| ISBN | 出版时间 | 包装 | 开本 | 页数 | 字数 |
|---|---|---|---|---|---|
| 未知 | 暂无 | 暂无 | 未知 | 0 | 暂无 |
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
4 Identifying Risk Control Drivers
5 Risk Financing: The Right Cost of Risks
Index
