Risk Analysis_ A Quantitative Guide

Risk Analysis_ A Quantitative Guide Risk Analysis_ A Quantitative GuideRisk analysisA QUANTITATIVE GUIDE THIRD EDITIONDavid voseTable of contentsTitle PageCopyrightDedicationrefacePart i: introductionChapter 1: Why do a risk analysis?1. Moving on fron“ What if”cevallos1.2 The Risk Analysis Process3 Risk Management Options1. 4 Evaluating Risk Management Options1.5 Inefficiencies in Transferring Risks to Others1. 6 Risk RegistersChapter 2: Planning a risk analysis2.1 Questions and Motives2.2 Determine the Assumptions that are Acceptable orReguiredTime and Timing2. 4 You 'll Need a Good Risk Analyst or TeamChapter 3: The quality of a risk analysis3.1 The Reasons why a risk Analysis can be Terrible3.2 Communicating the Ouality of Data Used in a RiskAn3.3 Level of criticality3. 4 The Biggest Uncertainty in a Risk Analysis3.5 derateChapter 4: Choice of model structure4.1 Software Tools and the Models they Build4.2 Calculation methods4.3 Uncertainty and variability4. 4 How Monte carlo simulation works4.5 Simulation ModellingChapter 5: Understanding and using the results of a riskanaLvsiS5.1 Writing a Risk Analysis report5.2 Explaining a Model's assumptions5.3 Graphical Presentation of a Model's results5.4 Statistical Methods of Analysing ResultsPart 2: IntroductionChapter 6: Probability mathematics and simulation6.1 Probability Distribution Equations62 The Definition of“ Probability”6.3 Probability rules6.4 Statistical measuresChapter 7: Building and running a modeZI Model Design and scope7.2 Building Models that are Easy to Check and Modifi1.3 Building Models that are Efficient7.4 Most Common Modelling ErrorsChapter 8: Some basic random processes8.ntroduction8.2 The binomial process8. 3 The poisson process8. 4 The Hypergeometric Process8. 5 Central limit heorem8.6 Renewal processes8.Mixture Distributions8.8 Martingales8.9 Miscellaneous ExamplesChapter 9: Data and statistics9.1 Classical statistics9.2 Bayesian Inference9.33 The bootstrap9 4 Maximum Entropy principle9.5 Which Technique Should You Use?9.6 Adding uncertainty in Simple linear least-SquaresRegression AnalysisChapter 10: Fitting distributions to data10. 1 Analysing the Properties of the Observed data10.2 Fitting a Non-Parametric Distribution to the6Observed dData10.3 Fitting a First-Order parametric Distribution toObserved data10. 4 Fitting a second-Order parametric Distribution toObserved dataChapter 11: Sums of random variables11.I The Basic problem11.2 Aggregate DistributionsChapter 12: Forecasting with uncertainty2.1 The properties of a Time Series Forecast12.2 Common financial Time series models12.3 Autoregressive Models12. 4 Markov chain models12.5 Birth and death models12.6 Time series Proiection of Events OccurringRandomly in Time12/Time Series Models with leading Indicators12.8 Comparing Forecasting Fits for Different Models12.9 Long-Term ForecastingChapter 13: Modelling correlation and dependencies3.ntroduction13. 2 Rank Order Correlation13.3 Copulas13. 4 The Envelope Method13. 5 Multiple Correlation Using a Look-Up TableChapter 14: Eliciting from expert opinion14.Introduction14.2 Sources of Error in Subjective estimation14.3 Modelling Techniques14. 4 Calibrating Subject Matter Experts14.5 Conducting a Brainstorming Session14.6 Conducting the InterviewChapter 15: Testing and modelling causal relationships15. I Campylobacter example15.2 Types of model to Analyse dat15.3 From risk factors to Causes15.4 Evaluating evidence15. 5 The Limits of causal arguments15. 6 An Example of a Qualitative Causal Analysis15.7 ls Causal analysis essential?Chapter 16: Optimisation in risk analysis16。1 Introduction16.2 Optimisation Methods16.3 Risk Analysis Modelling and Optimisation16. 4 Working Example: Optimal allocation of mineralPaotsChapter 17: Checking and validating a model17. I Spreadsheet Model errors17.2 Checking Model behaviour17.3 Comparing Predictions Against Reality8Chapter 18: Discounted cashflow modelling18. 1 Useful Time Series Models of sales and Market18.2 Summing Random variables18. 3 Summing Variable Moargins on variable revenues18 4 Financial Measures in Risk AnalysisChapter 19: Proiect risk analysis19. 1 Cost Risk Analysis19.2 Schedule risk ar19.3 Portfolios of risks19. 4 Cascading risksChapter 20: Insurance and finance risk analysis modelling20.1 Operational risk modelling20.2 Credit risk20.3 Credit Ratings and Markow Chain Models20. 4 Other Areas of financial risk20.5 Measures of risk20.6 Term Life Insurance20.7 Accident Insurance20.8 Modelling a Correlated insurance portfolio20.9 Modelling Extremes20.10 Premium CalculationsChapter 21: Microbial food safety risk assessment21.1 Growth and Attenuation models21.2 Dose-Response Models21.3 s Monte Carlo simulation the Right Approach?21. 4 Some Model simplificationsChapter 22: Animal import risk assessment22 1 Testing for an Infected animal22. 2 Estimating True Prevalence in a Population22.3 Importing Problems22. 4 Confidence of Detecting an Infected Group22.5 Miscellaneous Animal Health and Food safetyProblemsAppendix l Guide for lecturersAppendix l: about ModelriskAppendix l: A compendium of distributionsII. Discrete and continuous distributions1.2 Bounded and Unbounded Distributionsl. 3 Parametric and Non-Parametric Distributionsl. 4 Univariate and multivariate distributions1.5 Lists of applications andthe Most usefulDistributions11.6 How to Read Probability Distribution Equationsl. The distributions11.8 Introduction to Creating Your Own Distributions11.9 Approximation of one Distribution with Another11.10 Recursive Formulae for Discrete DistributionsI.1 A Visual Observation On The behaviour ofDistributions10