The New Science of Asset Allocation E-Book

Thomas Schneeweis

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Finance Editions
• Sprache: Englisch

Seitenzahl: 421

Veröffentlichungsjahr: 2010

Beschreibung

A feasible asset allocation framework for the post 2008 financial world

Asset allocation has long been a cornerstone of prudent investment management; however, traditional allocation plans failed investors miserably in 2008. Asset allocation still remains an essential part of the investment arena, and through a new approach, you'll discover how to make it work.

In The New Science of Asset Allocation, authors Thomas Schneeweis, Garry Crowder, and Hossein Kazemi first explore the myths that plague this field then quickly move on to examine how the practice of asset allocation has failed in recent years. They then propose new allocation models that employ liquidity, transparency, and real risk controls across multiple asset classes.

• Outlines a new approach to asset allocation in a post-2008 world, where risk seems hidden
• The "great manager" problem is examined with solutions on how to capture manager alpha while limiting downside risk
• A complete case study is presented that allocates for beta and alpha

Written by an experienced team of industry leaders and academic experts, The New Science of Asset Allocation explains how you can effectively apply this approach to a financial world that continues to change.

Leseprobe

Table of Contents

Title Page

Copyright Page

Preface

OVERVIEW OF THE BOOK

Acknowledgements

CHAPTER 1 - A Brief History of Asset Allocation

IN THE BEGINNING

A REVIEW OF THE CAPITAL ASSET PRICING MODEL

ASSET PRICING IN CASH AND DERIVATIVE MARKETS

MODELS OF RETURN AND RISK POST-1980

ASSET ALLOCATION IN THE MODERN WORLD

PRODUCT DEVELOPMENT: YESTERDAY, TODAY, AND TOMORROW

NOTES

CHAPTER 2 - Measuring Risk

WHAT IS RISK?

TRADITIONAL APPROACHES TO RISK MEASUREMENT

CLASSIC SHARPE RATIO

OTHER MEASURES OF RISK ASSESSMENT

PORTFOLIO RISK MEASURES

OTHER MEASURES OF PORTFOLIO RISK MEASUREMENT

VALUE AT RISK

NOTES

CHAPTER 3 - Alpha and Beta, and the Search for a True Measure of Manager Value

WHAT IS ALPHA?

ISSUES IN ALPHA AND BETA DETERMINATION

PROBLEMS IN ALPHA AND BETA DETERMINATION

MULTI-FACTOR RETURN ESTIMATION: AN EXAMPLE

TRACKING ALTERNATIVES IN ALPHA DETERMINATION

NOTES

CHAPTER 4 - Asset Classes: What They Are and Where to Put Them

OVERVIEW AND LIMITATIONS OF THE EXISTING ASSET ALLOCATION PROCESS

ASSET ALLOCATION IN TRADITIONAL AND ALTERNATIVE INVESTMENTS: A ROAD MAP

HISTORICAL RETURN AND RISK ATTRIBUTES AND STRATEGY ALLOCATION

TRADITIONAL STOCK/BOND ALLOCATION VERSUS MULTI-ASSET ALLOCATION

RISK AND RETURN COMPARISONS UNDER DIFFERING HISTORICAL TIME PERIODS

EXTREME MARKET SENSITIVITY

MARKET SEGMENT OR MARKET SENSITIVITY: DOES IT MATTER?

HOW NEW IS NEW?

NOTES

CHAPTER 5 - Strategic, Tactical, and Dynamic Asset Allocation

ASSET ALLOCATION OPTIMIZATION MODELS

STRATEGIC ASSET ALLOCATION

TACTICAL ASSET ALLOCATION

DYNAMIC ASSET ALLOCATION

NOTES

CHAPTER 6 - Core and Satellite Investment: Market/Manager Based Alternatives

DETERMINING THE APPROPRIATE BENCHMARKS AND GROUPINGS

SAMPLE ALLOCATIONS

CORE ALLOCATION

SATELLITE INVESTMENT

ALGORITHMIC AND DISCRETIONARY ASPECTS OF CORE/SATELLITE EXPOSURE

REPLICATION BASED INDICES

PEER GROUP CREATION—STYLE PURITY

NOTES

CHAPTER 7 - Sources of Risk and Return in Alternative Investments

ASSET CLASS PERFORMANCE

HEDGE FUNDS

MANAGED FUTURES (COMMODITY TRADING ADVISORS)

PRIVATE EQUITY

REAL ESTATE

COMMODITIES

NOTES

CHAPTER 8 - Return and Risk Differences among Similar Asset Class Benchmarks

MAKING SENSE OUT OF TRADITIONAL STOCK AND BOND INDICES

PRIVATE EQUITY

REAL ESTATE

ALTERNATIVE REIT INVESTMENTS INDICES

COMMODITY INVESTMENT

HEDGE FUNDS

INVESTABLE MANAGER BASED HEDGE FUND INDICES

CTA INVESTMENT

INDEX VERSUS FUND INVESTMENT: A HEDGE FUND EXAMPLE

NOTES

CHAPTER 9 - Risk Budgeting and Asset Allocation

PROCESS OF RISK MANAGEMENT: MULTI-FACTOR APPROACH

PROCESS OF RISK MANAGEMENT: VOLATILITY TARGET

RISK DECOMPOSITION OF PORTFOLIO

RISK MANAGEMENT USING FUTURES

RISK MANAGEMENT USING OPTIONS

COVERED CALL

LONG COLLAR

NOTES

CHAPTER 10 - Myths of Asset Allocation

INVESTOR ATTITUDES, NOT ECONOMIC INFORMATION, DRIVE ASSET VALUES

DIVERSIFICATION ACROSS DOMESTIC OR INTERNATIONAL EQUITY SECURITIES IS SUFFICIENT

HISTORICAL SECURITY AND INDEX PERFORMANCE PROVIDES A SIMPLE MEANS TO FORECAST ...

