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| Forewords | |
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| Introduction | |
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| Foundations | |
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| Risk management: principles and practice | |
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| Definitions | |
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| Systematic and unsystematic risk | |
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| Insurable risks | |
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| Exposure | |
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| Management | |
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| Risk management | |
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| Risk management objectives | |
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| Organizational objectives | |
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| Other significant objectives | |
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| Risk management decision process | |
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| Step 1-Diagnostic of exposures | |
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| Step 2-Risk treatment | |
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| Step 3-Audit and corrective actions | |
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| State of the art and the trends in risk management | |
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| Risk profile, risk map or risk matrix | |
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| Risk financing and strategic financing | |
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| From risk management to strategic risk management | |
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| From managing property to managing reputation | |
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| From risk manager to chief risk officer | |
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| Why is risk quantification needed?Risk quantification - a knowledge-based approach | |
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| Introduction | |
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| Causal structure of risk | |
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| Building a quantitative causal model of risk | |
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| Exposure, frequency, and probability | |
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| Exposure, occurrence, and impact drivers | |
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| Controlling exposure, occurrence, and impact | |
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| Controllable, predictable, observable, and hidden drivers | |
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| Cost of decisions | |
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| Risk financing | |
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| Risk management programme as an influence diagram | |
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| Modelling an individual risk or the risk management programme | |
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| Summary | |
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| Tool Box | |
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| Probability basics | |
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| Introduction to probability theory | |
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| Conditional probabilities | |
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| Independence | |
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| Bayes' theorem | |
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| Random variables | |
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| Moments of a random variable | |
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| Continuous random variables | |
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| Main probability distributions | |
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| Introduction-the binomial distribution | |
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| Overview of usual distributions | |
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| Fundamental theorems of probability theory | |
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| Empirical estimation | |
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| Estimating probabilities from data | |
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| Fitting a distribution from data | |
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| Expert estimation | |
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| From data to knowledge | |
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| Estimating probabilities from expert knowledge | |
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| Estimating a distribution from expert knowledge | |
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| Identifying the causal structure of a domain | |
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| Conclusion | |
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| Bayesian networks and influence diagrams | |
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| Introduction to the case | |
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| Introduction to Bayesian networks | |
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| Nodes and variables | |
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| Probabilities | |
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| Dependencies | |
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| Inference | |
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| Learning | |
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| Extension to influence diagrams | |
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| Introduction to Monte Carlo simulation | |
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| Introduction | |
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| Introductory example: structured funds | |
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| Risk management example 1 - hedging weather risk | |
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| Description | |
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| Collecting information | |
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| Model | |
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| Manual scenario | |
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| Monte Carlo simulation | |
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| Summary | |
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| Risk management example 2- potential earthquake in cement industry | |
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| Analysis | |
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| Model | |
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| Monte Carlo simulation | |
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| Conclusion | |
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| A bit of theory | |
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| Introduction | |
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| Definition | |
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| Estimation according to Monte Carlo simulation | |
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| Random variable generation | |
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| Variance reduction | |
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| Software tools | |
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| Quantitative Risk Assessment: A Knowledge Modelling Process | |
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| Introduction | |
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| Increasing awareness of exposures and stakes | |
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| Objectives of risk assessment | |
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| Issues in risk quantification | |
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| Risk quantification: a knowledge management process | |
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| The basel II framework for operational risk | |
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| Introduction | |
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| The three pillars | |
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| Operational risk | |
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| The basic indicator approach | |
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| The sound practices paper | |
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| The standardized approach | |
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| The alternative standardized approach | |
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| The advanced measurement approaches (AMA | |
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| Risk mitigation | |
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| Partial use | |
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| Conclusion | |
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| Identification and mapping of loss exposures | |
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| Quantification of loss exposures | |
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| The candidate scenarios for quantitative risk assessment | |
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| The exposure, occurrence, impact (XOI) model | |
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| Modelling and conditioning exposure at peril | |
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| Summary | |
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| Modelling and conditioning occurrence | |
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| Consistency of exposure and occurrence | |
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| Evaluating the probability of occurrence | |
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| Conditioning the probability of occurrence | |
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| Summary | |
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| Modelling and conditioning impact | |
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| Defining the impact equation | |
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| Defining the distributions of variables involved | |
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| Identifying drivers | |
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| Summary | |
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| Quantifying a single scenario | |
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| An example - "fat fingers" scenar | |