Risk quantification: management, diagnosis and hedging
Gespeichert in:
Hauptverfasser: | , , |
---|---|
Format: | Buch |
Sprache: | English |
Veröffentlicht: |
Chichester
Wiley
2006
|
Schriftenreihe: | Wiley finance series
|
Schlagworte: | |
Online-Zugang: | Klappentext Inhaltsverzeichnis |
Beschreibung: | XIV, 271 S. graph. Darst. |
ISBN: | 0470019077 9780470019078 |
Internformat
MARC
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Datensatz im Suchindex
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adam_text | Management, Diagnosis and Hedging
Laurent Condamin, Jean-Paul Louisot and Patrick Nairn
This book ofjcrs a practical answer for the non-mathematician to all the
any businessman always wanted to ask about risk quantification,
but never dared to ask.
Enterprise-wide risk management (ERM) is a key issue within companies
worldwide. Its proper implementation ensures transparent governance with all
stakeholders interests integrated into the strategic equation. Risk
quantification is the cornerstone of effective risk management at both a
strategic and tactical level, covering finance as well as ethics considerations.
Both the downside and the upside risks (threats
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 corporate
level to optimise the balance between threats and opportunities, systematic and
non
This book equips the reader with a tool box enabling them to quantify the risks
within their jurisdiction and make rational and justifiable decisions, while
recognising the limits of the exercise. Beyond traditional probability analysis
which has been used since the 18th Century by the insurance community
offers insight into new developments such as Bayesian expert networks and
Monte-Carlo simulation, with practical illustrations on how to implement
them within the three steps of risk management, diagnostic treatment and
audit.
With a foreword by Catherine
Contents
Poreword xi
Introduction
1
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-Diagnosis 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
Frequency
Risk financing and strategic financing
From risk management to strategic risk management
From managing physical assets to managing reputation
From risk manager to chief risk officer
Why is risk quantification needed?
Risk quantification
Introduction
Causal structure of risk
Building a quantitative causal model of risk
Exposure, frequency, and probability
Exposure, occurrence, and impact drivers
Contents
Controlling
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
Probability basics
Introduction to probability theory
Conditional probabilities
Independence
Bayes
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
Description
Collecting information
Model
Contents
Manual
Monte Carlo
Summary
Risk management example
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
Introduction
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
Introduction
The three pillars
Operational risk
The basic indicator approach
The sound practices paper
The standardized approach
The alternative standardized approach
The advanced measurement approaches
Risk mitigation
Partial use
Conclusion
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
Summary
Modelling and conditioning occurrence
Consistency of exposure and occurrence
Evaluating the probability of occurrence
Conditioning the probability of occurrence
Summary
Modelling and conditioning impact
Contents
Defining the
Defining the distributions of variables involved
Identifying drivers
Summary
Quantifying a single scenario
An example
Modelling the exposure
Modelling the occurrence
Modelling the impact
Quantitative simulation
Merging scenarios
Modelling the global distribution of losses
Conclusion
Identifying Risk Control Drivers
Introduction
Loss control
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
Basic concepts
Dysfunctions
General principles and axioms
Perspectives
Quantitative example
Introduction
The influenza pandemic risk model
Exposure
Occurrence
Impact
The Bayesian network
Risk control
Pre
Post-exposition treatment (antiviral drug)
Implementation within a Bayesian network
Strategy comparison
Cumulated point of view
Discussion
Quantitative example
The individual loss model
Analysing the potential severe losses
Identifying the loss control actions
Analysing the cumulated impact of loss control actions
Contents
Discussion
Quantitative
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
Risk mapping
Importance factors
Scenario analysis
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
S
Introduction
Risk financing instruments
Retention techniques
Current treatment
Reserves
Captives (insurance or reinsurance)
Transfer techniques
Contractual transfer (for risk financing
Purchase of insurance cover
Hybrid techniques
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
Securitization
Insurance derivatives
Contingent capital arrangements
Risk financing and risk quantifying
Using quantitative models
Contents
Example
Example
A tentative general representation of financing methods
Introduction
Risk financing building blocks
Usual financing tools revisited
Combining a risk model and a financing model
Conclusion
Index
|
adam_txt |
Management, Diagnosis and Hedging
Laurent Condamin, Jean-Paul Louisot and Patrick Nairn
This book ofjcrs a practical answer for the non-mathematician to all the
any businessman always wanted to ask about risk quantification,
but never dared to ask.