RECENT MANAGER FUND RETURN PERFORMANCE PROVIDES THE BEST FORECAST OF FUTURE RETURN

SUPERIOR MANAGERS OR SUPERIOR INVESTMENT IDEAS DO NOT EXIST

PERFORMANCE ANALYTICS PROVIDE A COMPLETE MEANS TO DETERMINE BETTER PERFORMING MANAGERS

TRADITIONAL ASSETS REFLECT “ACTUAL VALUES” BETTER THAN ALTERNATIVE INVESTMENTS

STOCK AND BOND INVESTMENT MEANS INVESTORS HAVE NO DERIVATIVES EXPOSURE

STOCK AND BOND INVESTMENT REMOVES INVESTOR CONCERNS AS TO LEVERAGE

GIVEN THE EFFICIENCY OF THE STOCK AND BOND MARKETS, MANAGERS PROVIDE NO USEFUL SERVICE

INVESTORS CAN RELY ON ACADEMICS AND INVESTMENT PROFESSIONALS TO PROVIDE ...

ALTERNATIVE ASSETS ARE RISKIER THAN EQUITY AND FIXED INCOME SECURITIES

ALTERNATIVE ASSETS SUCH AS HEDGE FUNDS ARE ABSOLUTE RETURN VEHICLES

ALTERNATIVE INVESTMENTS SUCH AS HEDGE FUNDS ARE UNIQUE IN THEIR INVESTMENT STRATEGIES

HEDGE FUNDS ARE BLACK BOX TRADING SYSTEMS UNINTELLIGIBLE TO INVESTORS

HEDGE FUNDS ARE TRADERS, NOT INVESTMENT MANAGERS

ALTERNATIVE INVESTMENT STRATEGIES ARE SO UNIQUE THAT THEY CANNOT BE REPLICATED

IT MAKES LITTLE DIFFERENCE WHICH TRADITIONAL OR ALTERNATIVE INDICES ARE USED IN ...

MODERN PORTFOLIO THEORY IS TOO SIMPLISTIC TO DEAL WITH PRIVATE EQUITY, REAL ...

NOTES

CHAPTER 11 - The Importance of Discretion in Asset Allocation Decisions

THE WHY AND WHEREFORE OF ASSET ALLOCATION MODELS

VALUE OF MANAGER DISCRETION

MANAGER EVALUATION AND REVIEW: THE DUE DILIGENCE PROCESS

MADOFF: DUE DILIGENCE GONE WRONG OR NEVER CONDUCTED

NOTES

CHAPTER 12 - Asset Allocation: Where Is It Headed?

AN UNCERTAIN FUTURE

WHAT IS THE DEFINITION OF ORDER?

COSTS AND BENEFITS

TODAY’S ISSUE

POSSIBLE GOVERNMENTAL AND PRIVATE FUND RESPONSES TO CURRENT MARKET CONCERNS

NOTE

APPENDIX - Risk and Return of Asset Classes and Risk Factors Through Business Cycles

Glossary: Asset Class Benchmarks

Bibliography

About the Authors

Index

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Copyright © 2010 by Thomas Schneeweis, Garry B. Crowder, and Hossein Kazemi. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Schneeweis, Thomas.

The new science of asset allocation : risk management in a multi-asset world / Thomas Schneeweis, Garry B. Crowder, Hossein Kazemi.

p. cm.

Includes bibliographical references and index.

eISBN : 978-0-470-60841-8

1. Asset allocation. 2. Risk management. I. Crowder, Garry B., 1954- II. Kazemi, Hossein, 1954- III. Title.

HG4529.5.S3366 2010

332.6--dc22

2009047243

Preface

Without reservation, everything we believe about asset allocation and the perceived science surrounding its application is not necessarily true. The corollary to this statement is that a complete understanding of asset allocation is impossible. First, all beliefs are based on perceived fact; unfortunately sometimes those perceptions stem from a misreading or misunderstanding of the relevant material, or on the reliance of oral communications from a trusted advisor or source. Often our beliefs are a function of intellectual laziness or a failure to properly question.

Second, we do not even know all the facts associated with any asset investment. What is known is that the market disturbances of 2007 and 2008 have brought into sharp relief the failure of past beliefs—and the facts upon which they rest—relating to financial models, the institutions that create and distribute these models, and the regulatory and legislative oversight designed to protect investors as well as the financial system as a whole. As such, this is a seminal period in asset allocation. It is a period where once again the approach to asset allocation and risk management has an opportunity to be re-examined and where a new appreciation of the changing nature of asset allocation approaches and the importance of discretion in creating and managing the preservation of wealth can be established.

