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.
作者簡介
暫缺《風險量化：管理、診斷與避險》作者簡介
圖書目錄
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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