Enterprise-wide risk management (ERM) is a key issue within companies
worldwide. Its proper implementation ensures transparent governance with all
stakeholders' interests integrated into the strategic equation. Risk
quantification is the cornerstone of effective risk management at both a
strategic and tactical level, covering finance as well as ethics considerations.
Both the downside and the upside risks (threats
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 corporate
level to optimise the balance between threats and opportunities, systematic and
non
This book equips the reader with a tool box enabling them to quantify the risks
within their jurisdiction and make rational and justifiable decisions, while
recognising the limits of the exercise. Beyond traditional probability analysis
which has been used since the 18th Century by the insurance community
offers insight into new developments such as Bayesian expert networks and
Monte-Carlo simulation, with practical illustrations on how to implement
them within the three steps of risk management, diagnostic treatment and
audit.
With a foreword by Catherine
Contents
Poreword xi
Introduction
1
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-Diagnosis 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
Frequency
Risk financing and strategic financing
From risk management to strategic risk management
From managing physical assets to managing reputation
From risk manager to chief risk officer
Why is risk quantification needed?
Risk quantification
Introduction
Causal structure of risk
Building a quantitative causal model of risk
Exposure, frequency, and probability
Exposure, occurrence, and impact drivers
Contents
Controlling
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
Probability basics
Introduction to probability theory
Conditional probabilities
Independence
Bayes'
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
Description
Collecting information
Model
Contents
Manual
Monte Carlo
Summary
Risk management example
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
Introduction
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
Introduction
The three pillars
Operational risk
The basic indicator approach
The sound practices paper
The standardized approach
The alternative standardized approach
The advanced measurement approaches
Risk mitigation
Partial use
Conclusion
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
Summary
Modelling and conditioning occurrence
Consistency of exposure and occurrence
Evaluating the probability of occurrence
Conditioning the probability of occurrence
Summary
Modelling and conditioning impact
Contents
Defining the
Defining the distributions of variables involved
Identifying drivers
Summary
Quantifying a single scenario
An example
Modelling the exposure
Modelling the occurrence
Modelling the impact
Quantitative simulation
Merging scenarios
Modelling the global distribution of losses
Conclusion
Identifying Risk Control Drivers
Introduction
Loss control
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
Basic concepts
Dysfunctions
General principles and axioms
Perspectives
Quantitative example
Introduction
The influenza pandemic risk model
Exposure
Occurrence
Impact
The Bayesian network
Risk control
Pre
Post-exposition treatment (antiviral drug)
Implementation within a Bayesian network
Strategy comparison
Cumulated point of view
Discussion
Quantitative example
The individual loss model
Analysing the potential severe losses
Identifying the loss control actions
Analysing the cumulated impact of loss control actions
Contents
Discussion
Quantitative
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
Risk mapping
Importance factors
Scenario analysis
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
S
Introduction
Risk financing instruments
Retention techniques
Current treatment
Reserves
Captives (insurance or reinsurance)
Transfer techniques
Contractual transfer (for risk financing
Purchase of insurance cover
Hybrid techniques
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
Securitization
Insurance derivatives
Contingent capital arrangements
Risk financing and risk quantifying
Using quantitative models
Contents
Example
Example
A tentative general representation of financing methods
Introduction
Risk financing building blocks
Usual financing tools revisited
Combining a risk model and a financing model
Conclusion
Index |
any_adam_object | 1 |
any_adam_object_boolean | 1 |
author | Condamin, Laurent Louisot, Jean-Paul Naïm, Patrick |
