Business Risk Management : Models and Analysis.

Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1 What is risk management? -- 1.1 Introduction -- 1.2 Identifying and documenting risk -- 1.3 Fallacies and traps in risk management -- 1.4 Why safety is different -- 1.5 The Basel framework -- 1.6 Hold or hedge? -- 1.7 Learning from a disaster -- 1.7.1 What went wrong? -- Notes -- References -- Exercises -- Chapter 2 The structure of risk -- 2.1 Introduction to probability and risk -- 2.2 The structure of risk -- 2.2.1 Intersection and union risk -- 2.2.2 Maximum of random variables -- 2.3 Portfolios and diversification -- 2.3.1 Adding random variables -- 2.3.2 Portfolios with minimum variance -- 2.3.3 Optimal portfolio theory -- 2.3.4 When risk follows a normal distribution -- 2.4 The impact of correlation -- 2.4.1 Using covariance in combining random variables -- 2.4.2 Minimum variance portfolio with covariance -- 2.4.3 The maximum of variables that are positively correlated -- 2.4.4 Multivariate normal -- 2.5 Using copulas to model multivariate distributions -- 2.5.1 *Details on copula modeling -- Notes -- References -- Exercises -- Chapter 3 Measuring risk -- 3.1 How can we measure risk? -- 3.2 Value at risk -- 3.3 Combining and comparing risks -- 3.4 VaR in practice -- 3.5 Criticisms of VaR -- 3.6 Beyond value at risk -- 3.6.1 *More details on expected shortfall -- Notes -- References -- Exercises -- Chapter 4 Understanding the tails -- 4.1 Heavy-tailed distributions -- 4.1.1 Defining the tail index -- 4.1.2 Estimating the tail index -- 4.1.3 *More details on the tail index -- 4.2 Limiting distributions for the maximum -- 4.2.1 *More details on maximum distributions and Fisher-Tippett -- 4.3 Excess distributions -- 4.3.1 *More details on threshold exceedances -- 4.4 Estimation using extreme value theory -- 4.4.1 Step 1. Choose a threshold u.

4.4.2 Step 2. Estimate the parameters ξ and β -- 4.4.3 Step 3. Estimate the risk measures of interest -- Notes -- References -- Exercises -- Chapter 5 Making decisions under uncertainty -- 5.1 Decisions, states and outcomes -- 5.1.1 Decisions -- 5.1.2 States -- 5.1.3 Outcomes -- 5.1.4 Probabilities -- 5.1.5 Values -- 5.2 Expected Utility Theory -- 5.2.1 Maximizing expected profit -- 5.2.2 Expected utility -- 5.2.3 No alternative to Expected Utility Theory -- 5.2.4 *A sketch proof of the theorem -- 5.2.5 What shape is the utility function? -- 5.2.6 *Expected utility when probabilities are subjective -- 5.3 Stochastic dominance and risk profiles -- 5.3.1 *More details on stochastic dominance -- 5.4 Risk decisions for managers -- 5.4.1 Managers and shareholders -- 5.4.2 A single company-wide view of risk -- 5.4.3 Risk of insolvency -- Notes -- References -- Exercises -- Chapter 6 Understanding risk behavior -- 6.1 Why decision theory fails -- 6.1.1 The meaning of utility -- 6.1.2 Bounded rationality -- 6.1.3 Inconsistent choices under uncertainty -- 6.1.4 Problems from scaling utility functions -- 6.2 Prospect Theory -- 6.2.1 Foundations for behavioral decision theory -- 6.2.2 Decision weights and subjective values -- 6.3 Cumulative Prospect Theory -- 6.3.1 *More details on Prospect Theory -- 6.3.2 Applying Prospect Theory -- 6.3.3 Why Prospect Theory does not always predict well -- 6.4 Decisions with ambiguity -- 6.5 How managers treat risk -- Notes -- References -- Exercises -- Chapter 7 Stochastic optimization -- 7.1 Introduction to stochastic optimization -- 7.1.1 A review of optimization -- 7.1.2 Two-stage recourse problems -- 7.1.3 Ordering with stochastic demand -- 7.2 Choosing scenarios -- 7.2.1 How to carry out Monte Carlo simulation -- 7.2.2 Alternatives to Monte Carlo -- 7.3 Multistage stochastic optimization.

7.3.1 Non-anticipatory constraints -- 7.4 Value at risk constraints -- Notes -- References -- Exercises -- Chapter 8 Robust optimization -- 8.1 True uncertainty: Beyond probabilities -- 8.2 Avoiding disaster when there is uncertainty -- 8.2.1 *More details on constraint reformulation -- 8.2.2 Budget of uncertainty -- 8.2.3 *More details on budgets of uncertainty -- 8.3 Robust optimization and the minimax approach -- 8.3.1 *Distributionally robust optimization -- Notes -- References -- Exercises -- Chapter 9 Real options -- 9.1 Introduction to real options -- 9.2 Calculating values with real options -- 9.2.1 *Deriving the formula for the surplus with a normal distribution -- 9.3 Combining real options and net present value -- 9.4 The connection with financial options -- 9.5 Using Monte Carlo simulation to value real options -- 9.6 Some potential problems with the use of real options -- Notes -- References -- Exercises -- Chapter 10 Credit risk -- 10.1 Introduction to credit risk -- 10.2 Using credit scores for credit risk -- 10.2.1 A Markov chain analysis of defaults -- 10.3 Consumer credit -- 10.3.1 Probability, odds and log odds -- 10.4 Logistic regression -- 10.4.1 *More details on logistic regression -- 10.4.2 Building a scorecard -- 10.4.3 Other scoring applications -- Notes -- References -- Exercises -- Appendix A Tutorial on probability theory -- A.1 Random events -- A.2 Bayes' rule and independence -- A.3 Random variables -- A.4 Means and variances -- A.5 Combinations of random variables -- A.6 The normal distribution and the Central Limit Theorem -- Appendix B Answers to even-numbered exercises -- Index.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.