Georgia State University ScholarWorks @ Georgia State University Risk Management and Insurance Dissertations Department of Risk Management and Insurance 5-11-2006 Operational Risk Capital Provisions for Banks and Insurance Companies Edoh Fofo Afambo Follow this and additional works at: https://scholarworks.edu/rmi_diss Part of the Insurance Commons Recommended Citation Afambo, Edoh Fofo, "Operational Risk Capital Provisions for Banks and Insurance Companies." Dissertation, Georgia State University, 2006. doi: https://doi.57709/1059047 This Dissertation is brought to you for free and open access by the Department of Risk Management and Insurance at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Risk Management and Insurance Dissertations by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact scholarworks@gsu.
PERMISSION TO BORROW In presenting this dissertation as a partial fulfillment of the requirements for an advanced degree from Georgia State University, I agree that the Library of the University shall make it available for inspection and circulation in accordance with its regulations governing materials of this type. I agree that permission to quote from, to copy from, or to publish this dissertation may be granted by the author or, in his/her absence, the professor under whose direction it was written or, in the absence, by the Dean of the Robinson College of Business. Such quoting, copying, or publishing must be solely for scholarly purposes and does not involve potential financial gain. It is understood that any copying or publication of this dissertation which involves potential gain will not be allowed without written permission of the author.
___________________________ Edoh Fofo Afambo NOTICE TO BORROWERS All dissertations deposited in the Georgia State University Library must be used only in accordance with the stipulations prescribed by the author in the preceding statement. The author of this dissertation is: Edoh Fofo Afambo 208 N Decatur Ln Decatur, GA 30033, USA kfofo@yahoo.com, kfofoedoh@gmail.com The Supervisor of this dissertation is: Dr. Cox Department of Risk Management and Insurance 35 Broad Street, Robinson College of Business Atlanta, GA 30303, USA Users of this dissertation not regularly enrolled as students of Georgia State University are required to attest acceptance of the preceding stipulations by signing below. Libraries borrowing this dissertation for the use of their patrons are required to see that each user records here the information requested.
Name of User Address Date Type of Use (Examination only or copying) OPERATIONAL RISK CAPITAL PROVISIONS FOR BANKS AND INSURANCE COMPANIES By Edoh Fofo Afambo A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in the Robinson College of Business Of Georgia State University GEORGIA STATE UNIVERSITY ROBINSON COLLEGE OF BUSINESS 2006 Copyright by Edoh Fofo Afambo 2006 Acceptance This dissertation was prepared under the direction of Edoh Fofo Afambo’s Dissertation Committee. It has been approved and accepted by all members of that committee, and it has been accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Robinson College of Business of Georgia State University. Fenwick Huss, Dean Robinson College of Business Dissertation Committee: ___________________________ Dr. Cox, Chair ___________________________ Dr.
Sanjay Srivastava ___________________________ Dr. Harry Panjer ___________________________ Dr. Shaun Wang ABSTRACT OPERATIONAL RISK CAPITAL PROVISIONS FOR BANKS AND INSURANCE COMPANIES BY Edoh Fofo Afambo 2006 Committee chair: Samuel H. Cox Major Academic Unit: Risk Management and Insurance This dissertation investigates the implications of using the Advanced Measurement Approaches (AMA) as a method to assess operational risk capital charges for banks and insurance companies within Basel II paradigms and with regard to U.
Operational risk has become recognized as a major risk class because of huge operational losses experienced by many financial firms over the last past decade. Unlike market risk, credit risk, and insurance risk, for which firms and scholars have designed efficient methodologies, there are few tools to help analyze and quantify operational risk. The New Basel Revised Framework for International Convergence of Capital Measurement and Capital Standards (Basel II) gives substantial flexibility to internationally active banks to set up their own risk assessment models in the context of vi the Advanced Measurement Approaches. The AMA developed in this thesis uses actuarial loss models complemented by the extreme value theory to determine the empirical probability distribution function of the overall capital charge in terms of various classes of copulas.
Publicly available operational risk loss data set is used for the empirical exercise. vii DEDICATION To my wife Peace, my children Rose and Nitya Cedric, and my parents. viii ACKNOWLEDGEMENTS I would like to extend my thanks to my dissertation chair, Dr. Cox who suggested to me the idea of this dissertation and guided me through the process with valuable comments.
