Various Authors – Risk Quantification Management Diagnosis and Hedging

Various Authors – Risk Quantification Management Diagnosis and Hedging

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This book offers a practical answer for the non-mathematician to all the questions any businessman always wanted to ask about risk quantification, and never dare to ask.

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Description

Risk Quantification Management Diagnosis and Hedging

By Patrick Naim, Jean-Paul Louisot, and Laurent Condamin.

This book offers a practical answer for the non-mathematician to all the questions any businessman always wanted to ask about risk quantification, and never dare to ask.

The issue of enterprise-wide risk management is important for the board of directors. Stakeholders interests are integrated into the strategic equation with proper implementation. Risk quantification is the cornerstone of effective risk management, covering finance as well as ethics considerations. To set up efficient risk financing mechanisms, both upside and downside risks must be assessed. It is necessary that an optimum return on capital and a reliable protection against bankruptcy be ensured.

Each 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 toOptimise the balance between threats and opportunities, systematic and non systematic risks.

The tool box in the book is designed to equip each board member, each executives and each field manager with the ability to quantify the risks within his/her jurisdiction to all the extend possible and thus make sound, rational and justifiable decisions. Since the 18 it has been used beyond traditional probability analysis. th There are practical illustrations on how to implement new developments within the three steps of risk management, diagnostic, treatment and audit.

The introduction was written by Kevin Knight.

About the Author

LAURENT CONDAMIN is engineer of the French Grande Ecole “Ecole Centrale de Paris”, PhD in Applied Mathematics and Associate in Risk Management (Insurance Institute of America). He is currently partner and managing director of Elseware where he makes consultancy on risk modelling in top leading companies.

There is a person named Jean-Paul Louisot. He is a civil engineer, Master in Economics, Master in Business Administration, and Associate in Risk Management. He has worked for more than thirty years to help private and public entities manage their risks. He is the director of the CARM_institute, and he is in charge of the professional designation. He teaches a postgraduate course in risk management. Jean-Paul teaches in engineering schools. Previous publications include. Diagnostic for exposure. The year 2004 and the year 2004. There are 100 questions to understand risk management. The year 2005.

Patrick NAIM is a man. An associate in risk management was graduated from Ecole Centrale de Paris. He is the founder and CEO of Elseware. He is a professor at several universities and engineering schools in France. He is an author in the field of quantitative modelling.

Table of contents

Forewords.

There is an introduction.

There are 1 foundations.

Principles and practice of risk management.

There are definitions.

There is a systematic and unsystematic risk.

There are insurable risks.

Exposure.

Management.

Risk management.

There are risk management objectives.

The objectives are organizational.

There are other significant objectives.

There is a risk management decision process.

The first step is theDiagnostic of exposures.

The second step is risk treatment.

Audit and corrective actions are part of the third step.

Trends in risk management and the state of the art.

There are risk profiles, risk maps and risk matrix.

There are risk financing and strategic financing.

Risk management goes all the way to strategic risk management.

Property and reputation are related.

From risk manager to chief risk officer.

Why is risk quantified?

Risk quantification is a knowledge-based approach.

There is an introduction.

There is a Causal structure of risk.

A quantitative model of risk is being built.

Frequency and exposure are related.

There are exposure, occurrence, and impact drivers.

It is possible to control exposure, occurrence, and impact.

There are controllable, predictable, observable, and hidden drivers.

The cost of decisions.

There is risk in financing.

The risk management programme is an influence diagram.

Modelling a risk is part of the risk management programme.

Summary.

There are two tool boxes.

The basics of probability.

There is an introduction to probability theory.

There are probabilities.

Independence.

Bayes’ hypothesis.

There are random variables.

There are moments of a variable.

There are continuous random variables.

There are main probability distributions.

The binomial distribution was introduced.

There is an overview of usual distributions.

There are fundamental theorems of probability theory.

It is an empirical estimation.

Estimating probabilities.

A distribution is made from data.

There is an expert estimation.

From data to knowledge.

Estimating probabilities from experts.

Estimating a distribution from experts.

The structure of a domain is identified.

Conclusion.

There are networks that influence diagrams.

The case has an introduction.

There is an introduction to Bayesian networks.

There are variables and variables.

There are probabilities.

There areDependencies.

Inference.

Learning.

Extension to change diagrams.

There is an introduction to Monte Carlo simulation.

There is an introduction.

An example of structured funds.

