Details

Mathematics and Statistics for Financial Risk Management


Mathematics and Statistics for Financial Risk Management


Wiley Finance 2. Aufl.

von: Michael B. Miller

CHF 61.00

Verlag: Wiley
Format: PDF
Veröffentl.: 12.12.2013
ISBN/EAN: 9781118757550
Sprache: englisch
Anzahl Seiten: 336

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Beschreibungen

<p><i>Mathematics and Statistics for Financial Risk Management</i> is a practical guide to modern financial risk management for both practitioners and academics.</p> <p>Now in its second edition with more topics, more sample problems and more real world examples, this popular guide to financial risk management introduces readers to practical quantitative techniques for analyzing and managing financial risk.</p> <p>In a concise and easy-to-read style, each chapter introduces a different topic in mathematics or statistics. As different techniques are introduced, sample problems and application sections demonstrate how these techniques can be applied to actual risk management problems. Exercises at the end of each chapter and the accompanying solutions at the end of the book allow readers to practice the techniques they are learning and monitor their progress. A companion Web site includes interactive Excel spreadsheet examples and templates.</p> <p><i>Mathematics and Statistics for Financial Risk Management</i> is an indispensable reference for today’s financial risk professional.</p>
<p>Preface ix</p> <p>What’s New in the Second Edition xi</p> <p>Acknowledgments xiii</p> <p><b>Chapter 1 Some Basic Math 1</b></p> <p>Logarithms 1</p> <p>Log Returns 2</p> <p>Compounding 3</p> <p>Limited Liability 4</p> <p>Graphing Log Returns 5</p> <p>Continuously Compounded Returns 6</p> <p>Combinatorics 8</p> <p>Discount Factors 9</p> <p>Geometric Series 9</p> <p>Problems 14</p> <p><b>Chapter 2 Probabilities 15</b></p> <p>Discrete Random Variables 15</p> <p>Continuous Random Variables 15</p> <p>Mutually Exclusive Events 21</p> <p>Independent Events 22</p> <p>Probability Matrices 22</p> <p>Conditional Probability 24</p> <p>Problems 26</p> <p><b>Chapter 3 Basic Statistics 29</b></p> <p>Averages 29</p> <p>Expectations 34</p> <p>Variance and Standard Deviation 39</p> <p>Standardized Variables 41</p> <p>Covariance 42</p> <p>Correlation 43</p> <p>Application: Portfolio Variance and Hedging 44</p> <p>Moments 47</p> <p>Skewness 48</p> <p>Kurtosis 51</p> <p>Coskewness and Cokurtosis 53</p> <p>Best Linear Unbiased Estimator (BLUE) 57</p> <p>Problems 58</p> <p><b>Chapter 4 Distributions 61</b></p> <p>Parametric Distributions 61</p> <p>Uniform Distribution 61</p> <p>Bernoulli Distribution 63</p> <p>Binomial Distribution 65</p> <p>Poisson Distribution 68</p> <p>Normal Distribution 69</p> <p>Lognormal Distribution 72</p> <p>Central Limit Theorem 73</p> <p>Application: Monte Carlo Simulations Part I: Creating Normal Random Variables 76</p> <p>Chi-Squared Distribution 77</p> <p>Student’s <i>t</i> Distribution 78</p> <p><i>F</i>-Distribution 79</p> <p>Triangular Distribution 81</p> <p>Beta Distribution 82</p> <p>Mixture Distributions 83</p> <p>Problems 86</p> <p><b>Chapter 5 Multivariate Distributions and Copulas 89</b></p> <p>Multivariate Distributions 89</p> <p>Copulas 97</p> <p>Problems 111</p> <p><b>Chapter 6 Bayesian Analysis 113</b></p> <p>Overview 113</p> <p>Bayes’ Theorem 113</p> <p>Bayes versus Frequentists 119</p> <p>Many-State Problems 120</p> <p>Continuous Distributions 124</p> <p>Bayesian Networks 128</p> <p>Bayesian Networks versus Correlation Matrices 130</p> <p>Problems 132</p> <p><b>Chapter 7 Hypothesis Testing and Confidence Intervals 135</b></p> <p>Sample Mean Revisited 135</p> <p>Sample Variance Revisited 137</p> <p>Confidence Intervals 137</p> <p>Hypothesis Testing 139</p> <p>Chebyshev’s Inequality 142</p> <p>Application: VaR 142</p> <p>Problems 152</p> <p><b>Chapter 8 Matrix Algebra 155</b></p> <p>Matrix Notation 155</p> <p>Matrix Operations 156</p> <p>Application: Transition Matrices 163</p> <p>Application: Monte Carlo Simulations Part II: Cholesky