I would also like to thank Dr. Sanjay Srivastava, Dr. Harry Panjer and Dr. Shaun Wang for agreeing to serve on my committee and for their helpful suggestions.
I am indebted to the Anderson Memorial Committee for financing my graduate education for three years. I would also like to thank the Society of Actuaries who financially supported this research. Above all, I owe a huge debt of gratitude to my wife Peace who patiently stood by me and encouraged me during the entire process. ix TABLE OF CONTENTS ABSTRACT.
ix TABLE OF CONTENTS.x LIST OF FIGURES. xii LIST OF TABLES. xiv CHAPTER 1 INTRODUCTION .2 Contributions and Organization of the Dissertation .5 CHAPTER 2 LITERATURE REVIEW .1 The Regulatory Framework .1 Rationale for Banking and Insurance Solvency Regulation .2 Basel Committee on Banking Supervision Framework: From the Cooke Ratio to the McDonough Ratio .3 Regulatory Capital Framework for the U.2 The Computational Aspects of the AMA .2 Practitioner and Academic Literature .1 Emerging Practices and Related Issues.2 Loss Severity Modeling .3 Loss Frequency Modeling.5 Self-Assessment and Scenario Analysis .48 CHAPTER 3 THE MODEL.2 BCBS Models for the Capital Charge.1 Basic Indicator Approach .3 Advanced Measurement Approaches .1 Internal Measurement Approach.3 Loss Distribution Approach.3 Loss Distribution Approach.1 The Cramer-Lundberg Model .2 The Point Process Methodology .2 Loss Severity Distribution Models .1 Publicly Available Operational Loss Modeling.2 Symbolic Computational Model .3 Calibration for Specific Organizations .3 Loss Frequency Distribution.4 Modeling Dependence Structure.5 Capital Charge Modeling.83 CHAPTER 4 EMPIRICAL ANALYSIS .2 The Data Set.3 Loss Severity Distribution Function .141 xi LIST OF FIGURES Figure 1.1 -- US Banks - Histogram of Contributor’s Log-Truncation-Point. – All Business Lines and All Event Types .2 -- US Insurers - Histogram of Contributor’s Log-Truncation-Point – All Business Lines All Event Types .1 -- US Banks Yearly Aggregate Losses By Business Lines & Settlement Year .2 -- US Banks - Yearly Aggregate Losses By Event Types & Settlement Year.3 -- US Banks - Yearly Aggregate Losses By CPBP Sub Event Types & Settlement Year.4 -- US Insurers - Yearly Aggregate Losses By Event Types & Settlement Year.5 -- US Insurers - Yearly Aggregate Losses By CPBP Sub Event Types & Settlement Year.6 -- US Banks & Insurers - Yearly Aggregate Loss by Event Types & Settlement Year.7 -- US Banks - Yearly Aggregate Loss Amounts & Occurrences by Settlement Year.8 -- US Insurers - Yearly Aggregate Loss Amounts & Occurrences by Settlement Year.1 - US Banks Underlying Loss Severity Distribution Parameter by Business Units and Random Truncation Point Distributional Assumption.2 - US Banks Underlying Loss Severity Distribution Parameter by Business Lines and Random Truncation Point Distributional Assumption.3 -- US Banks - Quantile-Quantile Plot All Business Lines All Event Types .4 -- US Banks - Observed Severity Distribution and Underlying Severity Distribution.