Hedging weather risk is a risk management example.

There is a description.

Collecting information.

Model.

There is a manual scenario.

Simulation of Monte Carlo.

Summary.

There is a potential earthquake in the cement industry.

Analysis.

Model.

Simulation of Monte Carlo.

Conclusion.

There is a bit of theory.

There is an introduction.

A definition.

Estimation according to a simulation.

There is a random variable generation.

There is a reduction in variability.

There are software tools.

Quantitative Risk Assessment is a knowledge modelling process.

There is an introduction.

Increasing awareness of stakes and exposures.

The objectives are risk assessment.

There are issues in risk quantification.

Risk quantification is a knowledge management process.

The framework for operational risk is called the basel II.

There is an introduction.

There are three pillars.

There is an operational risk.

The basic approach to indicators.

The sound is playing a paper.

The approach is standardized.

There is an alternative standardized approach.

The advanced measurement approaches are used.

The risk is mitigated.

There is partial use.

Conclusion.

Loss exposures can be identified and mapped.

Loss exposures are quantified.

The scenarios are for quantitative risk assessment.

The model includes exposure, occurrence, and impact.

Modelling and conditioning exposure is at risk.

Summary.

Modelling and conditioning occur.

There is a consistency of exposure and occurrence.

The probability of occurrence is evaluated.

The probability of occurrence is conditioned.

Summary.

Modelling and conditioning.

The impact equation is defined.

The distributions of variables are defined.

Identifying drivers.

Summary.

Figuring out a single scenario.

An example of a fat fingers scenario.

Modelling the exposure.

Modelling the event.

Modelling the impact.

There is a quantitative simulation.

Merging scenarios.

Modelling the distribution of losses.

Conclusion.

Risk control drivers are identified.

There is an introduction.

A qualitative view of loss control.

Action on the causes is loss prevention.

The exposure can be eliminated.

The chance of occurrence is reduced.

The action on the consequences is loss reduction.

There is either a pre- event or passive reduction.

There is either an active reduction or a post- event reduction.

There is an introduction to cindynics.

There are basic concepts.

There are problems.

There are general principles and axioms.

There are perspectives.

There is a quantitative example of a flu outbreak.

There is an introduction.

The risk model for the flu.

Exposure.

There is occurrence.

Impact.

The network is called the Bayesian network.

Risk control.

Pre-exposition treatment.

Post-exposition treatment.

Implementation within a network.

There is a strategy comparison.

A point of view.

There is a discussion.

The basel II operational risk is a quantitative example.

The model of individual loss.

Analysing the losses.

The loss control actions are identified.

The impact of loss control actions is analysed.

There is a discussion.

The third example is enterprise-wide risk management.

There is a context and objectives.

Complex systems and risk analysis.

There is an alternative definition of risk.

Representation using networks.

There is a time horizon.

The objectives are identified.

Risk factors and events are identified.

The network is being structured.

Causal links or influences are identified.

The network is quantified.

An example of global enterprise risk representation.

The model is used for loss control.

Risk mapping.

There are important factors.

An analysis of a scenario.

There is an application for the risk management of an industrial plant.

The system has a description.

The external risks have been assessed.

External risks are included in the global risk assessment.

The model is used for risk management.

Quantitative models are used for risk control.

The right cost of risks is the topic of risk financing.

There is an introduction.

There are risk financing instruments.

Retention techniques are used.

Current treatment.

Reserves.

Captives are insurance or reinsurance companies.

Transfer techniques are used.

There is a contractual transfer for risk financing.

Purchase of insurance.

There are hybrid techniques.

There are pools and closed mutuals.

History-based premiums are what the claims are based on.

There is a choice of retention levels.

Financial risks.

There is a prospective aggregate cover.

Capital markets products for risk financing.

Securitization.

There are insurance derivatives.

There are contingent capital arrangements.

Risk financing and risk quantifying.

Quantitative models are being used.

The first example is a satellite launcher.

Defining a housing stock insurance programme is the second example.

There is a representation of financing methods.

There is an introduction.

Building blocks can be risky to finance.

Financing tools are being reexamined.

A financing model is combined with a risk model.

Conclusion.

Index.

Various Authors are available at learnbeacons.store.

Salepage: https://www.wiley.com/en-us/Risk Quantification: Management, Diagnosis and Hedging-p-9780470060438
Archive: https://archive.ph/wip/WGH4A

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