Asset allocation is perhaps the only investment tool that provides investors with an inherent “free lunch.” It focuses on proven practices where equal risk assets with less-than-perfect correlation lead to higher long term returns than if those assets are held individually. What recent history has shown is that many of the benefits of asset allocation have been lost due to oversimplified approaches and a less-than-rigorous understanding of the risks and sources of return of differing asset classes. This is particularly true of “new” asset classes such as hedge funds, private equity, real estate, and commodities, as well as so-called structured products. For example:

• Many simplified approaches to asset allocation are based solely on historical index data. Unfortunately, times change. Benchmarks change. Index composition changes. Today’s Dow Jones Index holds a different set of firms and associated risks than those that existed even 10 years ago. This is even truer for emerging markets. Again, while holding a diverse set of assets may reduce risk in certain market environments, historical evidence alone may not provide the basis for deciding which assets to hold (the benefits of emerging markets shown in historical data may simply be due to the unique currency moves of that time period).

• Practitioner research generally focuses on a limited number of asset classes (stocks, bonds, cash, real estate; and so forth), largely because these are the asset classes that most practitioners have to sell. As shown during 2008, those asset classes do not provide the range of assets necessary to provide adequate diversification. Moreover, those asset classes do not contain many of the assets or investment approaches that provide today’s investors the ability to manage risk (however you define it). Just as important, many of the historical correlations reported by these asset classes are, in fact, not representative of correlations between many modern asset vehicles in current market environments. For instance, the historical low correlation numbers between stocks and bonds and real estate is due in part to the fact that real estate prices generally have not represented their true market value but their accounting value, which may not change over time, in contrast to their true sale price, which may often change over time. Similarly, private equity returns and the returns of many hedge fund strategies are model driven. The message sent is clear—beware of past data and doubly beware of bad past data.

• Today’s market and trading environment is fundamentally different than that of even five years ago. Today, tradable ETFs exist that provide access to a wide range of investment sectors and risk/return scenarios. Tradable forms of private equity, real estate, hedge fund, managed futures, and commodity indices also exist. Moreover, the degree to which these new investment tools are offered and how they are presented to investors is often based on the business model of the firm offering the investment or investment advice. Investors often fail to take into account that the underlying business models of the firms offering asset allocation advice directly impact their product mix, their approach to asset allocation, and the relative return and risk scenarios they use in their asset allocation processes.

In summary, asset allocation is a dynamic yet reflective process. While it is based in part on a fundamental understanding of the underlying assets, the markets in which they trade, and the pros and cons of the various asset allocation and risk models used to manage those assets, it also requires discretion. Simple reliance on past model based approaches, past data, or past success does not suffice. By definition the asset allocation process assumes change in both expectations and results. It cannot be viewed in a vacuum and must be viewed against what “can and/or should happen” to asset holdings. Meaningful analysis or reflection cannot be derived from simply reading the top 10 investment books on the New York Times nonfiction list. Often these books promote an investment theme that in some way ignores the fundamental rules of the marketplace (e.g., the belief that certain managers can and do defy the laws of financial equilibrium and can make money in all market environments) or ignores the benefits managers may offer by suggesting that successful investment can be accomplished by simple, systematic rules based approaches. Either approach is doomed. Neither discretion without an investment framework nor an investment framework without discretion is sustainable.

OVERVIEW OF THE BOOK

This book’s focus is simply to bring a sense of reality back into the investment process. Chapter 1 focuses on a very short history of asset allocation. In the early 1920s, several finance books warned of investment in stocks (and were proven right in the late 1920s). Equity investment however, provides a meaningful way to share in the growth of the world economy, and despite the stock market crash of the 1930s, individual stock investment became commonplace by the 1950s. However, until Markowitz’s article in 1952, many investment books concentrated on individual stock selection instead of portfolio creation. Times have changed dramatically since then. In the 1960s, theoretical tools such as the CAPM offered ways to understand expected risk and return. In the 1970s, markets expanded to provide a range of risk management tools (currency futures, bond futures, and stock options, to name a few) that permitted managers to move significantly away from long only based portfolio analysis. In the 1980s, stock index futures and index options were developed. New forms of dynamic risk management, such as portfolio insurance, also came into existence. In the 1990s, new asset sectors such as mortgages, new approaches to asset management such as hedge funds, and a wider range of investment vehicles such as Collateralized Debt Obligations (CDOs) were developed. By 2000, financial engineers had come into their own, developing even more complex investment instruments and vehicles, each designed to further cauterize and trade market risk. Unfortunately, few investors considered that each of these new investment forms or vehicles fundamentally changed the relationship between assets and how those assets would perform and respond in extreme economic environments. This chapter provides a brief history of how each of these major market changes affected the approach to asset allocation and how asset allocation has had to evolve to meet changed economic conditions.

At the core of asset allocation is a view of the expected return to risk relationship. However, when investors actually confront and contemplate the concept of risk, quickly the risk of measuring risk is revealed. Each investor has a different definition of risk. Most academics describe risk in terms of standard deviation and beta—most practitioners have little real understanding of either concept, and risk becomes some amorphous concept based on past experience or the reliance on mathematical models and company practice. Chapter 2 offers investors a better sense of what risk measurement is and what it is not. Differences among investors as to what risk is and how risk measurement affects asset allocations are several of the sources of differential approaches to asset allocation.

