书籍详情

风险量化:管理、诊断与避险

风险量化:管理、诊断与避险

作者:Laurent Condamin 著

出版社:John Wiley & Sons

出版时间:2006-12-01

ISBN:9780470019078

定价:¥650.43

购买这本书可以去
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.
作者简介
暂缺《风险量化:管理、诊断与避险》作者简介
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
猜您喜欢

读书导航