All Business Lines All Event Types.5 -- US Banks - QQ Plot CPBP .6 -- US Banks - Observed Severity Distribution and Underlying Severity Distribution.7 -- US Banks - QQ Plot Internal Fraud-EPWS .8 -- US Banks - Observed Severity Distribution and Underlying Severity Distribution. Internal Fraud - EPWS.9 -- US Banks- Observed Severity Distribution and Underlying Severity Distribution.10- US Banks- Observed Severity Distribution and Underlying Severity Distribution Retail Banking.11- US Banks- Observed Severity Distribution and Underlying Severity Distribution.12- US Banks- Observed Severity Distribution and Underlying Severity Distribution Retail Brokerage.13- Random Truncation Point Distribution CDF Industry-Wide Organization vs Specific Firm .14- Random Truncation Point Distribution PDF Industry-Wide Organization vs Specific Firm .15- US Banks CPBP Capital Charge Distribution ($M) .16- US Banks Internal Fraud Capital Charge Distribution ($M) .17- US Banks Other Event Types Capital Charge Distribution ($M).18- US Banks Aggregated Capital Charge Distribution ($M) Using Cauchy Copula.19- US Banks Aggregated Capital Charge ($M) for the Student’s t -Copula .130 xiii LIST OF TABLES Table 1. 1 --US Bank and Insurers - Number of Losses per Contributor. 2 --US Bank Contributors’ Truncation Point ($ M) .100 Descriptive Statistics by Business Lines.
3 --US Bank Contributors’ Truncation Point ($ M) .101 Descriptive Statistics by Event Types. 4 --US Insurer Contributors’ Truncation Point ($ M).101 Descriptive Statistics by Event Types. 5 --US Bank & Insurer Contributors’ Truncation Point ($ M) by Percentiles. 1 -- US Banks and Insurers’ Total Revenue .104 Descriptive Statistics by Size.
2 --US Banks and Insurers’ Total Revenue by Percentile. 2 --US Banks - Loss Occurrences by Business Lines & Event Types. 3 -- US Insurers - Loss Occurrences by Business Lines & Event Types. 1--US Banks & Insurers –Observed Loss Severity Distribution Parameters by Business Units/Event Types and Random Truncation Distributional Assumptions.
2 --US Banks–Observed Loss Severity Distribution Parameters by Business Lines/Event Types and Random Truncation Distributional Assumptions. 3 --US Banks & Insurers: Underlying Loss Severity by Exposure (Revenue) All Business Lines and Event Types. 4 --US Banks & Insurers: Loss Severity by Exposure (Revenue) All Business Lines and Event Types. 5 – Observed Loss Severity Distribution Parameters Industry-Wide Organization vs Specific Firm.
6 US Banks and Insurers: Sample Rank & Linear Correlation by Business Unit/Event types. 7 --US Banks and Insurers .128 Capital Charge’s Sensitivity to the Truncation Point Distributional Assumption All Business Lines and All Event Types. 8 – US Banks and Insurers.128 Capital Charge ($M) Assuming Various Yearly Number of Loss Occurrences. 9 -- US Banks and Insurers .128 Capital Charge ($M) for Three Business Line and Event Type Combinations.131 Capital Charges ($M) and Capital Savings ($M) by Types of Copulas.132 Capital Charges ($M) and Capital Saving ($M) by Types of Copulas.133 Descriptive Statistics of the Capital Charge ($M).
13 -- Mixing Weights by types of copulas. 14 --US Industry-Wide Bank & Specific Bank .135 Descriptive Statistics of the Mixing Weighted Capital Charges ($M). 15 -- Capital Charges by Common Shock Intensity. Risk Weights by Category of On-Balance-Sheet Asset BCBS (1988).
BCBS Business Lines .140 xv CHAPTER 1 INTRODUCTION 1.1 Motivation A look inside the banking industry over the last decade clearly reveals two stylized facts. On the one hand, increasing complexity of financial technology combined with deregulation and globalization trends have made banking practices more sophisticated and challenging. As a result, the industry faced new multifaceted risks envisioned as part of ‘other risks’ and as such, different from market and credit risk. These include system security and fraud risks arising from the expansion of e-commerce, system failure risks on account of the use of highly automated technology, and many other significant risks resulting from the increased use of outsourcing arrangements and new risk mitigation techniques such as credit derivatives, swaps, and asset securitization (BCBS, 2003c).
On the other hand, the banking industry all over the world has witnessed a growing number of insolvencies and experienced high-profile ‘other risks’ losses. In 1998, the press reported more than US$20 billion of ‘other risk’ losses in financial service firms, including the insurance industry. These combined facts brought supervisors as well as banking and insurance executives to view the management of these ‘other risks’ as a comprehensive practice comparable to the management of credit and market risk (BCBS, 2003c).