Measuring operational & reputational risks: a practitioners approach A Practitioner′s Approach The Wiley Finance Series, Vol. 448

This book shows practitioners the best models to use in a given situation, according to the type of risk an organization is facing. It is based on extensive applied research on operational risk models, testing results on Unicredit datasets. Theoretical models and available research do not include a direct testing on real databases, as banks' relevant information isn't available to the general public and academia. For operational risk this remains a key challenge in the development of models correctly interpreting the risk structure and elements. UniCredit operational risk team, formed by experienced former academics and finance practitioners, have applied and tested various models and fitting techniques.

Contents

Foreword by Alessandro Profumo

Preface

Acknowledgments

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 a 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

2.4 Minimum loss threshold

2.5 External 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 database evolution

2.11 Final considerations

3 Loss distribution approaches

3.1 Calculation dataset building

3.2 General LDA framework

3.3 Operational risk classes

3.4 Parametric estimation and goodness of fit techniques

3.5 Applying extreme value theory

3.6 g and h distribution theory

3.7 Calculating operational capital at risk

3.8 Insurance modeling

3.9 Adjustment for risk indicators

3.10 Operational risk classes aggregation

3.11 The closed form approximation for OpVaR

3.12 Confidence band for capital at risk

3.13 Stress testing

3.14 Loss data minimum threshold setting

3.15 Empirical application on fitch OpData

3.16 Regulatory capital requirement

3.17 Economic capital requirement

3.18 Integration of operational risk in the budgeting process

4 Analyzing insurance policies

4.1 The role of insurance and risk transfer in risk management

4.2 Qualifying criteria in the Basel 2 capital framework

4.3 A Practical application to traditional insurance

5 Managing reputational risk

5.1 Introducing reputational risk

5.2 A Financial institutions reputational risk exposure

5.3 Managing reputational risk: a matter of policy

5.4 Reputational Risk Measurement

5.5 A Recent example that shocked the industry: Société Générale

6 Conclusions

References

Further reading

Index

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

BERT CRIELAARD works in the Operational Risk Department of UniCredit (Holding) and is group-wide responsible for operational risk management in the Corporate, Private Banking and Asset Management business divisions. Previously he worked in the insurance and asset management industry in Italy and the Netherlands. He is (co-)author of articles on insurance in risk management.

FABIO PIACENZA is a senior quantitative analyst at UniCredit Group Operational Risk Management in Milan. Graduated in mathematics, he is author of several articles on operational risk related topics.

DANIELE RUSPANTINI works in UniCredit Group Milan in the Operational Risk Management team, graduated in mathematics, he is co author of articles on quantitative risk management.