Business Risk Management Models and Analysis

A comprehensive and accessible introduction to modern quantitative risk management.

The business world is rife with risk and uncertainty, and risk management is a vitally important topic for managers. The best way to achieve a clear understanding of risk is to use quantitative tools and probability models.  Written for students, this book has a quantitative emphasis but is accessible to those without a strong mathematical background.

Business Risk Management: Models and Analysis

• Discusses novel modern approaches to risk management
• Introduces advanced topics in an accessible manner
• Includes motivating worked examples and exercises (including selected solutions)
• Is written with the student in mind, and does not assume advanced mathematics
• Is suitable for self-study by the manager who wishes to better understand this important field. 

Aimed at postgraduate students, this book is also suitable for senior undergraduates, MBA students, and all those who have a general interest in business risk.  

Preface

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

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 modelling

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

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 detail 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

5 Making Decisions Under Uncertainty

5.1 Decisions, states and outcomes

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

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

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 Multi-stage stochastic optimization

7.3.1 Non-anticipatory constraints

7.4 Value at risk constraints

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

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

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-dds

10.4 Logistic Regression

10.4.1 * More details on logistic regression

10.4.2 Building a scorecard

10.4.3 Other scoring applications

11 Appendix: Tutorial on Probability Theory

11.1 Random events

11.2 Bayes Rule and Independence

11.3 Random variables

11.4 Means and variances

11.5 Combinations of random variables

11.6 The normal distribution and the central limit theorem

Index

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.