Risk Analysis concerns itself with the quantification of risk, the modeling of identified risks and how to make decisions from those models. Quantitative risk analysis (QRA) using Monte Carlo simulation offers a powerful and precise method for dealing with the uncertainty and variability of a problem. By providing the building blocks the author guides the reader through the necessary steps to produce an accurate risk analysis model and offers general and specific techniques to cope with most modeling problems. A wide range of solved problems is used to illustrate these techniques and how they can be used together to solve otherwise complex problems.

David Vose is senior partner of Vose Consulting, a risk analysis consulting, software and training firm with offices in the US, Europe and Russia. He has worked in risk analysis since 1988 in an extensive range of industry and government problems from insurance, banking, corporate finance, food safety, nuclear power, and epidemiology to oil and gas, construction, utilities, and general commerce. he has co-authored and edited several international guidelines on risk. A charismatic speaker, David gives frequent public and in-house risk analysis seminars. David has served as expert witness in a variety of high profile court cases. A keen squash player, he lives with Veerle and their two children in Ghent, Belgium and dreams of one day owning an old Bentley when there's room in the garage..

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