Risk Analysis: A Quantitative Guideis a comprehensive guide for eh 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-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 technique 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 risk analysis, as well as new chapters for this 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.

Key Features:

• Breaks down techniques into types of modelling issues (like distribution fitting, correlation and time series forecasting) and then applies them with easy-to-follow examples.
• Explains powerful and proven Monte Carlo simulation and numerical techniques for dealing with uncertainty.
• Includes recent innovations in modelling like fast Fourier transforms and copulas.
• Over 150 examples models and over 400 illustrations.
• Written in an informal manner with a practical rather than academic focus.
• Discusses the planning, uses and abuses of risk analysis.
• Includes a compendium of almost eighty distribution types and their uses.

'Risk Analysis: A Quantitative Guideconcerns itself with the quantification of risk, the modelling 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 modelling 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.

The third edition of Risk Analysis: A Quantitative Guidehas been very thoroughly updated and extended with new chapters being added on optimization, insurance modelling, forecasting financial markets, human health, and risk analysis and Six Sigma.It also provides a greater focus on business and includes applications in a range of different settings, e.g. finance, food, pharma, tobacco and healthcare.

Key Features:

• Based on the author’s wide practical experience of developing risk analysis models and also his years of running training seminars for a wide variety of organizations. 
• Uses Monte Carlo simulation as a powerful and proven method for dealing with uncertainty.
• Written in an informal manner and provides a practical guide for the analyst.
• Discusses the uses and abuses of risk analysis in the food, pharma, tobacco and healthcare industries.
• Brings together the theory for different industries or typical industry problems so that a reader can quickly find what is useful to them.

'

Risk Analysisconcerns 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.

Includes bibliographical references (p. [411]) and index.

David Vose is an independent consultant specialising in Monte Carlo risk analysis with eleven years' experience in simulation modelling. He has used risk analysis in an extensive range of industry and government problems, from food safety, nuclear power, and epidemiology to foreign exchange risk, oil and gas, construction, utilities, and general commerce. David Vose is based in Europe but consults globally. He provides advice on specific and general risk issues, produces risk assessment models and lectures and runs public, in-house and web-based training seminars in various aspects of risk analysis modelling.

Preface.

Part 1: Introduction.

1. Why do a risk analysis?

1.1. Moving on from “What If” Scenarios.

1.2. The Risk Analysis Process.

1.3. Risk Management Options.

1.4. Evaluating Risk Management Options.

1.5. Inefficiencies in Transferring Risks to Others.

1.6. Risk Registers.

2. Planning a risk analysis.

2.1. Questions and Motives.

2.2. Determine the Assumptions that are Acceptable or Required.

2.3. Time and Timing.

2.4. You’ll Need a Good Risk Analyst or Team.

3. The quality of a risk analysis.

3.1. The Reasons Why a Risk Analysis can be Terrible.

3.2. Communicating the Quality of Data Used in a Risk Analysis.

3.3. Level of Criticality.

3.4. The Biggest Uncertainty in a Risk Analysis.

3.5. Iterate.

4. Choice of model structure.

4.1. Software Tools and the Models they Build.

4.2. Calculation Methods.

4.3. Uncertainty and Variability.

4.4. How Monte Carlo Simulation Works.

4.5. Simulation Modelling.

5. Understanding and using the results of a risk analysis.

5.1. Writing a Risk Analysis Report.

5.2. Explaining a Model’s Assumptions.

5.3. Graphical Presentation of a Model’s Results.

5.4. Statistical Methods of Analysing Results.

Part 2: Introduction.