author_facet | Condamin, Laurent Louisot, Jean-Paul Naïm, Patrick |
author_role | aut aut aut |
author_sort | Condamin, Laurent |
author_variant | l c lc j p l jpl p n pn |
building | Verbundindex |
bvnumber | BV021590729 |
callnumber-first | H - Social Science |
callnumber-label | HD61 |
callnumber-raw | HD61 |
callnumber-search | HD61 |
callnumber-sort | HD 261 |
callnumber-subject | HD - Industries, Land Use, Labor |
classification_rvk | QP 300 |
ctrlnum | (OCoLC)72354498 (DE-599)BVBBV021590729 |
dewey-full | 658.15/5 |
dewey-hundreds | 600 - Technology (Applied sciences) |
dewey-ones | 658 - General management |
dewey-raw | 658.15/5 |
dewey-search | 658.15/5 |
dewey-sort | 3658.15 15 |
dewey-tens | 650 - Management and auxiliary services |
discipline | Wirtschaftswissenschaften |
discipline_str_mv | Wirtschaftswissenschaften |
format | Book |
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id | DE-604.BV021590729 |
illustrated | Illustrated |
index_date | 2024-07-02T14:44:22Z |
indexdate | 2024-07-09T20:39:23Z |
institution | BVB |
isbn | 0470019077 9780470019078 |
language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014806218 |
oclc_num | 72354498 |
open_access_boolean | |
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owner_facet | DE-355 DE-BY-UBR DE-1102 DE-1051 DE-521 |
physical | XIV, 271 S. graph. Darst. |
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publisher | Wiley |
record_format | marc |
series2 | Wiley finance series |
spelling | Condamin, Laurent Verfasser aut Risk quantification management, diagnosis and hedging Laurent Condamin ; Jean-Paul Louisot ; Patrick Naïm Chichester Wiley 2006 XIV, 271 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley finance series Mathematisches Modell Risk management Mathematical models Risiko (DE-588)4050129-2 gnd rswk-swf Quantifizierung (DE-588)4176601-5 gnd rswk-swf Risikomanagement (DE-588)4121590-4 gnd rswk-swf Schätzung (DE-588)4193791-0 gnd rswk-swf Unternehmen (DE-588)4061963-1 gnd rswk-swf Unternehmen (DE-588)4061963-1 s Risiko (DE-588)4050129-2 s Quantifizierung (DE-588)4176601-5 s Schätzung (DE-588)4193791-0 s DE-604 Risikomanagement (DE-588)4121590-4 s b DE-604 Louisot, Jean-Paul Verfasser aut Naïm, Patrick Verfasser aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014806218&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014806218&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
spellingShingle | Condamin, Laurent Louisot, Jean-Paul Naïm, Patrick Risk quantification management, diagnosis and hedging Mathematisches Modell Risk management Mathematical models Risiko (DE-588)4050129-2 gnd Quantifizierung (DE-588)4176601-5 gnd Risikomanagement (DE-588)4121590-4 gnd Schätzung (DE-588)4193791-0 gnd Unternehmen (DE-588)4061963-1 gnd |
subject_GND | (DE-588)4050129-2 (DE-588)4176601-5 (DE-588)4121590-4 (DE-588)4193791-0 (DE-588)4061963-1 |
title | Risk quantification management, diagnosis and hedging |
title_auth | Risk quantification management, diagnosis and hedging |
title_exact_search | Risk quantification management, diagnosis and hedging |
title_exact_search_txtP | Risk quantification management, diagnosis and hedging |
title_full | Risk quantification management, diagnosis and hedging Laurent Condamin ; Jean-Paul Louisot ; Patrick Naïm |
title_fullStr | Risk quantification management, diagnosis and hedging Laurent Condamin ; Jean-Paul Louisot ; Patrick Naïm |
title_full_unstemmed | Risk quantification management, diagnosis and hedging Laurent Condamin ; Jean-Paul Louisot ; Patrick Naïm |
title_short | Risk quantification |
title_sort | risk quantification management diagnosis and hedging |
title_sub | management, diagnosis and hedging |
topic | Mathematisches Modell Risk management Mathematical models Risiko (DE-588)4050129-2 gnd Quantifizierung (DE-588)4176601-5 gnd Risikomanagement (DE-588)4121590-4 gnd Schätzung (DE-588)4193791-0 gnd Unternehmen (DE-588)4061963-1 gnd |
topic_facet | Mathematisches Modell Risk management Mathematical models Risiko Quantifizierung Risikomanagement Schätzung Unternehmen |
url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014806218&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014806218&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
work_keys_str_mv | AT condaminlaurent riskquantificationmanagementdiagnosisandhedging AT louisotjeanpaul riskquantificationmanagementdiagnosisandhedging AT naimpatrick riskquantificationmanagementdiagnosisandhedging |