Decomposition 165</p> <p>Problems 168</p> <p><b>Chapter 9 Vector Spaces 169</b></p> <p>Vectors Revisited 169</p> <p>Orthogonality 172</p> <p>Rotation 177</p> <p>Principal Component Analysis 181</p> <p>Application: The Dynamic Term Structure of Interest Rates 185</p> <p>Application: The Structure of Global Equity Markets 191</p> <p>Problems 193</p> <p><b>Chapter 10 Linear Regression Analysis 195</b></p> <p>Linear Regression (One Regressor) 195</p> <p>Linear Regression (Multivariate) 203</p> <p>Application: Factor Analysis 208</p> <p>Application: Stress Testing 211</p> <p>Problems 212</p> <p><b>Chapter 11 Time Series Models 215</b></p> <p>Random Walks 215</p> <p>Drift-Diffusion Model 216</p> <p>Autoregression 217</p> <p>Variance and Autocorrelation 222</p> <p>Stationarity 223</p> <p>Moving Average 227</p> <p>Continuous Models 228</p> <p>Application: GARCH 230</p> <p>Application: Jump-Diffusion Model 232</p> <p>Application: Interest Rate Models 232</p> <p>Problems 234</p> <p><b>Chapter 12 Decay Factors 237</b></p> <p>Mean 237</p> <p>Variance 243</p> <p>Weighted Least Squares 244</p> <p>Other Possibilities 245</p> <p>Application: Hybrid VaR 245</p> <p>Problems 247</p> <p>Appendix A Binary Numbers 249</p> <p>Appendix B Taylor Expansions 251</p> <p>Appendix C Vector Spaces 253</p> <p>Appendix D Greek Alphabet 255</p> <p>Appendix E Common Abbreviations 257</p> <p>Appendix F Copulas 259</p> <p>Answers 263</p> <p>References 303</p> <p>About the Author 305</p> <p>About the Companion Website 307</p> <p>Index 309</p>
<p><b>Michael B. Miller</b> studied economics at the American University of Paris and the University of Oxford before starting a career in finance. He is currently the CEO of Northstar Risk Corp. Before that, he was the Chief Risk Officer of Tremblant Capital Group, and prior to that, Head of Quantitative Risk Management at Fortress Investment Group. Mr. Miller is also a certified FRM and an adjunct professor at Rutgers Business School.</p>
<p><i>Mathematics and Statistics for Financial Risk Management</i> is a practical guide to modern financial risk management for both practitioners and academics. </p> <p>The recent financial crisis and its impact on the broader economy underscore the importance of financial risk management in today’s world. At the same time, financial products and investment strategies are becoming increasingly complex. Today, it is more important than ever that risk managers possess a sound understanding of mathematics and statistics. <p>In a concise and easy-to-read style, each chapter introduces a different topic in mathematics or statistics. As different techniques are introduced, sample problems and application sections demonstrate how these techniques can be applied to actual risk management problems. Exercises at the end of each chapter and the accompanying solutions at the end of the book allow readers to practice the techniques they are learning and monitor their progress. A companion web site includes interactive Excel spreadsheet examples and templates. <p>This comprehensive resource covers basic statistical concepts from standard deviation and correlation to regression analysis and hypothesis testing. Widely used risk models, including value at risk, factor analysis, Monte Carlo simulation, and stress testing are also explored. Time series analysis, interest rate modeling, optimal hedging, and many other financial topics are covered as well. <p>The <i>Second Edition</i> of this popular guide includes two new chapters. The first new chapter, on multivariate distributions, explores important concepts for measuring the risk of portfolios, including joint distributions and copulas. The other new chapter, on Bayesian analysis, explores an approach to statistical analysis that is particularly useful in dealing with the short, noisy data sets that risk managers often face in practice. <p><i>Mathematics and Statistics for Financial Risk Management</i> is an indispensable reference for today’s financial risk professional.</p>

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