do to avoid beingnext?

5.5.9 Psychological, ‘soft’ factors.

5.5.10 Control instruments.

5.5.11 Managing data and signals.

6 Conclusions.


Further reading.


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