Risk Quantification: Management, Diagnosis and Hedging
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.
Risk management: principles and practice.
Systematic and unsystematic risk.
Risk management 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.
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 management programme as an influence diagram.
Modelling an individual risk or the risk management programme.
2 Tool Box.
Introduction to probability theory.
Moments of a random variable.
Continuous random variables.
Main probability distributions.
Introduction–the binomial distribution.
Overview of usual distributions.
Fundamental theorems of probability theory.
Estimating probabilities from data.
Fitting a distribution from data.
From data to knowledge.
Estimating probabilities from expert knowledge.
Estimating a distribution from expert knowledge.
Identifying the causal structure of a domain.
Bayesian networks and influence diagrams.
Introduction to the case.
Introduction to Bayesian networks.
Nodes and variables.
Extension to influence diagrams.
Introduction to Monte Carlo simulation.
Introductory example: structured funds.
Risk management example 1 – hedging weather risk.
Monte Carlo simulation.
Risk management example 2– potential earthquake in cement industry.
Monte Carlo simulation.
A bit of theory.
Estimation according to Monte Carlo simulation.
Random variable generation.
3 Quantitative Risk Assessment: A Knowledge Modelling Process.
Increasing awareness of exposures and stakes.
Objectives of risk assessment.
Issues in risk quantification.
Risk quantification: a knowledge management process.
The basel II framework for operational risk.
The three pillars.
The basic indicator approach.
The sound practices paper.
The standardized approach.
The alternative standardized approach.
The advanced measurement approaches (AMA).
Identification and mapping of loss exposures.
Quantification of loss exposures.
The candidate scenarios for quantitative risk assessment.
The exposure, occurrence, impact (XOI) model.
Modelling and conditioning exposure at peril.
Modelling and conditioning occurrence.
Consistency of exposure and occurrence.
Evaluating the probability of occurrence.
Conditioning the probability of occurrence.
Modelling and conditioning impact.
Defining the impact equation.
Defining the distributions of variables involved.
Quantifying a single scenario.
An example – “fat fingers” scenario.
Modelling the exposure.
Modelling the occurrence.
Modelling the impact.
Modelling the global distribution of losses.
4 Identifying Risk Control Drivers.
Loss control – a qualitative view.
Loss prevention (action on the causes).
Eliminating the exposure.
Reducing the probability of occurrence.
Loss reduction (action on the consequences).
Pre-event or passive reduction.
Post-event or active reduction.
An introduction to cindynics.
General principles and axioms.
Quantitative example 1 – pandemic influenza.
The influenza pandemic risk model.
The Bayesian network.
Pre-exposition treatment (vaccination).
Post-exposition treatment (antiviral drug).
Implementation within a Bayesian network.
Cumulated point of view.
Quantitative example 2 – basel II operational risk.
The individual loss model.
Analysing the potential severe losses.
Identifying the loss control actions.
Analysing the cumulated impact of loss control actions.
Quantitative example 3 – enterprise-wide risk management.
Context and objectives.
Risk analysis and complex systems.
An alternative definition of risk.
Representation using Bayesian networks.
Selection of a time horizon.
Identification of objectives.
Identification of risks (events) and risk factors (context).
Structuring the network.
Identification of relationships (causal links or influences).
Quantification of the network.
Example of global enterprise risk representation.
Usage of the model for loss control.
Application to the risk management of an industrial plant.
Description of the system.
Assessment of the external risks.
Integration of external risks in the global risk assessment.
Usage of the model for risk management.
Summary – using quantitative models for risk control.
5 Risk Financing: The Right Cost of Risks.
Risk financing instruments.
Captives (insurance or reinsurance).
Contractual transfer (for risk financing – to a noninsurer).
Purchase of insurance cover.
Pools and closed mutual.
Claims history-based premiums.
Choice of retention levels.
Financial reinsurance and finite risks.
Prospective aggregate cover.
Capital markets products for risk financing.
Contingent capital arrangements.
Risk financing and risk quantifying.
Using quantitative models.
Example 1: Satellite launcher.
Example 2: Defining a housing stock insurance programme.
A tentative general representation of financing methods.
Risk financing building blocks.
Usual financing tools revisited.
Combining a risk model and a financing model.
JEAN-PAUL LOUISOT is a civil engineer, Master in Economics, Master in Business Administration (Kellog, 1972) and Associate in Risk Management. He has spent more than thirty years of his career to service private and public entities helping them manage their risks and coach their risk managers and executives. As director for the CARM_institute, Ltd, he is in charge of the professional designations ARM and EFARM. As a Professor at Panthéon/Sorbonne University, he teaches a postgraduate course in Risk Management. Jean-Paul teaches also in various Engineering Schools and MBA programs. Previous publications include Exposure Diagnostic (AFNOR – 2004) and 100 Questions to understand Risk Management (AFNOR – 2005).
PATRICK NAIM graduated from Ecole Centrale de Paris, and Associate in Risk Management (ARM). He is the founder and CEO of Elseware, a consulting company specialising in quantitative modelling and risk quantification. He also teaches data modelling and Bayesian Networks in several universities and engineering schools in France. He is author of several books in the field of quantitative modelling.
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