Since we monitor only what we can measure, Chapter 3 concentrates on reviewing the principal tools (alpha and beta) governing the determination of fundamental asset risk as well as the ability of managers to create value. We show that even in the simple world of single -factor risk models (standard deviation, skewness, market beta) as well as in more complex models of risk and return determination, the model itself may impede an understanding of the fundamental risks we face. In short, there is risk in assuming we know what risk is, as well as risk in the actual models used for risk estimation.

Chapter 4 provides the building blocks for a multi-asset look at asset allocation. We do not attempt to change accepted approaches to asset class determination as much as to expand it to places it has long wished to go such as a wider range of asset classes including alternative investments. From the very beginning, questions existed as to where non-investable assets fit in the world of the CAPM. For many the question still remains “Do alternative investments provide the average investor with valuable return and risk opportunities beyond that available in traditional stock and bond investments?” In its most simple form, an equal weighted stock (high risk) and bond (low risk) portfolio is in fact a high risk stock portfolio with a little bit of bond risk. The potential addition of a range of other investment classes should at least offer one answer to this stock/bond conundrum.

Moreover, the answer to the benefits of asset allocation in a multi-asset universe may simply be that “more is better than less.” Additional assets may provide investors with access to return opportunities that may not exist in other states of the traditional stock and bond world. Many of the limitations of the current asset allocation approaches are that they concentrate primarily on investment in a limited number of assets (stocks, bonds, and real estate). Today, investment in a larger range of investable assets is being addressed through more active asset construction. The increase in potential investment opportunities increases the potential benefit of strategic asset allocation opportunities as well as tactical and dynamic approaches to asset allocation. Chapter 5 addresses those issues.

There are of course numerous approaches to asset allocation. At the heart of asset allocation remains the fundamental set of decisions centered on what and how much to buy, given risk preferences. Chapter 6 ignores individual risk preferences in providing a simple core/satellite approach to asset allocation. This chapter does not emphasize the more complex models of return and risk optimization but focuses on the potential impacts of moving from more liquid, transparent investment vehicles in each asset class to less liquid, less transparent investment vehicles and the potential increase in expected return and risk associated with that movement.

There is a caveat. As noted above, over the last 30 years or so, the underlying characteristics of the asset classes used to measure risks have dramatically changed in composition and delivery. Most books on asset allocation continue to emphasize the return and risk characteristics of traditional stock and bond investments. Given the amount of research and information on the return and risk characteristics of traditional stock and bond investment, Chapter 7 travels a new road and focuses on other major forms of alternative investments, their source of returns, and their recent performance. Understanding the primary forms of alternative investment does not provide sufficient information as to the investability of various alternative investments. The underlying investments that investors have access to must reflect the return and risk characteristics of the traditional benchmarks used in most asset allocation models. Investors forget that even the most traditional stock and bond benchmarks are not strictly investable in their “common index” form. For stock and bond indices, management costs and trading costs make even investable stock and bond products differ slightly from the pure non-investable index products used in most asset allocation research.

Chapter 8 provides some answers to the relative performance of various non-investable alternative investment benchmarks and their associated investable counterparts. Here, as in most questions of asset management, the devil is in the details. For many portfolios, it is necessary to back into the asset allocation decision by first determining a reasonable set of investment vehicles with the desired liquidity and return characteristics. For most, traditional asset allocation remains the simple choice of mixing various asset classes to provide a mix of assets that offers increased expected return for a particular level of risk tolerance. However, as discussed previously there is no one definition of risk. Before risk can be managed, the fundamental risks impacting a particular investor must be understood. Chapter 9 reviews some of the major risks facing an investor as well as some common methods of managing them. Finally, we provide several examples of how simple approaches to risk management based on futures markets, options markets, and other basic forms of dynamic asset allocation can fundamentally transform the risk exposure of various investment vehicles. These approaches focus primarily on managing price risk. Thus even the simplified approaches to risk management must be viewed as the proverbial tip of the iceberg of risk and risk management.

It is always dangerous to point out one’s own failings, when they are generally fairly obvious to others. Despite that, it is always beneficial to point out that when telling a story it is best that the reader know what parts of the story are true and what parts are based on myth. Chapter 10 examines a number of myths of asset allocation. Perceptions are weighed against measurable outcomes in discussing issues such as whether stocks, bonds, and cash provide an adequate means of diversification; or whether hedge funds provide a natural low correlation to traditional assets; or whether economic and risk relationships remain static over time. The list goes on.

Asset allocation is not a simple science. There are a number of risks involved in its use. In Chapter 11 we discuss the benefits and costs of various asset allocation approaches from more algorithmic to more discretionary. In many books on asset allocation, the systematic model driven approach is emphasized. In Chapter 11 the importance of manager discretion is emphasized. This chapter does not detail a model for determining the costs and benefits of manager discretion. Manager discretion can often increase return but at the potential cost of increased risk. However, for many investors, the potential costs and benefits of discretionary management are not fully appreciated. Most investors simply fail to take to heart the axiom that unusual returns can only be obtained from holding unusual risks or paying for means of managing that risk (systematic or discretionary).

