Asset Allocation Overview- Callan Research.pdf

7/29/2019 Asset Allocation Overview- Callan Research.pdf

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0Independent Adviser Group

Mark AndersenVice President415-274-3023 [email protected]

Callan Associates

101 California StreetSuite 3500San Francisco, CA 94111

The Chemistry of Asset Allocation

Are Traditional Asset Classes the Right Building

Blocks for Portfolio Diversification?

2011 Callan IAG National Conference

September 2011


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Agenda

Overview of traditional approaches

Benefits and shortfalls

Implementation

Introduction to factor-based allocation

Classifying factors

Combining factors

Implementation

A hybrid approach to asset allocation

Economic Scenario Buckets


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Why theInterest in Factors?

Traditionally portfolios are built with asset classes such as equity, fixed income,

real estate, etc.

Ideally portfolios would be made up of many components, each independently

risky, and each compensated with return for taking risk.

It is possible to break down asset classes into building blocks, or factors, which

explain the majority of their return and risk characteristics.

Asset classes are an indirect way to invest in factors, but it is possible to invest

directly in certain factors.


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Asset classes are bundles of exposures divided into categories such as

equities, bonds, real assets, etc.

Asset classes should be as independent as possible (minimal overlap) and in

aggregate should cover the investment universe (minimal gaps).

Correlations among asset classes are driven by many common factor

exposures.

The Basics Why the Interest in Factors?


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The finer the distinctions between various asset classes, the higher the resultingcorrelations.

Typical asset allocation relies on sub-asset classes (such as large cap or small capU.S. equity or non-U.S. developed equity).

Equity

U.S.

Large

Small

Non-

U.S.Developed

Emerging

Debt

U.S.

Investment

Grade

HighYield

Non-

U.S.Developed

Emerging

Asset Class

Sub-Asset Class

The BasicsDistinction Between Asset Classes

and Sub-Asset Classes


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The output of a mean-variance optimizer is a frontier composed of efficientportfolios.

Portfolios are classified as efficient if they provide the greatest expectedreturn for a given level of expected risk.

Optimization, and efficient portfolios, rely heavily on the quality of the inputs

Capital market forecasts are the basis of this type of model.

The Basics Mean Variance Optimization


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The frontier is composed of efficient portfolios across the risk spectrum.

Less than 100% correlation among asset classes lead to diversification benefits whichefficient portfolios maximize.

0% 5% 10% 15% 20% 25% 30% 35%

0%

2%

4%

6%

8%

10%

U.S.

Equity

Non-U.S. Equity

U.S. Fixed Income

Real Estate

PrivateEquity

Cash

Emerging Markets

Equity

Expected Risk (Standard Deviation)

Expec

tedReturn

24% U.S. Equity

13% Non-U.S. Equity5% Emerging Markets

48% Fixed Income

7% Real Estate

3% Private Equity

E(r) = 6.6%, E() = 10%

Traditional Approach Classic Efficient Frontier


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Correlations across asset classes can be high because many asset classes are

exposed to similar or common risk factors.

For example, U.S. equity and U.S. corporate bonds share some common exposures, as doprivate equity and public equity.

U.S. Equity U.S. Corporate Bonds

GDPGrowth

Value Liquidity

Size Inflation

Volatility

Currency

Momentum

RealRates

Liquidity

DurationDefault

Risk

Inflation Volatility

Currency

Capital

Structure

Traditional Approach Some Limitations


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If asset classes are molecules, then factors are atoms.

Those factors aggregate into the risk return characteristics of asset classes.

We are interested in identifying and classifying various factors and exploring

how they can be used to allocate assets.

What Are Factors?


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Factors are the basic exposures which make up investments.

They can be thought of as building blocks or risk premiums.

Periodic Table of Factors

Macroeconomic Regional Equity Fixed Income Other

GDP GrowthSovereign

ExposureSize Duration Liquidity

Product ivit y Currency Value Convexity Leverage

Real Interest

Rates

Emerging

Markets

(Institutions +Transparency)

Momentum Credit Spread Real Estate

Inflation Default Risk Commodities

VolatilityCapital

StructurePrivate Markets

Classifying Factors A Sampling


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Ironically, even though risk factors are the basic building blocks of investments,

there is no natural way of investing in many of them directly.

Most risk factors can be accessed through derivatives or long/shortpositions.

