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A Practitioner's Guide to Asset Allocation

A Practitioner's Guide to Asset Allocation

  • 作者:
  • 出版商: John Wiley & Sons
  • ISBN: 9781119397809
  • 出版时间 May 2017
  • 规格: Hardback , 256 pages
  • 适应领域: International ? 免责申明:
    Countri(es) stated herein are used as reference only

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  • 描述 
  • 大纲 
  • 作者 
  • 详细

    Since the formalization of asset allocation in 1952 with the publication of Portfolio Selection by Harry Markowitz, there have been great strides made to enhance the application of this groundbreaking theory. However, progress has been uneven. It has been punctuated with instances of misleading research, which has contributed to the stubborn persistence of certain fallacies about asset allocation.

    A Practitioner's Guide to Asset Allocation fills a void in the literature by offering a hands-on resource that describes the many important innovations that address key challenges to asset allocation and dispels common fallacies about asset allocation. The authors cover the fundamentals of asset allocation, including a discussion of the attributes that qualify a group of securities as an asset class and a detailed description of the conventional application of mean-variance analysis to asset allocation..

    The authors review a number of common fallacies about asset allocation and dispel these misconceptions with logic or hard evidence. The fallacies debunked include such notions as: asset allocation determines more than 90% of investment performance; time diversifies risk; optimization is hypersensitive to estimation error; factors provide greater diversification than assets and are more effective at reducing noise; and that equally weighted portfolios perform more reliably out of sample than optimized portfolios.

    A Practitioner's Guide to Asset Allocation also explores the innovations that address key challenges to asset allocation and presents an alternative optimization procedure to address the idea that some investors have complex preferences and returns may not be elliptically distributed. Among the challenges highlighted, the authors explain how to overcome inefficiencies that result from constraints by expanding the optimization objective function to incorporate absolute and relative goals simultaneously. The text also explores the challenge of currency risk, describes how to use shadow assets and liabilities to unify liquidity with expected return and risk, and shows how to evaluate alternative asset mixes by assessing exposure to loss throughout the investment horizon based on regime-dependent risk.

    This practical text contains an illustrative example of asset allocation which is used to demonstrate the impact of the innovations described throughout the book. In addition, the book includes supplemental material that summarizes the key takeaways and includes information on relevant statistical and theoretical concepts, as well as a comprehensive glossary of terms.

