风险量化：管理、诊断与避险 RISK QUANTIFICATION - MANAGEMENT, DIAGNOSIS AND HEDGING
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作者： Laurent Condamin著
出 版 社： John Wiley & Sons
出版时间： 2006-12-1字数：版次： 1页数： 271印刷时间： 2006/12/01开本：印次：纸张： 胶版纸I S B N ： 9780470019078包装： 精装编辑推荐
作者简介：
LAURENT CONDAMIN is engineer of the French Grande Ecole “Ecole Centrale de Paris”, PhD in Applied Mathematics and Associate in Risk Management (Insurance Institute of America). He is currently partner and managing director of Elseware where he makes consultancy on risk modelling in top leading companies.
内容简介
Enterprise-wide risk management (ERM) is a key issue for board of directors worldwide. Its proper implementation ensures transparent governance with all stakeholders’ interests integrated into the strategic equation. Furthermore, Risk quantification is the cornerstone of effective risk management,at the strategic and tactical level, covering finance as well as ethics considerations. Both downside and upside risks (threats & opportunities) must be assessed to select the most efficient risk control measures and to set up efficient risk financing mechanisms. Only thus will an optimum return on capital and a reliable protection against bankruptcy be ensured, i.e. long term sustainable development.
Within the ERM framework, each individual operational entity is called upon to control its own risks, within the guidelines set up by the board of directors, whereas the risk financing strategy is developed and implemented at the corporate level to optimise the balance between threats and opportunities, systematic and non systematic risks.
This book is designed to equip each board member, each executives and each field manager, with the tool box enabling them to quantify the risks within his/her jurisdiction to all the extend possible and thus make sound, rational and justifiable decisions, while recognising the limits of the exercise. Beyond traditional probability analysis, used since the 18th Century by the insurance community, it offers insight into new developments like Bayesian expert networks, Monte-Carlo simulation, etc. with practical illustrations on how to implement them within the three steps of risk management, diagnostic, treatment and audit.
With a foreword by Catherine Veret and an introduction by Kevin Knight.
目录
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
