Details

<p>Preface xiii</p> <p><b>1 What is risk management? 1</b></p> <p>1.1 Introduction 2</p> <p>1.2 Identifying and documenting risk 5</p> <p>1.3 Fallacies and traps in risk management 7</p> <p>1.4 Why safety is different 9</p> <p>1.5 The Basel framework 11</p> <p>1.6 Hold or hedge? 12</p> <p>1.7 Learning from a disaster 13</p> <p>Notes 17</p> <p>References 18</p> <p>Exercises 19</p> <p><b>2 The structure of risk 22</b></p> <p>2.1 Introduction to probability and risk 23</p> <p>2.2 The structure of risk 25</p> <p>2.3 Portfolios and diversification 30</p> <p>2.4 The impact of correlation 40</p> <p>2.5 Using copulas to model multivariate distributions 49</p> <p>Notes 58</p> <p>References 59</p> <p>Exercises 60</p> <p><b>3 Measuring risk 63</b></p> <p>3.1 How can we measure risk? 64</p> <p>3.2 Value at risk 67</p> <p>3.3 Combining and comparing risks 73</p> <p>3.4 VaR in practice 76</p> <p>3.5 Criticisms of VaR 79</p> <p>3.6 Beyond value at risk 82</p> <p>Notes 88</p> <p>References 88</p> <p>Exercises 89</p> <p><b>4 Understanding the tails 92</b></p> <p>4.1 Heavy-tailed distributions 93</p> <p>4.2 Limiting distributions for the maximum 100</p> <p>4.3 Excess distributions 109</p> <p>4.4 Estimation using extreme value theory 115</p> <p>Notes 121</p> <p>References 122</p> <p>Exercises 123</p> <p><b>5 Making decisions under uncertainty 125</b></p> <p>5.1 Decisions, states and outcomes 126</p> <p>5.2 Expected Utility Theory 130</p> <p>5.3 Stochastic dominance and risk profiles 148</p> <p>5.4 Risk decisions for managers 156</p> <p>Notes 160</p> <p>References 161</p> <p>Exercises 162</p> <p><b>6 Understanding risk behavior 164</b></p> <p>6.1 Why decision theory fails 165</p> <p>6.2 Prospect Theory 172</p> <p>6.3 Cumulative Prospect Theory 180</p> <p>6.4 Decisions with ambiguity 189</p> <p>6.5 How managers treat risk 191</p> <p>Notes 194</p> <p>References 194</p> <p>Exercises 195</p> <p><b>7 Stochastic optimization 198</b></p> <p>7.1 Introduction to stochastic optimization 199</p> <p>7.2 Choosing scenarios 212</p> <p>7.3 Multistage stochastic optimization 218</p> <p>7.4 Value at risk constraints 224</p> <p>Notes 228</p> <p>References 228</p> <p>Exercises 229</p> <p><b>8 Robust optimization 232</b></p> <p>8.1 True uncertainty: Beyond probabilities 233</p> <p>8.2 Avoiding disaster when there is uncertainty 234</p> <p>8.3 Robust optimization and the minimax approach 250</p> <p>Notes 261</p> <p>References 262</p> <p>Exercises 263</p> <p><b>9 Real options 265</b></p> <p>9.1 Introduction to real options 266</p> <p>9.2 Calculating values with real options 267</p> <p>9.3 Combining real options and net present value 273</p> <p>9.4 The connection with financial options 278</p> <p>9.5 Using Monte Carlo simulation to value real options 282</p> <p>9.6 Some potential problems with the use of real options 285</p> <p>Notes 287</p> <p>References 287</p> <p>Exercises 288</p> <p><b>10 Credit risk 291</b></p> <p>10.1 Introduction to credit risk 292</p> <p>10.2 Using credit scores for credit risk 294</p> <p>10.3 Consumer credit 301</p> <p>10.4 Logistic regression 308</p> <p>Notes 317</p> <p>References 318</p> <p>Exercises 319</p> <p><b>Appendix A Tutorial on probability theory 323</b></p> <p>A.1 Random events 323</p> <p>A.2 Bayes’ rule and independence 326</p> <p>A.3 Random variables 327</p> <p>A.4 Means and variances 329</p> <p>A.5 Combinations of random variables 332</p> <p>A.6 The normal distribution and the Central Limit Theorem 336</p> <p><b>Appendix B Answers to even-numbered exercises 340</b></p> <p>Index 361</p>

<b>Edward J. Anderson</b><br /><i>The University of Sydney Business School, Australia</i>

<p><b>A comprehensive and accessible introduction to modern quantitative risk management</b>.</p> <p>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.</p> <p><i>Business Risk Management: Models and Analysis</i></p> <ul> <li>Discusses novel modern approaches to risk management</li> <li>Introduces advanced topics in an accessible manner</li> <li>Includes motivating worked examples and exercises (including selected solutions)</li> <li>Is written with the student in mind, and does not assume advanced mathematics</li> <li>Is suitable for self-study by the manager who wishes to better understand this important field.</li> </ul> <p>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.  </p>

GB