| List of Tables | p. ix |
| List of Figures | p. x |
| Preface | p. xiii |
| Understanding the Contribution of Science and of Models | |
| Science and the Solution of Real-life Business Problems | p. 3 |
| Introduction | p. 3 |
| Thinking is the common ground between science and philosophy | p. 5 |
| Principles underlying scientific thought | p. 8 |
| What is meant by the scientific method? | p. 12 |
| Models and the internal rating-based solution | p. 16 |
| Natural death and oblivion of models, products, factories, companies and people | p. 20 |
| Is the Work of Financial Analysts Worth the Cost and the Effort? | p. 24 |
| Introduction | p. 24 |
| The role of financial analysts | p. 25 |
| Metaknowledge is a basic concept of science and technology | p. 29 |
| Metaphors, real world problems and their solution | p. 32 |
| Characteristics of an internally consistent analysis | p. 36 |
| Financial studies and the methodology of physicists and inventors | p. 39 |
| Management based on research and analysis | p. 42 |
| The Contribution of Modelling and Experimentation in Modern Business | p. 45 |
| Introduction | p. 45 |
| The multiple role of analysis in the financial industry | p. 46 |
| Can models help in improving business leadership? | p. 48 |
| Non-traditional financial analysis and qualitative criteria | p. 54 |
| Models become more important in conjunction to internal control | p. 57 |
| Human factors in organisation and modelling | p. 60 |
| Elements of the Internal Rating-based Method | |
| Practical Applications: the Assessment of Creditworthiness | p. 67 |
| Introduction | p. 67 |
| Notions underpinning the control of credit risk | p. 68 |
| RAROC as a strategic tool | p. 74 |
| Standardised approach and IRB method of Basle II | p. 78 |
| Amount of leverage, loss threshold and counterparty risk | p. 81 |
| Risk factors help in better appreciation of exposure | p. 84 |
| Has the Westdeutsche Landesbank Girozentrale (West LB) an AA + or a D rating? | p. 88 |
| Debts and the Use of Models in Evaluating Credit Risk | p. 91 |
| Introduction | p. 91 |
| Contribution of information technology (IT) to the control of credit exposure | p. 94 |
| Credit risk, rating and exposure: examples with credit derivatives | p. 97 |
| Rules by Banque de France on securitisation of corporate debt | p. 101 |
| Credit derivatives with non-performing loans: Banca di Roma and Thai Farmers' Bank | p. 106 |
| Don't use market risk models for credit risk | p. 108 |
| Models for Actuarial Science and the Cost of Money | p. 113 |
| Introduction | p. 113 |
| Basic principles underpinning actuarial science | p. 114 |
| The stochastic nature of actuarial models | p. 120 |
| Interest rates, present value and discounting | p. 123 |
| Modelling a cash flow system | p. 126 |
| Actuarial reserves and collective models | p. 129 |
| Forecasting, Reporting, Evaluating and Exercising Market Discipline | |
| Scenario Analysis and the Delphi Method | p. 137 |
| Introduction | p. 137 |
| Why expert opinion is not available matter-of-course | p. 139 |
| The delphi method helps management avoid tunnel vision | p. 141 |
| Scenarios and the pattern of expert advice | p. 146 |
| Extending the scope of analytics and the planning horizon | p. 151 |
| Making effective use of informed intuitive judgement | p. 154 |
| Financial Forecasting and Economic Predictions | p. 157 |
| Introduction | p. 157 |
| The art of prognostication and its pitfalls | p. 158 |
| Predictive trends, evolutionary concepts and rocket scientists | p. 162 |
| A prediction theory based on the underlying simplicity of systems | p. 166 |
| Undocumented hypotheses are in the background of many model failures | p. 171 |
| Investment horizon and the arrow of time | p. 174 |
| Reliable Financial Reporting and Market Discipline | p. 179 |
| Introduction | p. 179 |
| Committee of Sponsoring Organisations (COSO) of the Treadway Commission and implementation of COSO | p. 181 |
| Qualitative and quantitative disclosures by financial institutions | p. 184 |
| Proactive regulation and the use of an accounting metalanguage | p. 188 |
| Defining the territory where new regulations must apply | p. 191 |
| Measurement practices, reporting guidelines and management intent | p. 194 |
| Why fair value in financial reporting is a superior method | p. 198 |
| What to do and not to do with Models | |
| The Model's Contribution: Examples with Value at Risk and the Monte Carlo Method | p. 203 |
| Introduction | p. 203 |
| Concepts underpinning value at risk and its usage | p. 204 |
| What VAR is and what it is not | p. 209 |
| Historical correlation and simulation with VAR models | p. 213 |
| The bootstrapping method and backtesting | p. 215 |
| Levels of confidence with models and operating characteristics curves | p. 218 |
| Is Value at Risk an Alternative to Setting Limits? | p. 224 |
| Introduction | p. 224 |
| Establishing a policy of prudential limits | p. 226 |
| Limits, VAR and market risk | p. 230 |
| The impact of level of confidence on the usability of VAR | p. 233 |
| Can we use eigenmodels for precommitment? | p. 237 |
| Using the warning signals given by value at risk | p. 240 |
| Facing the Challenge of Model Risk | |
| Errors in Prognostication | p. 247 |
| Introduction | p. 247 |
| 'For' and 'against' the use of models for forecasting | p. 249 |
| Faulty assumptions by famous people and their models | p. 252 |
| The detection of extreme events | p. 257 |
| Costly errors in option pricing and volatility smiles | p. 261 |
| Imperfections with modelling and simulation | p. 265 |
| Model Risk is Part of Operational Risk | p. 268 |
| Introduction | p. 268 |
| The risk you took is the risk you got | p. 270 |
| Model risk whose origin is in low technology | p. 272 |
| The downside may also be in overall operational risk | p. 275 |
| Operational risk in the evaluation of investment factors | p. 278 |
| How far can internal control reduce operational risk? | p. 281 |
| The contribution that is expected from auditing | p. 285 |
| Notes | p. 288 |
| Index | p. 292 |
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