Modelling the Survival of Financial and Industrial Enterprises: Advantages, Challenges and Problems with the Internal-Ratings Base (IRB)

314 Pages·2002·1.79 MB·English

by  Dimitris N. Chorafas

Modelling the Survival of
Financial and Industrial
Enterprises
Advantages, Challenges and Problems with
the Internal Ratings-Based (IRB) Method
Dimitris N. Chorafas
Modelling the Survival of Financial and Industrial Enterprises
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Modelling the Survival of
Financial and Industrial
Enterprises
Advantages, Challenges and Problems with the
Internal Ratings-Based (IRB) Method
Dimitris N. Chorafas
© Dimitris N. Chorafas 2002
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First published 2002 by
PALGRAVE MACMILLAN
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PALGRAVE MACMILLAN is the global academic imprint of the Palgrave
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ISBN 0–333–98466–8
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Library of Congress Cataloging-in-Publication Data
Chorafas, Dimitris N.
Modelling the survival of financial and industrial enterprises:
advantages, challenges and problems with the internal
ratings-based method/by Dimitris N. Chorafas.
p. cm.
Includes bibliographical references and index.
ISBN 0–333–98466–8
1. Financial institutions—Mathematical models. 2. Business
enterprises—Mathematical models. I. Title.
HG173.C565 2002
338.7′01′5118—dc21 2001059114
10 9 8 7 6 5 4 3 2 1
11 10 09 08 07 06 05 04 03 02
Printed and bound in Great Britain by
Antony Rowe Ltd, Chippenham and Eastbourne
Contents
List of Tables ix
List of Figures x
Preface xiii
Part One Understanding the Contribution of
Science and of Models
1 Science and the Solution of Real-life Business Problems 3
1.1 Introduction 3
1.2 Thinking is the common ground between science
and philosophy 5
1.3 Principles underlying scientific thought 8
1.4 What is meant by the scientific method? 12
1.5 Models and the internal rating-based solution 16
1.6 Natural death and oblivion of models, products,
factories, companies and people 20
2 Is the Work of Financial Analysts Worth the
Cost and the Effort? 24
2.1 Introduction 24
2.2 The role of financial analysts 25
2.3 Metaknowledge is a basic concept of science
and technology 29
2.4 Metaphors, real world problems and their solution 32
2.5 Characteristics of an internally consistent analysis 36
2.6 Financial studies and the methodology of
physicists and inventors 39
2.7 Management based on research and analysis 42
3 The Contribution of Modelling and Experimentation
in Modern Business 45
3.1 Introduction 45
3.2 The multiple role of analysis in the financial industry 46
3.3 Can models help in improving business leadership? 48
3.4 Non-traditional financial analysis and qualitative criteria 54
v
vi Contents
3.5 Models become more important in conjunction
to internal control 57
3.6 Human factors in organisation and modelling 60
Part Two Elements of the Internal Rating-based Method
4 Practical Applications: the Assessment of Creditworthiness 67
4.1 Introduction 67
4.2 Notions underpinning the control of credit risk 68
4.3 RAROC as a strategic tool 74
4.4 Standardised approach and IRB method of Basle II 78
4.5 Amount of leverage, loss threshold and counterparty risk 81
4.6 Risk factors help in better appreciation of exposure 84
4.7 Has the Westdeutsche Landesbank Girozentrale
(West LB) an AA+ or a D rating? 88
5 Debts and the Use of Models in Evaluating Credit Risk 91
5.1 Introduction 91
5.2 Contribution of information technology (IT) to
the control of credit exposure 94
5.3 Credit risk, rating and exposure: examples
with credit derivatives 97
5.4 Rules by Banque de France on securitisation
of corporate debt 101
5.5 Credit derivatives with non-performing loans:
Banca di Roma and Thai Farmers’ Bank 106
5.6 Don’t use market risk models for credit risk 108
6 Models for Actuarial Science and the Cost of Money 113
6.1 Introduction 113
6.2 Basic principles underpinning actuarial science 114
6.3 The stochastic nature of actuarial models 120
6.4 Interest rates, present value and discounting 123
6.5 Modelling a cash flow system 126
6.6 Actuarial reserves and collective models 129
Part Three Forecasting, Reporting, Evaluating
and Exercising Market Discipline
7 Scenario Analysis and the Delphi Method 137
7.1 Introduction 137
7.2 Why expert opinion is not available matter-of-course 139
Contents vii
7.3 The delphi method helps management avoid
tunnel vision 141
7.4 Scenarios and the pattern of expert advice 146
7.5 Extending the scope of analytics and the
planning horizon 151
7.6 Making effective use of informed intuitive judgement 154
8 Financial Forecasting and Economic Predictions 157
8.1 Introduction 157
8.2 The art of prognostication and its pitfalls 158
8.3 Predictive trends, evolutionary concepts
and rocket scientists 162
8.4 A prediction theory based on the underlying
simplicity of systems 166
8.5 Undocumented hypotheses are in the background
of many model failures 171
8.6 Investment horizon and the arrow of time 174
9 Reliable Financial Reporting and Market Discipline 179
9.1 Introduction 179
9.2 Committee of Sponsoring Organisations (COSO) of the
Treadway Commission and implementation of COSO 181
9.3 Qualitative and quantitative disclosures by financial
institutions 184
9.4 Proactive regulation and the use of an accounting
metalanguage 188
9.5 Defining the territory where new regulations must apply 191
9.6 Measurement practices, reporting guidelines and
management intent 194
9.7 Why fair value in financial reporting is a superior method 198
Part Four What to do and not to do with Models
10 The Model’ss Contribution: Examples with Value at Risk
and the Monte Carlo Method 203
10.1 Introduction 203
10.2 Concepts underpinning value at risk and its usage 204
10.3 What VAR is and what it is not 209
10.4 Historical correlation and simulation with VAR models 213
10.5 The bootstrapping method and backtesting 215
10.6 Levels of confidence with models and operating
characteristics curves 218
viii Contents
11 Is Value at Risk an Alternative to Setting Limits? 224
11.1 Introduction 224
11.2 Establishing a policy of prudential limits 226
11.3 Limits, VAR and market risk 230
11.4 The impact of level of confidence on the
usability of VAR 233
11.5 Can we use eigenmodels for precommitment? 237
11.6 Using the warning signals given by value at risk 240
Part Five Facing the Challenge of Model Risk
12 Errors in Prognostication 247
12.1 Introduction 247
12.2 ‘For’ and ‘against’ the use of models for forecasting 249
12.3 Faulty assumptions by famous people and their models 252
12.4 The detection of extreme events 257
12.5 Costly errors in option pricing and volatility smiles 261
12.6 Imperfections with modelling and simulation 265
13 Model Risk is Part of Operational Risk 268
13.1 Introduction 268
13.2 The risk you took is the risk you got 270
13.3 Model risk whose origin is in low technology 272
13.4 The downside may also be in overall operational risk 275
13.5 Operational risk in the evaluation of investment factors 278
13.6 How far can internal control reduce operational risk? 281
13.7 The contribution that is expected from auditing 285
Notes 288
Index 292
List of Tables
3.1 Banking problems studied through models and
simulation since the 1960s 49
4.1 Increasing probabilities of average cumulative
default rates over a 15-year timespan (%) 69
4.2 Risk factors for swaps trades worked out by the
Federal Reserve and the Bank of England 86
6.1 The top eight telecoms debt defaults in the first half
of 2001 130
6.2 Consolidated statements of cash flows 131
9.1 Three types of risk to which the board and senior
management must pay attention 190
10.1 The time schedule of major regulatory measures by
the Basle Committee on banking supervision 204
ix