Asset allocation exists in an evolving marketplace. Chapter 12 explores various factors affecting the future of asset allocation. There will certainly be a series of choices and each of those choices will have ripple consequences. The existence of a multi-asset world is of benefit only if we can take advantage of it. As these choices are constrained by market forces, government forces, or personal choice, the potential benefits are perhaps reduced. The question, not answered in this book, is whether the potential constraints are balanced by the change in risk. Unfortunately, making no decision as to the impact of potential future events on asset allocation is in fact a decision. At the end of the day, asset choices have to be made. Investors, however, must know the basis for these decisions as well as the basis for disinvesting from these assets. Systematic approaches to asset allocation may help, but in the final analysis the choice is yours and it must contain your personal discretionary beliefs.

The book concludes with additional material that should help the investor to follow the ideas presented herein. Given the constraints of time and space, the actual historical relationship between many of the asset classes discussed in the book and their performance over various market conditions has not been detailed. However, in an appendix we do provide a review of the performance of various investment classes over a range of historical economic conditions. A glossary summarizes the major asset benchmarks used in the book, followed by a bibliographic section that offers references to the source material for many of the ideas expressed in the book.

Of course, any book with three authors is both a debate and reconciliation. Invariably there are points where the differing perspectives must be melded into one. There are also cases where one author believes that something is important, but for the sake of time, civility, or space constraints, it is agreed that that information will be left out or not explored as fully as that author would like. Thus at the outset of this endeavor we fully acknowledge this book does not cover every aspect of asset allocation from either a practitioner’s standpoint or that of an academic. Rather, it is the exploration of a number of the primary issues relating to this subject and designed to provide the reader sufficient insight to effectively question market opportunities, products, and ideas.

The reader should also be aware that like most things, the ideas expressed in this book are time sensitive. The writing of this book began in the early spring of 2009 and concluded in early 2010. Throughout this period, extreme events have occurred. If history is to be a teacher, we know that the future will provide additional information where many of the thoughts and questions within this book will be challenged as well as proven incorrect. Also, throughout this book the reader has been cautioned to be wary of historical data, historical thoughts, and historical performance. In other words show little fear in puncturing myths and their companions. History rarely repeats itself in the same manner; and one of the failings of modern portfolio design as well as some of the recent academic and quantitative research is the presumption that it will. Just as important, given the dynamic aspects of the markets, any asset allocation and risk management approach requires both a full understanding of the benefits and risks of various strictly quantitative approaches as well as a discretionary overlay to provide additional insight and experience to the asset allocation and risk management process.

Acknowledgments

We stand on the shoulders of those who preceded us. There are just too many individuals whose words and ideas we simply transcribed to thank them all. For ideas we conveyed correctly, we extend thanks to those who provided them to us. For those whose ideas we have failed to present in suitable fashion, we ask indulgence. We would like to offer special thanks to our editor, Emilie Herman. There are many reasons for starting a book. There are even more reasons for not finishing it. Without her constant support and encouragement, we fear this effort would have met the fate of the latter.

CHAPTER 1

A Brief History of Asset Allocation

For most investors, asset allocation and its meaning seems relatively straightforward, that is, the process of allocating assets. It is the how and the why of asset allocation that has led to an entire asset management industry dedicated to its operation. Given the amount of resources and effort dedicated to understanding asset allocation, it would be reasonable to expect that after almost 5,000 years of human history there would be a suitable solution. The fact that the investment management industry is still groping for an answer is illustrated in the millions of references to “asset allocation” from any Internet search and the fact that there are enough practitioner books and academic articles on “how to allocate assets” to fill any investor’s library. This chapter provides a brief history of how major advances in financial theory and investment practice affected investors’ approach to asset allocation and how asset allocation has had to evolve to meet changes in economic, regulatory, and technological environments. However, given the range of current and past efforts to diagnose, describe, and prescribe the process of asset allocation, it seems relatively futile to provide any reasonable summary of how we got here, much less what “here” is.

Before reviewing how we have arrived at current approaches to asset allocation, a brief review of what asset allocation is seems appropriate. Simply put, the ability to estimate what the future returns and risks of a range of investors’ acceptable investments are and to choose a course of action based upon those alternatives is at the heart of asset allocation. As a result, much of asset allocation is centered on the quantitative tools or approaches used to estimate the probabilities of what may happen (risk) and the alternative approaches to managing that risk (risk management). While the concept of risk is multi-dimensional—including various types of market risks as well as liquidity risk, operational risk, legal risk, counterparty risk, and so on—for many it is simply the probability of a bad outcome. There is simply no single approach to asset allocation that covers all individuals’ sense of risk tolerance or even what risk is. In the world of asset allocation, we generally concentrate on the concept of statistically driven risk management since those risk measurements are often centered on statistical estimates of probability (which is measurable) rather than on the concept of uncertainty (or possibility management), on which our empirically driven asset allocation models have little to say.

