Business risk management : models and analysis / Edward Anderson, PhD.

Contents
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 in different
1.5 The Basel framework

2. The structure of risk
2.1 Introduction to probaility and risk
2.2 The Structure of risk
2.3 Portfolios and diversitification
2.4 The Impact of correlation
2.5 Using Couplas to model multivariate distributions

3. Measuring risks
3.1 How can we measure risk?
3.2 Valueat risks
3.3 Combining and comparing risks
3.4 VaR in Practice
3.5 Criticisms of VaR
etc

4. Understanding the tails
4.1 Heavy -tailed distribution
4.2 Limiting distributions for maximum
4.3 Excess distribuion
4.4 Estimation using extreme value theory

5. Making decisions under uncertainty
5.1 Decisions, states and outcomes
5.2 Exected Utility Theory
5.3 Stochastic dominace and risks profile
5.4 Risks decision for managers

6. Understanding risk behavior
6.1 Why decision theory fails
6.2 Propect Theory
6.3 Cummulative prospects
6.4 Decision with ambiguity
6.5 How Managers treat risks

7. Stochastic optimization
7.1 Introduction to stochastic optimization
7.2 Choosing Scenarios
7.3 Multistage stochastic Optimization
7.4 Value at risk constraints

8. Robust Optimization
8.1 True Uncertainty : Beyondprobabilities
8.2 Avoiding disaster when there is uncertainity
8.3 Robust optimization and the minimax approach

9. Real Options
9.1 Introduction to real options
9.2 Calculating values with real options
9.3 Combining real options and net presents
9.4 The connection with financial options
9.5 Using Monte Carlo simulation to value real options

10. Credit risk
10.1 Introduction to credit risk
10.2 using credit scores for credit risk
10.3 Consumer credit
10.4 Logistic regression

Includes bibliographical references and index.

Description based on print version record and CIP data provided by publisher.