Simple examples:

Inflation: Nominal Treasuries less TIPS

Real Interest Rates: TIPS

Volatility: VIX futures

More complex examples:

Value: Long Developed Equity Value, Short Developed Equity Growth

Size: Long Developed Equity Small Cap, Short Developed Equity LargeCap

Credit Spread: Long U.S. High Quality Credit, Short U.S.Treasury/Government

Duration: Long U.S. Treasury 10+ Year, Short US Treasury 1-3 Year

Emerging Markets: Long Emerging Equity, Short Developed Equity

Not exactly possible:

GDP Growth, Productivity, Momentum, Leverage

Risk Factors How can one get exposure?


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Factor Risk and Return

Factor CorrelationsEquity Value Size EM HY Spread Default Duration Real Rates Inflation Volatility

Equity 1.00

Value 0.07 1.00

Size 0.39 0.15 1.00

EM 0.43 -0.14 0.42 1.00HY Spread 0.69 0.06 0.40 0.41 1.00

Default 0.54 0.03 0.38 0.34 0.74 1.00

Duration -0.18 0.12 0.07 -0.04 -0.38 -0.28 1.00

Real Rates 0.09 -0.04 0.32 0.19 -0.02 0.21 0.57 1.00

Inflation -0.43 0.12 -0.41 -0.35 -0.49 -0.60 0.12 -0.67 1.00

Volatility -0.69 0.04 -0.22 -0.32 -0.49 -0.40 0.15 -0.03 0.32 1.00

Correlation < -0.3 Correlation between 0.4 and 0.6 Correlation > 0.6

Factor Exposure Long Position Short Position Return Risk

Equity MSCI World 3.20% 17.58%

Value MSCI World Value MSCI World Growth 1.07% 6.31%

Size MSCI World Small Cap MSCI World Large Cap 8.80% 8.22%

EM MSCI Emerging Markets MSCI World 13.69% 11.47%

HY Spread BC High Yield BC Intermediate Credit (IG) 2.57% 9.97%

Default BC Aaa BC BBB 1.22% 5.08%

Duration BC 20+ Year Treasuries BC 1-3 Year Treasuries 2.51% 11.95%Real Rates BC TIPS 7.03% 6.86%

Inflation BC Treasuries BC TIPS -1.74% 5.05%

Volatility CBOE VIX -4.05% 66.54%

Historical

Based on 10 Years of Monthly Data ending 12/31/2010

Risk FactorsReturn, Volatility, and

Correlation Historical Experience


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Factor returns (or premiums) are fairly low, most have returned less than 5% overthe past decade.

Factor characteristics are extremely time sensitive, so varying the data horizoncan materially impact relationships.

Factor standard deviations range widely between 5% to 66% (for the VIX).

Correlations among factors are low, typically ranging from -0.5 to +0.6.

Relatively highly correlated factors include Equity vs. High Yield and HighYield vs. Default.

The average correlation for the 10 factors on the previous page is 0.03.

This is significantly less than many asset class correlations which range from -

0.15 to more than +0.90.

Sub-asset classes like U.S. Small Cap vs. U.S. Large Cap are the mostcorrelated while relatively unrelated pairings such as U.S. 1-3 YearTreasuries vs. Private Equity are also the least correlated.

Factor Assumptions Notes on the Historical Experience


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Equal weighting 10 sample factors into a portfolio with monthly rebalancing results in historicalequity-like returns with considerably less risk.

Even with this simple weighting scheme, the factor portfolio exhibits relatively low volatility vs. atypical 60/40 portfolio.

Both portfolios have a very low correlation with each other (-0.18).

40%Russell 3000

40%BC Aggregate

20%MSCI ACWI ex-US

vs.

Historical Return: 5.16% 5.66%

Historical Risk: 11.16% 5.75%

Traditional 60/40 Portfolio

Value

Size

EM

HY

SpreadDefault

Duration

Real Rates

Inflation

Volatility

Factor Portfolio


Equity

Traditional vs. Factor Portfolio comparison


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Optimizing a factor portfolio using historical risk, return, and correlations results in a best-fit portfolio tuned for the 10 year data sample.

This portfolio was selected from the efficient frontier based on its 5.75% expected risk.

This example is meant to illustrate that optimization with factors is possible, but high qualityforward-looking inputs are necessary.

Historical Return: 5.66% 10.25%

Historical Risk: 5.75% 5.75%

Equity

Value

Size

EM

HY

SpreadDefault

Duration

RealRates

Inflation

Volatility

Simple Factor Portfolio

Real Rates

VolatilityValue

Size

EM

OptimizedFactorPortfolio

vs.


