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风险量化：管理、诊断与避险 RISK QUANTIFICATION - MANAGEMENT, DIAGNOSIS AND HEDGING

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

版次: 1

ISBN: 9780470019078

定价: 650.43

装帧: 精装

开本: 其他

纸张: 胶版纸

页数: 271页

分类: 管理

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. 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
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

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8

Risk Quantification: Management, Diagnosis and Hedging（货号TJ）风险量化：管理、诊断和对冲

2006年印刷
印次: 1

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