Asset Allocation. William Kinlaw
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but struggle to marry these techniques with quantitative analysis. We include new models for forecasting (Chapter 13) and scenario analysis (Chapter 23) that can improve the rigor and effectiveness of traditional quantitative techniques while offering easy interpretation to complement judgment and debate. We also expand our discussion of illiquid alternative assets to address specific challenges associated with measuring the true risk exposure of private equity (Chapter 11). The topics and methodologies introduced in other chapters remain as relevant today as they were in 2017.
We have made two enhancements to this edition to help readers navigate the content more efficiently. First, there are a number of common threads in this book that link the chapters, both new and old, together. To highlight these linkages, we now include references to related topics at the end of each chapter so that readers can explore their particular interests efficiently. Second, we now begin the book with a summary of key takeaways from each of the chapters. Readers can peruse these takeaways and then decide which chapters are of particular interest. Of course, we hope that readers will find all the chapters interesting.
We divide the rest of the book into four sections. Section I covers the fundamentals of asset allocation, including a discussion of the attributes that qualify a group of securities as an asset class, as well as a detailed description of the conventional application of mean-variance analysis to asset allocation. In describing the conventional approach to asset allocation, we include an illustrative example that serves as a base case, which we use to demonstrate the impact of the innovations we describe in subsequent chapters.
Section II presents certain fallacies about asset allocation, which we attempt to dispel either by logic or with evidence. These fallacies include the notion that asset allocation determines more than 90% of investment performance, that time diversifies risk, that risk parameters are stable across return intervals, that correlations are symmetric, that optimization is hypersensitive to estimation error, that factors provide greater diversification than assets and are more effective at reducing noise, that equally weighted portfolios perform more reliably out-of-sample than optimized portfolios, that policy portfolios matter, and that the volatility of private equity is greater than that of public equity due to private equity's higher leverage.
Section III describes several innovations that address key challenges to asset allocation. We present an alternative optimization procedure to address the challenge that some investors have complex preferences and returns may not be elliptically distributed. We introduce a new forecasting technique that exploits information about the relevance of historical observations. We apply advances in quantitative methods to forecast the stock–bond correlation. We show how to overcome inefficiencies that result from constraints by augmenting the optimization objective function to incorporate absolute and relative goals simultaneously. We describe how to integrate asset allocation with factor investing. We demonstrate how to use shadow assets and liabilities to unify liquidity with expected return and risk. And we address the challenge of currency risk by presenting a cost/benefit analysis of several linear and nonlinear currency-hedging strategies.
We show how to reduce estimation error in covariances by introducing a nonparametric procedure for incorporating the relative stability of covariances directly into the asset allocation process. We address the challenge of choosing between leverage and concentration to raise expected return by relaxing the simplifying assumptions that support the theoretical arguments. We describe a dynamic programming algorithm as well as a quadratic heuristic to determine a portfolio's optimal rebalancing schedule. We address the challenge of regime shifts with several innovations, including stability-adjusted optimization, blended covariances, and regime indicators. We introduce a mathematically rigorous and empirically driven approach for carrying out scenario analysis. Finally, we show how to evaluate alternative asset mixes by assessing exposure to loss throughout the investment horizon based on regime-dependent risk.
Section IV provides supplementary material, including an expanded chapter on relevant statistical and theoretical concepts as well as a comprehensive glossary of terms.
This book is not an all-inclusive treatment of asset allocation. There are certainly some innovations that are not known to us, and there are other topics that we do not cover because they are well described elsewhere. The topics that we choose to write about are ones that we believe to be especially important, yet not well known nor understood. We hope that readers will benefit from our efforts to convey this material, and we sincerely welcome feedback, be it favorable or not.
Some of the ideas in this book originally appeared in journal articles that we coauthored with past and current colleagues. We would like to acknowledge the contributions of Nelson Aruda, Alain Bergeron, Wei Chen, George Chow, David Chua, Paula Cocoma, Eric Jacquier, Ding Li, Kenneth Lowry, Simon Myrgren, Sébastien Page, Baykan Pamir, Grace (Tiantian) Qiu, Don Rich, and Gleb Sivitsky. And we would like to express special gratitude to Megan Czasonis, who coauthored many of the articles that underpin much of this book's content and who has contributed importantly to shaping our views and understanding of asset allocation.
In addition, we have benefited enormously from the wisdom and valuable guidance, both directly and indirectly, from a host of friends and scholars, including Peter Bernstein, Stephen Brown, John Campbell, Edwin Elton, Frank Fabozzi, Gifford Fong, Martin Gruber, Martin Leibowitz, Andrew Lo, Harry Markowitz, Robert C. Merton, Krishna Ramaswamy, Stephen Ross, Paul A. Samuelson, William Sharpe, and Jack Treynor. Obviously, we accept sole responsibility for any errors.
Finally, we would like to thank our wives, Michelle Kinlaw, Abigail Turkington, and Elizabeth Gorman, for their support of this project as well as their support in more important ways.
William Kinlaw
Mark Kritzman
David Turkington
Key Takeaways
Chapter 1: What Is an Asset Class?
The composition of an asset class should be stable.
The components of an asset class should be directly investable.
The components of an asset class should be similar to each other.
An asset class should be dissimilar from other asset classes in the port- folio as well as combinations of other asset classes.
The addition of an asset class to a portfolio should raise its expected utility.
An asset class should not require selection skill to identify managers within the asset class.
An asset class should have the capacity to absorb a meaningful fraction of a portfolio cost-effectively.
Chapter 2: Fundamentals of Asset Allocation
A portfolio's expected return is the weighted average of the expected returns of the asset classes within it.
Expected return is measured as the arithmetic average, not the geometric average.
A portfolio's risk is measured as the variance of returns or its square root, the standard deviation.
Portfolio risk must account for how asset classes co-vary with one another.
Portfolio risk is less than the weighted average of the variances or stan- dard deviations of the asset classes within it.
Diversification cannot eliminate portfolio variance entirely. It can only reduce it to the average covariance of the asset classes within it.
The efficient frontier comprises portfolios that offer the highest expected return for a given level of risk.
The optimal portfolio balances an investor's goal to increase wealth with the investor's aversion to risk.
Mean-variance