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

	List of Tables	ix
	List of Figures	x
	Preface	xiii
Part 1	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
3.5	Models become more important in conjunction to internal control	57
3.6	Human factors in organisation and modelling	60
Part 2	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 3	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
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 4	What to do and not to do with Models
10	The Model's 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
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 5	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