Risk Analysis - Vose, David

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Résumé :
This book 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....

Biographie:
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.....

Sommaire:
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 i...