Risk quantification : management, diagnosis and hedging /
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| Author / Creator: | Condamin, Laurent. |
|---|---|
| Imprint: | Chichester, West Sussex, England ; Hoboken, NJ : John Wiley, c2006. |
| Description: | xiv, 271 p. : ill. ; 26 cm. |
| Language: | English |
| Series: | Wiley finance series |
| Subject: | |
| Format: | Print Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/6827778 |
Table of Contents:
- Forewords
- Introduction
- 1. Foundations
- Risk management: principles and practice
- Definitions
- Systematic and unsystematic risk
- Insurable risks
- Exposure
- Management
- Risk management
- Risk management objectives
- Organizational objectives
- Other significant objectives
- Risk management decision process
- Step 1-Diagnostic of exposures
- Step 2-Risk treatment
- Step 3-Audit and corrective actions
- State of the art and the trends in risk management
- Risk profile, risk map or risk matrix
- Risk financing and strategic financing
- From risk management to strategic risk management
- From managing property to managing reputation
- From risk manager to chief risk officer
- Why is risk quantification needed?Risk quantification - a knowledge-based approach
- Introduction
- Causal structure of risk
- Building a quantitative causal model of risk
- Exposure, frequency, and probability
- Exposure, occurrence, and impact drivers
- Controlling exposure, occurrence, and impact
- Controllable, predictable, observable, and hidden drivers
- Cost of decisions
- Risk financing
- Risk management programme as an influence diagram
- Modelling an individual risk or the risk management programme
- Summary
- 2. Tool Box
- Probability basics
- Introduction to probability theory
- Conditional probabilities
- Independence
- Bayes' theorem
- Random variables
- Moments of a random variable
- Continuous random variables
- Main probability distributions
- Introduction-the binomial distribution
- Overview of usual distributions
- Fundamental theorems of probability theory
- Empirical estimation
- Estimating probabilities from data
- Fitting a distribution from data
- Expert estimation
- From data to knowledge
- Estimating probabilities from expert knowledge
- Estimating a distribution from expert knowledge
- Identifying the causal structure of a domain
- Conclusion
- Bayesian networks and influence diagrams
- Introduction to the case
- Introduction to Bayesian networks
- Nodes and variables
- Probabilities
- Dependencies
- Inference
- Learning
- Extension to influence diagrams
- Introduction to Monte Carlo simulation
- Introduction
- Introductory example: structured funds
- Risk management example 1 - hedging weather risk
- Description
- Collecting information
- Model
- Manual scenario
- Monte Carlo simulation
- Summary
- Risk management example 2- potential earthquake in cement industry
- Analysis
- Model
- Monte Carlo simulation
- Conclusion
- A bit of theory
- Introduction
- Definition
- Estimation according to Monte Carlo simulation
- Random variable generation
- Variance reduction
- Software tools
- 3. Quantitative Risk Assessment: A Knowledge Modelling Process
- Introduction
- Increasing awareness of exposures and stakes
- Objectives of risk assessment
- Issues in risk quantification
- Risk quantification: a knowledge management process
- The basel II framework for operational risk
- Introduction
- The three pillars
- Operational risk
- The basic indicator approach
- The sound practices paper
- The standardized approach
- The alternative standardized approach
- The advanced measurement approaches (AMA
- Risk mitigation
- Partial use
- Conclusion
- Identification and mapping of loss exposures
- Quantification of loss exposures
- The candidate scenarios for quantitative risk assessment
- The exposure, occurrence, impact (XOI) model
- Modelling and conditioning exposure at peril
- Summary
- Modelling and conditioning occurrence
- Consistency of exposure and occurrence
- Evaluating the probability of occurrence
- Conditioning the probability of occurrence
- Summary
- Modelling and conditioning impact
- Defining the impact equation
- Defining the distributions of variables involved
- Identifying drivers
- Summary
- Quantifying a single scenario
- An example - "fat fingers" scenar
