Modern Asset Allocation for Wealth Management
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Оглавление
David M. Berns. Modern Asset Allocation for Wealth Management
Table of Contents
List of Illustrations
Guide
Pages
Modern Asset Allocation for Wealth Management
Preface
Acknowledgments
CHAPTER 1 Preliminaries
EXPECTED UTILITY. Introduction
MPT Is an Approximation
Higher Moment Motivation
Modernized Preference Motivation
A Modern Utility Function
Returns-Based EU Maximization
ESTIMATION ERROR. Introduction
Minimizing Estimation Error
Reducing Sensitivity to Estimation Error
A MODERN DEFINITION OF ASSET ALLOCATION
NOTES
CHAPTER 2 The Client Risk Profile
INTRODUCTION
MEASURING PREFERENCES. Risk Aversion
Loss Aversion
Reflection
Lottery Question Sizing
INCORPORATING GOALS. Preference Moderation via SLR
Discretionary Wealth
Comparison with Monte Carlo
Comparison with Glidepaths
NOTES
CHAPTER 3 Asset Selection
INTRODUCTION
MOMENT CONTRIBUTIONS. Overview
Calculation
Utility Contribution
MIMICKING PORTFOLIOS
A NEW ASSET CLASS PARADIGM. Overview
A Review of Risk Premia
From Assets to Asset Classes
NOTES
CHAPTER 4 Capital Market Assumptions
INTRODUCTION
USING HISTORY AS OUR FORECAST. Background
Estimation Error and Sample Size
Stationarity: Does History Repeat?
ADJUSTING FORECASTS. Pre-Tax Adjustments
Post-Tax Adjustments
NOTES
CHAPTER 5 Portfolio Optimization
INTRODUCTION
OPTIMIZATION RESULTS
TO MPT OR NOT TO MPT?
ASSET ALLOCATION SENSITIVITY
FINAL REMARKS
NOTES
Bibliography
Index
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Chapter 4. Capital Market Assumptions. This chapter justifies the use of historical return distributions as the starting point for asset class forecasts. We review techniques that help diagnose whether history indeed repeats itself and whether our historical data is sufficient to estimate accurately the properties of the markets we want to invest in. A system is then introduced for modifying history-based forecasts by shifting and scaling the distributions, allowing advisors to account for custom forecasts, manager alpha, manager fees, and the effects of taxes in their capital market assumptions.
Chapter 5. Portfolio Optimization. In the fifth and final chapter, we finally maximize our new three-dimensional utility function over the assets selected and capital market assumptions created in the previous chapters. Optimizer results are presented as a function of our three utility function parameters, showcasing an intuitive evolution of portfolios as we navigate through the three-dimensional risk preference space. By comparing these results to other popular optimization frameworks, we will showcase a much more nuanced mapping of client preferences to portfolios. The chapter ends with a review of the sensitivity of our optimal portfolios to estimation error, highlighting generally robust asset allocation results.
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