6. Probability mathematics and simulation.

6.1. Probability Distribution Equations.

6.2. The Definition of “Probability”.

6.3. Probability Rules.

6.4. Statistical Measures.

7. Building and running a model.

7.1. Model Design and Scope.

7.2. Building Models that are Easy to Check and Modify.

7.3. Building Models that are Efficient.

7.4. Most Common Modelling Errors.

8. Some basic random processes.

8.1. Introduction.

8.2. The Binomial Process.

8.3. The Poisson Process.

8.4. The Hypergeometric Process.

8.5. Central Limit Theorem.

8.6. Renewal Processes.

8.7. Mixture Distributions.

8.8. Martingales.

8.9. Miscellaneous Example.

9. Data and statistics.

9.1. Classical Statistics.

9.2. Bayesian Inference.

9.3. The Bootstrap.

9.4. Maximum Entropy Principle.

9.5. Which Technique Should You Use?

9.6. Adding uncertainty in Simple Linear Least-Squares Regression Analysis.

10. Fitting distributions to data.

10.1. Analysing the Properties of the Observed Data.

10.2. Fitting a Non-Parametric Distribution to the Observed Data.

10.3. Fitting a First-Order Parametric Distribution to Observed Data.

10.4. Fitting a Second-Order Parametric Distribution to Observed Data.

11. Sums of random variables.

11.1. The Basic Problem.

11.2. Aggregate Distributions.

12. Forecasting with uncertainty.

12.1. The Properties of a Time Series Forecast.

12.2. Common Financial Time Series Models.

12.3. Autoregressive Models.

12.4. Markov Chain Models.

12.5. Birth and Death Models.

12.6. Time Series Projection of Events Occurring Randomly in Time.

12.7. Time Series Models with Leading Indicators.

12.8. Comparing Forecasting Fits for Different Models.

12.9. Long-Term Forecasting.

13. Modelling correlation and dependencies.

13.1. Introduction.

13.2. Rank Order Correlation.

13.3. Copulas.

13.4. The Envelope Method.

13.5. Multiple Correlation Using a Look-Up Table.

14. Eliciting from expert opinion.

14.1. Introduction.

14.2. Sources of Error in Subjective Estimation.

14.3. Modelling Techniques.

14.4. Calibrating Subject Matter Experts.

14.5. Conducting a Brainstorming Session.

14.6. Conducting the Interview.

15. Testing and modelling causal relationships.

15.1. CampylobacterExample.

15.2. Types of Model to Analyse Data.

15.3. From Risk Factors to Causes.

15.4. Evaluating Evidence.

15.5. The Limits of Causal Arguments.

15.6. An Example of a Qualitative Causal Analysis.

15.7. Is Causal Analysis Essential?

16. Optimisation in risk analysis.

16.1. Introduction.

16.2. Optimisation Methods.

16.3. Risk Analysis Modelling and Optimisation.

16.4. Working Example: Optimal Allocation of Mineral Pots.

17. Checking and validating a model.

17.1. Spreadsheet Model Errors.

17.2. Checking Model Behaviour.

17.3. Comparing Predictions Against Reality.

18. Discounted cashflow modelling.

18.1. Useful Time Series Models of Sales and Market Size.

18.2. Summing Random Variables.

18.3. Summing Variable Margins on Variable Revenues.

18.4. Financial Measures in Risk Analysis.

19. Project risk analysis.

19.1. Cost Risk Analysis.

19.2. Schedule Risk Analysis.

19.3. Portfolios of risks.

19.4. Cascading Risks.

20. Insurance and finance risk analysis modelling.

20.1. Operational Risk Modelling.

20.2. Credit Risk.

20.3. Credit Ratings and Markov Chain Models.

20.4. Other Areas of Financial Risk.

20.5. Measures of Risk.

20.6. Term Life Insurance.

20.7. Accident Insurance.

20.8. Modelling a Correlated Insurance Portfolio.

20.9. Modelling Extremes.

20.10. Premium Calculations.

21. Microbial food safety risk assessment.

21.1. Growth and Attenuation Models.

21.2. Dose–Response Models.

21.3. Is Monte Carlo Simulation the Right Approach?

21.4. Some Model Simplifications.

22. Animal import risk assessment.

22.1. Testing for an Infected Animal.

22.2. Estimating True Prevalence in a Population.

22.3. Importing Problems.

22.4. Confidence of Detecting an Infected Group.

22.5. Miscellaneous Animal Health and Food Safety Problems.

I. Guide for lecturers.

II. About ModelRisk.

III. A compendium of distributions.

III.1. Discrete and Continuous Distributions.

III.2. Bounded and Unbounded Distributions.

III.3. Parametric and Non-Parametric Distributions.

III.4. Univariate and Multivariate Distributions.

III.5. Lists of Applications and the Most Useful Distributions.

III.6. How to Read Probability Distribution Equations.

III.7. The Distributions.

III.8. Introduction to Creating Your Own Distributions.

III.9. Approximation of One Distribution with Another.

III.10. Recursive Formulae for Discrete Distributions.

III.11. A Visual Observation On The Behaviour Of Distributions.

IV. Further reading.

V. Vose Consulting.

References.

Index.