风险量化

出版时间：2006-12  出版社：John Wiley & Sons Inc  作者：Condamin, Laurent/ Louisot, Jean-paul/ Naim, Patrick  页数：271  

内容概要

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.

书籍目录

ForewordsIntroduction.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　Summary2 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 tools3 Quantitative Risk Assessment: A Knowledge Modelling Process4 Identifying Risk Control Drivers5 Risk Financing: The Right Cost of RisksIndex