As a consequence, there is risk or uncertainty even in the most basic concept of asset allocation. Much of what we do in asset allocation is based on the tradeoffs between the risks and returns of various investable assets as well as the risks and returns of various aspects of asset allocation, including alternative approaches to return and risk estimation. Choosing among the various courses of action lies at the heart of a wide range of asset allocation approaches, including:

• Strategic asset management (allocation across various investment classes with the goal of achieving a desired long-term risk exposure)

• Tactical asset management (allocation within or across investment classes with the goal of maximizing the portfolio ’s short-term returnrisk profile)

• Dynamic asset management (systematic changes in allocation across assets with the goal of fundamentally changing the portfolio’s risk exposure in a predetermined way)

Asset allocation is not about solely maximizing expected return. It is a central thesis of this book as well as years of academic theory and investment practice that expected return is a function of the risks taken and that those risks may not be able to be measured or managed solely through systematic algorithmic based risk management. Thus, asset allocation must focus on risk management in a broader context, including the benefit of an individual asset allocators’s discretionary oversight in order to provide a suitable return to risk tradeoff consistent with an investor’s risk tolerance or investment goals. The story of the evolution of our understanding of that return to risk tradeoff is the subject of this chapter. It is important to emphasize the “evolution” part as our understanding of the expected return to risk relationship keeps changing. First, because through time we learn more about how individuals react to risk and second, because the world itself changes (the financial world included).1

An individual’s or institution’s approach to asset allocation depends of course in part on their relative understanding of the alternative approaches and the underlying risks and returns of each. For the most part, this book does not attempt to depict the results of the most current research on various approaches to asset allocation. In many cases, that research has not undergone a full review or critical analysis and is often based solely on algorithmic based model building. Also, many individuals are simply not aware of or at ease with this current research since their investment background is often rooted in traditional investment books in which much of this “current research” is not included.2

IN THE BEGINNING

It should be of no surprise to investors that the two fundamental directives of asset allocation: (1) estimate what may happen and (2) choose a course of action based on those estimates have been at the core of practitioner and academic debate. For our purposes, the timeline of that debate is illustrated in Exhibit 1.1. The advent of Modern Portfolio Theory and practice is often linked to the publication of Harry Markowitz’s 1952 article “Portfolio Selection.” For many the very words “Modern Portfolio Theory” are synonymous with Markowitz. It is important to point out that Modern Portfolio Theory is now almost 60 years old. As such, and not merely as a result of age, MPT (Modern Portfolio Theory) is really IPT (Initial Portfolio Theory) or OPT (Old Portfolio Theory). Moreover, the fundamental concept expressed in Markowitz’s article (the ability to manage risk based on the expected correlation relationships between assets) was well known by practitioners at the time of its publication.

EXHIBIT 1.1 Timeline of Financial Advances in Asset Allocation

Markowitz formalized the return and risk relationship between securities in what is known today as the mathematics of diversification. If expected single-period returns and standard deviations of available securities as well as the correlations among them are estimated, then the standard deviation and the expected return of any portfolio consisting of those securities can be calculated. This means that portfolios can be constructed with desirable standard deviation and expected return profiles. One particular set of such portfolios is the so-called mean-variance efficient portfolios, which have the highest expected rate of return for a given level of risk (variance). The collection of such portfolios for various levels of variance leads to the mean - variance efficient frontier.3 In the mid 1950s, James Tobin (1958) expanded on Markowitz’s work by adding a risk-free asset to the analysis.4 This brought into focus an individual ’s ability to hold only two types of assets (risky and riskless) and to lend or borrow such that those two assets provided the tools necessary to match a wide range of investor return and risk preferences.5

The next major advancement in asset allocation expanded the work of Markowitz and Tobin into a general equilibrium model of risk and return. In this work, academics treated volatility and expected return as proxies for risk and reward. In the early 1960s, academics (Sharpe, 1964) proposed a theoretical relationship between expected return and risk based on a set of assumptions of individual behavior and market conditions. These author(s) proposed that if investors invested in the mean -variance efficient market portfolio, then the required rate of return of an individual security would be directly related to its marginal contribution to the volatility of that mean-variance efficient market portfolio; that is, the risk of a security (and therefore its expected return) could not be determined while ignoring its role in a diversified portfolio.

A REVIEW OF THE CAPITAL ASSET PRICING MODEL

The model developed by Sharpe and others is known as the Capital Asset Pricing Model (CAPM). While the results of this model are based on several unrealistic assumptions, it has dominated the world of finance and asset allocation for the past 40 years. The main foundation of the CAPM is that regardless of their risk-return preference, all investors can create desirable mean-variance efficient portfolios by combining two portfolios/assets: One is a unique, highly diversified, mean-variance efficient portfolio (market portfolio) and the other is the riskless asset. By combining these two investments, investors should be able to create mean-variance efficient portfolios that match their risk preferences. The combination of the riskless asset and the market portfolio (the Capital Market Line [CML] as shown in Exhibit 1.2) provides a solution to the asset allocation problem in a very simple and intuitive manner: Just combine the market portfolio with riskless asset and you will create a portfolio that has optimal risk -return properties.

Thus, in the world of the CAPM all the assets are theoretically located on the same straight line that passes through the point representing the market portfolio with beta equal to 1. That line is called the Security Market Line (SML), as shown in Exhibit 1.3. The basic difference between the CML and the SML is one of reference system. In the CML the risk measured is total risk (standard deviation), while the risk measured in the SML is a security’s marginal risk to the market portfolio (beta).

While the most basic messages of MPT and CAPM (that diversification is important and that risk has to be measured in the context of an asset ’s marginal contribution to the risk of reference market portfolio) are valid and accepted widely by both academics and practitioners, many of their specific recommendations and predictions are not yet fully accepted and in some cases have been rejected by empirical evidence.6 For instance, observed security returns are very weakly, if at all, related to a security ’s beta, and most investors find a simple combination of the market portfolio and the riskless asset totally inadequate in meeting their risk-return requirements.

