Demonstrates why MPT never became ineffective during the crisis, and how you can continue to reap the rewards of managed diversification into the future. This book arms you with concrete steps to accurately select and apply the right risk measures in a given circumstance.
Demonstrates why MPT never became ineffective during the crisis, and how you can continue to reap the rewards of managed diversification into the future. This book arms you with concrete steps to accurately select and apply the right risk measures in a given circumstance.
Harry M. Markowitz is president of Harry Markowitz Co. in San Diego. In 1990, he was jointly awarded the Nobel Prize for economics with Merton Miller and William Sharpe.
Inhaltsangabe
Foreword xi Preface xxi Acknowledgments xxvii Outline of Plans for Volumes II, III, and IV xxix 1. The Expected Utility Maxim 1 Introduction 1 Defi nitions 5 Uniqueness 10 Characteristics of Expected Utility Maximization 12 RDMs Versus HDMs 14 Allais's Paradox 17 Weber's Law and the Allais Paradox 21 The Axioms 24 Axiom I 25 Axiom II 26 Axioms III and III 28 Bounded Versus Unbounded Utility of Returns 31 Postscript 34 2. Mean-Variance Approximations to Expected Utility 37 Introduction 37 Why Not Just Maximize Expected Utility? 41 Utility of Return Versus Utility of Wealth 44 Loistl's Erroneous Analysis 47 Levy and Markowitz (1979) 48 Highly Risk-Averse Investors 53 Highly Risk-Averse Investors and a Risk-Free Asset 56 Portfolios of Call Options 58 Ederington's Quadratic and Gaussian Approximations to Expected Utility 63 Other Pioneers 69 Conclusion 72 3. Mean-Variance Approximations to the Geometric Mean 73 Introduction 73 Why Inputs to a Mean-Variance Analysis Must Be Arithmetic Means 78 Six Mean-Variance Approximations to g 80 Observed Approximation Errors for Asset Classes 84 Relationships Among Approximation Methods 90 Twentieth-Century Real Equity Returns 97 Choice of Approximation 111 Recap 117 Technical Note: Selecting a Weighted Average of Approximations 118 4. Alternative Measures of Risk 123 Introduction 123 The Asset-Class Database 124 Comparisons 127 The DMS Database 137 Caveat and Conclusion 147 5. The Likelihood of Various Return Distributions (With Anthony Tessitore, Ansel Tessitore,and Nilufer Usmen) 149 Introduction 149 Bayes Factors 153 Transformed Variables 156 Compound Hypotheses 159 The Pearson Family 161 The DMS Database 169 Practically Normal Distributions 175 Illustrative Histograms 179 Near LH-Maximizing Distributions for the Ensemble 182 Transformed Country Distributions 186 Observations 190 Recommendation 192 Notes 195 References 209 Index 217
Foreword xi Preface xxi Acknowledgments xxvii Outline of Plans for Volumes II, III, and IV xxix 1. The Expected Utility Maxim 1 Introduction 1 Defi nitions 5 Uniqueness 10 Characteristics of Expected Utility Maximization 12 RDMs Versus HDMs 14 Allais's Paradox 17 Weber's Law and the Allais Paradox 21 The Axioms 24 Axiom I 25 Axiom II 26 Axioms III and III 28 Bounded Versus Unbounded Utility of Returns 31 Postscript 34 2. Mean-Variance Approximations to Expected Utility 37 Introduction 37 Why Not Just Maximize Expected Utility? 41 Utility of Return Versus Utility of Wealth 44 Loistl's Erroneous Analysis 47 Levy and Markowitz (1979) 48 Highly Risk-Averse Investors 53 Highly Risk-Averse Investors and a Risk-Free Asset 56 Portfolios of Call Options 58 Ederington's Quadratic and Gaussian Approximations to Expected Utility 63 Other Pioneers 69 Conclusion 72 3. Mean-Variance Approximations to the Geometric Mean 73 Introduction 73 Why Inputs to a Mean-Variance Analysis Must Be Arithmetic Means 78 Six Mean-Variance Approximations to g 80 Observed Approximation Errors for Asset Classes 84 Relationships Among Approximation Methods 90 Twentieth-Century Real Equity Returns 97 Choice of Approximation 111 Recap 117 Technical Note: Selecting a Weighted Average of Approximations 118 4. Alternative Measures of Risk 123 Introduction 123 The Asset-Class Database 124 Comparisons 127 The DMS Database 137 Caveat and Conclusion 147 5. The Likelihood of Various Return Distributions (With Anthony Tessitore, Ansel Tessitore,and Nilufer Usmen) 149 Introduction 149 Bayes Factors 153 Transformed Variables 156 Compound Hypotheses 159 The Pearson Family 161 The DMS Database 169 Practically Normal Distributions 175 Illustrative Histograms 179 Near LH-Maximizing Distributions for the Ensemble 182 Transformed Country Distributions 186 Observations 190 Recommendation 192 Notes 195 References 209 Index 217
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