Risk Analysis: A Quantitative Guide / Edition 2

Risk Analysis: A Quantitative Guide / Edition 2

by David Vose
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Risk Analysis: A Quantitative Guide / Edition 2

Risk Analysis: A Quantitative Guide is a comprehensive guide for the risk analyst and decision maker. Based on the author's extensive experience in solving real-world risk problems, this book is an invaluable aid to the risk analysis practitioner. By providing the building blocks of risk-based thinking the author guides the reader through the steps necessary to produce a realistic risk analysis and offers general and specific techniques to cope with most common and challenging risk modelling problems. A wide range of solved examples is used to illustrate these techniques and how they can be put together to make the best possible risk-based decisions.

The third edition of this highly regarded text has been thoroughly updated and expanded considerably with five new chapters for the risk manager, including how to plan and assess the quality of a risk analysis, as well as new chapters for the risk analysis modeller on summation of random variables, causality, optimization, insurance and finance modelling, forecasting, model validation and common errors, capital investment and microbial risk assessment. This new edition provides a greater focus on business and includes applications in a wide range of different settings.

About the Author:
David Vose is senior partner of Vose Consulting, a risk analysis consulting, software and training firm with offices in the US, Europe and Russia

Product Details

ISBN-13: 9780471997658
Publisher: Wiley
Publication date: 01/28/1978
Edition description: REV
Pages: 430
Product dimensions: 7.72(w) x 9.92(h) x 1.13(d)

Table of Contents

Preface     xiii
Introduction     1
Why do a risk analysis?     3
Moving on from "What If" Scenarios     3
The Risk Analysis Process     5
Risk Management Options     7
Evaluating Risk Management Options     10
Inefficiencies in Transferring Risks to Others     11
Risk Registers     13
Planning a risk analysis     21
Questions and Motives     21
Determine the Assumptions that are Acceptable or Required     22
Time and Timing     23
You'll Need a Good Risk Analyst or Team     23
The quality of a risk analysis     29
The Reasons Why a Risk Analysis can be Terrible     29
Communicating the Quality of Data Used in a Risk Analysis     31
Level of Criticality     34
The Biggest Uncertainty in a Risk Analysis     35
Iterate     36
Choice of model structure     37
Software Tools and the Models they Build     37
Calculation Methods     42
Uncertainty and Variability     47
How Monte Carlo Simulation Works     57
Simulation Modelling     63
Understanding and using the results of arisk analysis     67
Writing a Risk Analysis Report     67
Explaining a Model's Assumptions     69
Graphical Presentation of a Model's Results     70
Statistical Methods of Analysing Results     91
Introduction     109
Probability mathematics and simulation     115
Probability Distribution Equations     115
The Definition of "Probability"     118
Probability Rules     119
Statistical Measures     137
Building and running a model     145
Model Design and Scope     145
Building Models that are Easy to Check and Modify     146
Building Models that are Efficient     147
Most Common Modelling Errors     159
Some basic random processes     167
Introduction     167
The Binomial Process     167
The Poisson Process     176
The Hypergeometric Process     183
Central Limit Theorem     188
Renewal Processes     190
Mixture Distributions     193
Martingales     194
Miscellaneous Examples     194
Data and statistics     207
Classical Statistics      208
Bayesian Inference     215
The Bootstrap     246
Maximum Entropy Principle     254
Which Technique Should You Use?     255
Adding uncertainty in Simple Linear Least-Squares Regression Analysis     256
Fitting distributions to data     263
Analysing the Properties of the Observed Data     264
Fitting a Non-Parametric Distribution to the Observed Data     269
Fitting a First-Order Parametric Distribution to Observed Data     281
Fitting a Second-Order Parametric Distribution to Observed Data     297
Sums of random variables     301
The Basic Problem     301
Aggregate Distributions     305
Forecasting with uncertainty     321
The Properties of a Time Series Forecast     322
Common Financial Time Series Models     327
Autoregressive Models     335
Markov Chain Models     339
Birth and Death Models     343
Time Series Projection of Events Occurring Randomly in Time     345
Time Series Models with Leading Indicators     348
Comparing Forecasting Fits for Different Models     351
Long-Term Forecasting     352
Modelling correlation and dependencies      353
Introduction     353
Rank Order Correlation     356
Copulas     367
The Envelope Method     380
Multiple Correlation Using a Look-Up Table     391
Eliciting from expert opinion     393
Introduction     393
Sources of Error in Subjective Estimation     394
Modelling Techniques     401
Calibrating Subject Matter Experts     412
Conducting a Brainstorming Session     414
Conducting the Interview     416
Testing and modelling causal relationships     423
Campylobacter Example     424
Types of Model to Analyse Data     426
From Risk Factors to Causes     427
Evaluating Evidence     429
The Limits of Causal Arguments     429
An Example of a Qualitative Causal Analysis     430
Is Causal Analysis Essential?     434
Optimisation in risk analysis     435
Introduction     435
Optimisation Methods     436
Risk Analysis Modelling and Optimisation     439
Working Example: Optimal Allocation of Mineral Pots     444
Checking and validating a model     451
Spreadsheet Model Errors     451
Checking Model Behaviour     456
Comparing Predictions Against Reality     460
Discounted cashflow modelling     461
Useful Time Series Models of Sales and Market Size     463
Summing Random Variables     466
Summing Variable Margins on Variable Revenues     467
Financial Measures in Risk Analysis     469
Project risk analysis     473
Cost Risk Analysis     474
Schedule Risk Analysis     478
Portfolios of risks     486
Cascading Risks     487
Insurance and finance risk analysis modelling     493
Operational Risk Modelling     493
Credit Risk     494
Credit Ratings and Markov Chain Models     499
Other Areas of Financial Risk     503
Measures of Risk     503
Term Life Insurance     506
Accident Insurance     509
Modelling a Correlated Insurance Portfolio     511
Modelling Extremes     512
Premium Calculations     513
Microbial food safety risk assessment     517
Growth and Attenuation Models     519
Dose-Response Models     527
Is Monte Carlo Simulation the Right Approach?     532
Some Model Simplifications     533
Animal import risk assessment     537
Testing for an Infected Animal     539
Estimating True Prevalence in a Population     544
Importing Problems     553
Confidence of Detecting an Infected Group     556
Miscellaneous Animal Health and Food Safety Problems     559
Guide for lecturers     567
About ModelRisk     569
A compendium of distributions     585
Discrete and Continuous Distributions     585
Bounded and Unbounded Distributions     586
Parametric and Non-Parametric Distributions     587
Univariate and Multivariate Distributions     588
Lists of Applications and the Most Useful Distributions     588
How to Read Probability Distribution Equations     593
The Distributions     599
Introduction to Creating Your Own Distributions     696
Approximation of One Distribution with Another     703
Recursive Formulae for Discrete Distributions     710
A Visual Observation On The Behaviour Of Distributions     713
Further reading     715
Vose Consulting      721
References     725
Index     729

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