ASSET PRICING IN CASH AND DERIVATIVE MARKETS

CAPM and EMH

As discussed in greater detail later in this book, the CAPM profoundly shaped how asset allocation within and across asset classes was first conducted. Individual assets could be priced using a limited set of parameters. Securities could be grouped by their common market sensitivity into different risk classes and evaluated accordingly; and, to the degree that an expected market risk premia could be modeled, it would also be possible (if desired) to adjust the underlying risk or beta of a portfolio to take advantage of changes in expected market risk premia (i.e., increase the beta of the portfolio if expected market risk premia is high and reduce the beta of the portfolio if the expected market risk premia is low). Here, market risk premia is defined as the difference between the expected rate of return of the market portfolio and the “riskless rate of interest.”

While the CAPM is at its heart a model of expected return determination, it quickly became the basis for a number of asset allocation based decision models. The rudimentary nature of computers in the early 1960s is often forgotten and, while the mathematics of the Markowitz portfolio optimization model were well known, the practical application was limited due primarily to the number of numerical calculations. Specifically, the amount of data needed to obtain reasonable estimates of the covariance matrix is significant. For instance, if we have 100 securities, then to estimate the covariance matrix, we would need to estimate 100 variances and (1002 - 100)/2 covariances, which add up to 5,050 parameters, have to be estimated. This would be computationally difficult and would have required many hours of work. As an alternative, the number of calculations can be significantly reduced if it is assumed that returns are driven by only one factor (e.g., the market portfolio). Note that this does not assume that CAPM holds. In other words, suppose we use a simple linear regression to estimate the beta of an asset with respect to a well diversified portfolio.

The rate of return on the asset at time t is given by Rit, the rate of return on the diversified portfolio is given by Rmt, the intercept and the slope (beta) are given by αi and βi respectively. Finally, the error term for asset i is given by eit. Suppose we run the same regression for another asset, denoted asset j. If the error term for asset j is uncorrelated with the error term for asset i, then the covariance between the two assets is given by

Notice that to estimate covariance between the two assets, we need an estimate of the variance of the market portfolio as well (Var(Rm)). However, this term will be common to all estimates of covariance. The result is that the number calculations required to estimate covariance matrix is now reduced to (2 × 100 + 1).

It is important to note that the above regression model, known as the market model, has nothing to do with the CAPM. The above regression makes no prediction about the size or the sign of intercept. It simply a statistical relationship used to estimate the beta. On the other hand, the CAPM predicts that the market model intercept will be (1 - βi)Rf.

It is fair to say, however, that almost 40 years ago most academics and professionals knew that the CAPM was an “incomplete” model of expected return. We now know that Sharpe and his fellow academics had unwittingly created a sort of “Asset Pricing Vampire,” which rose from their model and, despite 30 years of stakes driven into its heart lives to this day for many practitioners as the primary approach to return estimation.7 In the early years of the CAPM, financial economists were like kids with a new hammer in which everything in the financial world looked like a nail. For example, if an asset’s expected return can be estimated, then that estimate could be used as a basis for determining if an individual could consistently choose assets that were fundamentally underpriced and offered an ex post return greater than that consistent with its underlying risk. In sum, it provided the basis for determining if managers could obtain an alpha (excess return above that consistent with the expected return of a similar risk - passive investable asset).

The combination of the full information assumptions in the CAPM, along with the “presumed” ability to measure expected returns consistent with risk, offered academics the chance to measure the true informational efficiency of the marketplace. Initial studies by academics indicated that active managers underperformed similar risk passive indices. This empirical result helped give rise later to the creation of a series of passive non-investable and investable indices that would form the basis for the asset allocation consulting industry. As important, the combination of presumed informational efficiency with the ability to measure expected return led to the development of the Efficient Market Hypothesis (Fama, 1970) in which assets’ prices were described relative to the degree to which their current prices reflected various types of information; that is, an asset’s current price may be consistent with (1) past price information (weak form efficiency); (2) public information (semi-strong efficiency); and (3) private information (strong form efficiency). If market inefficiencies existed, this implied that investors could earn returns that would exceed what is predicted by the asset’s underlying risk as if there were some violation of information efficiency (similar to a monopoly or oligopolies). However, if the Efficient Market Hypothesis (EMH) is true, most investors should not waste their time trying to pick individual stocks using well-known public information but concentrate on risk determination and the proper set of assets to capture the expected risk that matches their risk preferences.

Today it is realized that the Efficient Market Hypothesis would be more correctly named the “Excess Return if We Only Knew How To Measure Expected Return Hypothesis”; it did provide the impetus for moving from a “Managers Only Matter” state of mind to an asset allocation process based on “Managers May Matter But Let Us Measure It First ” plus a “Passive Approach to Asset Class/Security Selection.” Again, it is important to come to terms with what the EMH says and does not say. EMH does not say that prices fluctuate randomly. EMH states that prices randomly fluctuate with a drift; that is, tomorrow’s expected price is equal to today’s price times the asset’s expected return where expected return is based on current information (risk assessment). EMH says that there are no free lunches. Such profit opportunities are quickly eliminated, and the only way one can earn a high rate of return is through assuming a higher level of risk.