  • Foreword by Harry Markowitz xi

    Preface xiii

    SECTION ONE Basics of Asset Allocation

    CHAPTER 1 What Is an Asset Class? 3

    Stable Aggregation 3

    Investable 4

    Internally Homogeneous 4

    Externally Heterogeneous 5

    Expected Utility 5

    Selection Skill 6

    Cost-Effective Access 6

    Potential Asset Classes 7

    References 8

    Notes 8

    CHAPTER 2 Fundamentals of Asset Allocation 9

    The Foundation: Portfolio Theory 9

    Practical Implementation 12

    References 23

    Notes 23

    SECTION TWO Fallacies of Asset Allocation

    CHAPTER 3 The Importance of Asset Allocation 27

    Fallacy: Asset Allocation Determines More Than 90 Percent of Performance 27

    The Determinants of Portfolio Performance 27

    The Behavioral Bias of Positive Economics 30

    The Samuelson Dictum 34

    References 34

    Notes 35

    CHAPTER 4 Time Diversification 36

    Fallacy: Time Diversifies Risk 36

    Samuelson’s Bet 36

    Time, Volatility, and Probability of Loss 36

    Time and Expected Utility 37

    Within-Horizon Risk 40

    A Preference-Free Contradiction to Time Diversification 41

    The Bottom Line 41

    References 42

    Notes 42

    CHAPTER 5 Error Maximization 43

    Fallacy: Optimized Portfolios Are Hypersensitive to Input Errors 43

    The Intuitive Argument 43

    The Empirical Argument 44

    The Analytical Argument 48

    The Bottom Line 52

    References 53

    Notes 53

    CHAPTER 6 Factors 54

    Fallacy: Factors Offer Superior Diversification and Noise Reduction 54

    What Is a Factor? 54

    Equivalence of Asset Class and Factor Diversification 55

    Noise Reduction 57

    Where Does This Leave Us? 59

    References 59

    Notes 59

    CHAPTER 7 1/N 60

    Fallacy: Equally Weighted Portfolios Are Superior to Optimized Portfolios 60

    The Case for 1/N 60

    Setting the Record Straight 61

    Empirical Evidence in Defense of Optimization 61

    Practical Problems with 1/N 62

    Broken Clock 63

    The Bottom Line 64

    References 64

    Note 64

    SECTION THREE Challenges to Asset Allocation

    CHAPTER 8 Necessary Conditions for Mean-Variance Analysis 67

    The Challenge 67

    Departures from Elliptical Distributions 68

    Departures from Quadratic Utility 71

    Full-Scale Optimization 73

    The Curse of Dimensionality 75

    Applying Full-Scale Optimization 77

    Summary 78

    References 79

    Notes 79

    CHAPTER 9 Constraints 80

    The Challenge 80

    Wrong and Alone 80

    Mean-Variance-Tracking Error Optimization 81

    References 85

    Note 85

    CHAPTER 10 Currency Risk 86

    The Challenge 86

    Why Hedge? 86

    Why Not Hedge Everything? 87

    Linear Hedging Strategies 90

    Nonlinear Hedging Strategies 96

    Economic Intuition 100

    References 101

    Notes 102

    CHAPTER 11 Illiquidity 103

    The Challenge 103

    Shadow Assets and Liabilities 103

    Expected Return and Risk of Shadow Allocations 105

    Other Considerations 107

    Case Study 108

    The Bottom Line 118

    Appendix 119

    References 120

    Notes 120

    CHAPTER 12 Risk in the Real World 121

    The Challenge 121

    End-of-Horizon Exposure to Loss 121

    Within-Horizon Exposure to Loss 123

    Regimes 124

    The Bottom Line 127

    References 127

    Notes 127

    CHAPTER 13 Estimation Error 128

    The Challenge 128

    Traditional Approaches to Estimation Error 129

    Stability-Adjusted Optimization 131

    Building a Stability-Adjusted Return Distribution 140

    Determining the Optimal Allocation 142

    Empirical Analysis 143

    The Bottom Line 146

    References 146

    Notes 147

    CHAPTER 14 Leverage versus Concentration 148

    The Challenge 148

    Leverage in Theory 148

    Leverage in Practice 150

    The Bottom Line 156

    References 157

    Notes 157

    CHAPTER 15 Rebalancing 158

    The Challenge 158

    The Dynamic Programming Solution 159

    The Markowitz–van Dijk Heuristic 163

    The Bottom Line 166

    References 167

    Notes 167

    CHAPTER 16 Regime Shifts 168

    The Challenge 168

    Predictability of Return and Risk 169

    Regime-Sensitive Allocation 169

    Tactical Asset Allocation 174

    The Bottom Line 179

    Appendix: Baum-Welch Algorithm 180

    References 181

    Notes 182

    SECTION FOUR Addendum

    CHAPTER 17 Key Takeaways 185

    CHAPTER 18 Statistical and Theoretical Concepts 192

    Discrete and Continuous Returns 192

    Arithmetic and Geometric Average Returns 193

    Standard Deviation 194

    Correlation 195

    Covariance 196

    Covariance Invertibility 196

    Maximum Likelihood Estimation 198

    Mapping High-Frequency Statistics onto Low-Frequency Statistics 198

    Portfolios 199

    Probability Distributions 200

    The Central Limit Theorem 201

    The Normal Distribution 201

    Higher Moments 201

    The Lognormal Distribution 202

    Elliptical Distributions 202

    Probability of Loss 203

    Value at Risk 203

    Utility Theory 204

    Sample Utility Functions 204

    Alternative Utility Functions 204

    Expected Utility 206

    Certainty Equivalents 206

    Mean-Variance Analysis for More Than Two Assets 207

    Equivalence of Mean-Variance Analysis and Expected Utility Maximization 208

    Monte Carlo Simulation 208

    Bootstrap Simulation 209

    References 210

    Note 210

    CHAPTER 19 Glossary of Terms 211

    Index 233

  • William Kinlaw, CFA, is a Senior Managing Di??rector and Global Head of State Street's academic affiliate, State Street Associates, a unique partnership that bridges the worlds of financial theory and practice.

    Mark P. Kritzman, CFA, is a Founding Partner and Chief Executive Officer of Windham Capital Management, LLC and the Chairman of Windham's investment committee. He is responsible for managing research activities and investment advisory services. He is also a Founding Partner of State Street Associates, and teaches a graduate course at the Massachusetts Institute of Technology.

    David Turkington, CFA, is a Senior Managing Director and Head of Portfolio and Risk Research at State Street Associates.

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