Simple vs. Optimized Portfolio Comparison


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Which factors to include?

How to weight factors?

Factor portfolios must be implemented using long and short exposures, oftenvia derivatives allocations.

Active high frequency rebalancing among a large number of risk factors isnecessary to maintain the desired exposure.

Factor portfolios resemble certain styles of hedge funds and risk parityapproaches (sans leverage).

Portfol io Construction Factor Portfolios Present Notable Challenges


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Examine asset classes through a factor lens and group like asset classes

together under macroeconomic scenarios.

Inflation

Eco

nomicGrowth

Low or Falling Growth

High or Rising Inflation

Inflation Linked Bonds (TIPS)

Commodities

Infrastructure

HighGrowth

HighInflation

Real Assets: Real Estate,Timberland, Farmland,Energy, MLPs

LowGrowthLowInflationorDeflation

Cash

Government Bonds

HighGrowthLowInflation

Equity

Corporate Debt

A Hybrid Approach Can We Still Apply Factors to Traditional Methods?


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Bucketing asset classes based on their response to macroeconomic scenarioscombines the transparency of investing with asset classes with the granularity offactor-based approaches.

Sample Groupings:

Capital Growth: Grow assets through relatively high long-term returns

Global equity

Private equity

Income / Flight to Quality: Provide current income and protect capital in times

of market uncertainty

Global fixed income

Cash equivalents

Volatility Hedge: Earn returns between stocks and bond while attempting to

protect capital and temper market volatility

Diversified hedge funds

Real Assets: Support the purchasing power of assets

Real estate

TIPS

Commodities

Natural Resource Equities

Economic Scenarios What Roles do Asset Classes Play?


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0% 5% 10% 15% 20% 25% 30% 35%

2%

4%

6%

8%

10%

Optimization Set: 2011 ESB PvtAsset Class Risk-Reward

Expected Volatility

ExpectedR

eturn

Capital Growth

Private Equity

Income

Real Assets

Real Estate

Volatility Hedge

Cash

Economic Scenarios Working Within the Mean Variance Optimizer


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Economic Scenarios The Building Blocks

0% 5% 10% 15% 20% 25% 30% 35%

2%

4%

6%

8%

10%

Optimization Set: 2011 ESB PvtAsset Class Risk-Reward

Expected Volatility

ExpectedR

eturn

Broad Domestic EquityGlobal Ex-US Equity

Private Equity

Domestic Fixed

High Yield

Non US FixedTIPS

Commodities

Natural Resources

Real Estate

REITsVolatility Hedge

Cash


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Factor Asset Class

Target

Al lo cation CapitalA

ppreciati

on

InflationProtection

Deflation/Crisi

s

Protection

CapitalP

reservati

on

Purcha

sing

Power

Preservation

IncomeGen

eratio

n

Diversific

ation

Alpha

Cash 2% Interest Rates 6%

U.S. Government Bonds 4% International Government Bonds 2%

Company Exposure 53%

Global Credit 11% Global Equity 36% Private Equity 6%

Real Assets 18%

Real Estate 12% Infrastructure 3% TIPS 3%

Special Opportunities 21%

Absolute Return 6% Real Return 7% Distressed Debt 1% Mezzanine Debt 1% Structured Credit 1% Other 5%

This is how one large plan combines factors and asset classes:

Source: Adapted from the Alaska Permanent Fund Corporations Investment Policy Statement dated 12/1/2010.

Economic Scenarios A Hybrid Example


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Practitioners will place increased emphasis on understanding how differentportfolios react to specific economic and capital market outcomes.

Asset classes will be increasingly defined by their expected reactions to thesedifferent economic environments.

Liquidity will be an explicit consideration both in strategic policy developmentand implementation.

Next Steps

in Asset AllocationWhat Does the Future Hold?


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Building portfolios of factors is challenging, but using factors to understandtraditionally constructed portfolios is very useful.

Limitations, including basic investability, preclude most market participantsfrom solely investing along factor lines.

However, factors provide a useful way to group traditional asset classesin macroeconomic buckets.

Several multi asset class managers can engineer strategies with specificfactor exposures; these can be used to augment existing frameworks.

Some factor exposures are explicitly incorporated within manager structureanalysis (such as liquidity, leverage, duration, currency, size, momentum,

etc.).

Conclusion

Asset Allocation Overview- Callan Research.pdf

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