The quintessential problem is that there is no firm understanding of how people determine expected risk-adjusted return since there are no conclusive models that demonstrate how people price risk. All we can say is whether a manager has been able to create excess return (return above some arbitrary chosen expected return model). The EMH does not say that an investment manager cannot make a gross return in excess of a passive approach. The EMH only says that if a manager makes such an excess return (e.g., because of access to technology or information), the investor may be charged a fee equal to the excess return such that the net return will be similar to that of investment in the passive index (e.g., manager returns - manager fee ≥ return on passive index). The manager’s fee is supposed to cover the cost of acquiring the technology and/or information plus the investment made in time and effort to use that technology and information.

The combination of the CAPM and the EMH gave the market place the twin academic pillars required for the development of the asset allocation industry. All that was needed was a third pillar, a business model capable of developing the infrastructure required to market this new industry. Fortunately, computers and information technology had advanced such that in the late 1960s the investment industry witnessed the expansion of the index business. Both within the United States and overseas, monthly and even daily data series of domestic and global stock indices were being created. These indices could be used to provide estimates of the benefits of various approaches to asset allocation. For instance, newly developed global stock indices were used in a number of studies to illustrate the potential benefits of combining domestic stock indices (asset classes) with foreign and international stock indices (Grubel, 1968; Levy and Sarnet, 1970).8

Lost, of course, in this academic and practitioner euphoria were some of the practical realities relating to the underlying assumptions of the CAPM and EMH. First, the available empirical evidence had not strictly supported the CAPM’s expected return and risk relationship. There was no means to estimate the “True Market Portfolio,” so any empirically estimated betas were only estimates subject to unknown measurement errors. More complex multi-factor models were required to capture expected return processes. While the market for financial products aimed at providing such multi-factor models came into existence (e.g., Barr Rosenberg and Barr’s better betas), most academics remained wedded to single -factor models. As academics came to appreciate the statistical problems associated with using underspecified single factor (beta) models of return determination or the data problems associated with the use of international data (e.g., timing of data or liquidity), attempts were made to “tweak” the CAPM. Throughout the 1970s, various forms of zero beta and multi -beta APT models came into existence—better to explain the previously unexplained residual error of the single factor models of return estimation. These models provided additional statistical tools for measuring the efficacy of the EMH.

As with most people, when given the choice between the familiar and the unfamiliar, academics and practitioners kept using the hammers they had (CAPM and EMH) to nail down the problem of expected return estimation and the degree to which individual managers provided returns in excess of similar risk passively produced portfolio returns. In truth, the CAPM and EMH models did an excellent job of describing most market conditions. For the most part, markets do work. It should be expected that for financial markets with low-cost information (e.g., Treasury Bill market), asset prices would reflect current information and a common risk based return model. Other markets and/or assets may require enlarged risk based factor models that capture an enlarged set of underlying risks and therefore expected returns. Small firms with few analysts following them, with less ability to raise capital, with a less diversified client base, limited legal support, and so on may be priced to reflect those risks. Many assets are simply not tradable or have high transaction costs (e.g., housing, commodities, employment contracts, or distressed debt). How they could or should be priced in a single-factor or even a multi-factor model framework was explored, but a solution was rarely found.9

Option Pricing Models and Growth of Futures Markets

We have spent a great deal of time focusing on the equity markets. During this period of market innovation, considerable research also centered on direct arbitrage relationships. Arbitrage relationships in capital and corporate markets were explored during the 1930s (forward interest rates implied in yield curve models)10 and in the 1950s (corporate dividend policy and debt policy). Similarly, cost of carry arbitrage models had long been the focal point of pricing in most futures based research. In the early 1970s Fischer Black and Myron Scholes (1973) and Merton (1973) developed a simple-to-use option pricing model based in part on arbitrage relationships between investment vehicles. Soon after, fundamental arbitrage between the relative prices of a put option (the right to sell) and a call option (the right to buy) formed a process to become known as the Put-Call Parity Model, which provided a means to explain easily the various ways options can be used to modify the underlying risk characteristics of existing portfolios. Exchange based trading floors soon came into existence, which helped eventually to develop a market for a wide range of option based financial derivatives. While a range of dynamic futures based approaches should provide similar risk management opportunities, options provided a direct and easily measured approach to fundamentally change the risk composition of an asset or a portfolio. As important, the model allowed one to estimate the cost for modifying the risk of a portfolio.

The growth of options as a means to provide risk management was centered primarily on equity markets. The 1970s also witnessed the creation and growth of new forms of financial futures, including currency futures in the early part of that decade and various forms of fixed income futures in the latter half (Treasury Bond futures). The creation of the Commodity Futures Trading Commission (CFTC) in the mid 1970s provided the additional government oversight necessary for the growth and development of new forms of financial futures as well as options products based on them. It is well known that futures provide a means to directly track underlying investment markets as well as to provide risk reduction opportunities. Futures contracts offer the ability to reduce or increase the underlying variability of an asset but futures alone do not permit one to fundamentally change the risk structure of the asset. The ability to directly change the distributional form of an asset is left for options. It can simply be said that the creation and development of options and futures trading in the 1970s led the way for the creation of an entire new industry dedicated to new means of managing risk.

MODELS OF RETURN AND RISK POST-1980