Fixed Income Attribution Whitepaper

FIXED INCOME ATTRIBUTIONPortfolio insights through analytical precision

may 2013

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1 | rbc invest or services & statpr o

Todays low-rate slow-growth markets are challenging institutional investors to more carefully analyse correlations between investment strategies and portfolio performance. Fixed income investments embody unique characteristics that require unique performance attribution solutions.

fixed income at tribution | 1

Portfolio diversification strategies continue to include fixed

income instruments as a means to counter market uncertainty.

Moreover, demographic trends are pointing to an increased

dependency on these instruments as a risk hedgenamely Baby

Boomers who are shifting a growing share of their retirement

assets into bonds. But is there a clear understanding of the factors

influencing portfolio performance? How can fixed income

managers prepare for future economic shocks?

The answer in part, lies in reliable methodologies and

technologies that offer the transparency demanded by todays

sophisticated investors.

RBC Investor Services and StatPro are pleased to share with

you strategies and solutions that offer a new perspective

an alternative to traditional performance attribution models.

In order to best demonstrate the value of this approach, it is

important to have insight into current economic conditions and

also understand the gaps that exist with common fixed income

modeling disciplines. We also explore fixed income trends

and opportunities.

We trust you will find these insights relevant and useful as

you continue to refine your performance attribution strategies.

We look forward to your comments and feedback.

Mandeep Dhillon Dario CintioliProduct Manager Product Director

RBC Investor Services StatPro

foreword


fixed income in demand

Global demand for fixed income products continues to be strong,

largely due to the diversification benefits this asset class provides

investors. Towards the end of Q1 2011, 20 percent of worldwide

investment assets were held in fixed income. This figure has since

risen to 24 percent at the end of Q3 2012 with further allocations

held in balanced funds.

According to Mandeep Dhillon, Product Manager, Risk &

Investment Analytics with RBC Investor Services, Increasing

allocations to fixed income instruments signifies the growing

value and importance of this asset class in creating investor

value. In assessing fixed income and balanced fund assets

collectively, positions have increased from 30 percent to 35

percent during the period noted.

According to the European Fund and Asset Management

Association (EFAMA), the value of fixed income and balanced

fund assets increased from 5.97 trillion to 7.6 trillion in less

than two years, a 27 percent growth rate, while equity fund

assets increased by only four percent during the same period.

Contributing factors include a continued low growth environment

globally, increased uncertainty and volatility in equity markets

and a diminished tolerance for risk spanning many investor

typesparticularly Baby Boomers who continue to recover from

significant devaluations in their retirement savings resulting

from the financial crises and ongoing turmoil.

composition of worldwide investment fund assets 2011:q1

Percent of total assets, end of quarter

composition of worldwide investment fund assets 2012:q3(*)

Percent of total assets, end of quarter

Equity 40%

Money Market 18%

Bond 20%

Balanced-Mixed 10%

Other/Unclassified 12%

Equity 37%

Money Market 16%

Bond 24%

Balanced-Mixed 11%

Other/Unclassified 12%

Source: EFAMA International Statistical Release Q1 2011

Source: EFAMA International Statistical Release - Q3 2012

(*) Including fund of funds

12%

Equity40%

Money Market18%

Bond20%

10%

12%

Equity

Money Market

Bond24%

11%


85

75

65

55

45

35

25

15

Jun

05

Jun

08

Sep

05

Sep

08

Dec

05

Dec

08

Mar

06

Mar

08

Mar

11

Jun

06

Jun

09

Jun

11

Sep

t 06

Sep

t 09

Sep

t 11

Dec

06

Dec

09

Dec

11

Mar

07

Mar

09

Mar

12

Jun

07

Jun

10

Jun

12

Sep

07

Sep

10

Sep

12

Dec

07

Dec

10

Dec

12

generating additional returns

While traditional passive fixed income investing is an approach

designed to generate steady returns and diversify portfolios,

mandates that deviate from the benchmark and focus on placing

active bets have also proven to be successful. According to RBC

Investor Services Pooled Fund Survey (Q4 2012), median active

Canadian bond managers were able to add 35 basis points against

the DEX Universe over a five-year period and 15 basis points over

a 10-year period. For global bond mandates, the survey shows

median active managers outperforming the Citigroup World

Government Bond Index by 128 basis points over five years and

296 basis points over 10 years.

To replicate this experience, active fixed income management

requires a thorough understanding of the risk and return drivers

affecting a portfolios value including duration, spread, as well as

sector and market allocation. It may also require an increased

allocation to non-traditional fixed income instruments such as

floating rate notes, credit default swaps, convertible bonds and,

in general, a willingness to accept greater risk through higher

yielding issues.

The acceptance of higher yielding, yet higher risk bond issues is

captured in RBC Investor Services quarterly All Plan universe,

which reveals that pension managers have been materially

overweight in their allocations to non-federal issues relative

to the index since Q3 2007.

At the end of Q4 2012, the overweighting was 19 percent. The low

interest rate environment required plan sponsors to seek higher

returns (and assume greater risk) in other fixed income sectors

such as corporates, provincials and municipals to offset the rise

in pension plan liabilities. As a result, accurately measuring and

monitoring the effects of these active bets and their associated

risk factors becomes an integral component of the asset

management process.

The figures presented illustrate a continued dependence on fixed

income assets as a major component of both discretionary and

non-discretionary mandates. While the aggregate composition of

worldwide investment funds may shift as global macroeconomic

conditions improve, persistent market uncertainty and slow

economic recovery in the United States, China and the Eurozone

will continue to spur interest in fixed income assets.

It is essential then, that global fixed income investors have

access to appropriate models and robust, market-leading tools

that assist in effectively managing the risk and return attributes

of this exposure.

canadian bond characteristics

Allocation to Non-Federals within Domestic Bonds All Plan Universe

Non-Federals DEX U Non-Federals


new fixed income tools

Both fixed income investment strategies and the instruments themselves are evolving as the quest

to extract greater yields in this low interest rate environment continues. Beyond standard fixed rate

bonds, instruments have been developed that target changes in credit quality, such as FRNs and

CLOs. Others, such as MBS-based derivatives, have been developed to target specific segments in the

mortgage market. CMOs and basket derivatives go further and target more implicit aspects such as

default correlation among issuers.

A common and transparent approach to pinpointing all sources of risk and return is crucial.

But as each new development emerges, a greater level of expertise is required in order to understand

the fundamental aspects of the instruments and the nuances of each as they are used in specific

strategies. From an oversight and investor perspective, a common and transparent approach to

pinpointing all sources of risk and return is crucial. The performance attribution model selected

must be sophisticated enough to accommodate the range of market instruments as well as the

changing nature of the market. At the same time, it must have the capacity to convey the results

in an easily understood manner to facilitate timely and informed investment decisions.

While these characteristics are familiar and reflect methodologies currently employed by equity

markets, they are not as relevant to the fixed income sector. A specific attribution model for the fixed

income sector is essential.


limitations of traditional attribution models

Many investors and managers are familiar with traditional

performance attribution models such as the Brinson-Fachler

model that highlights a managers ability to meet or exceed stated

objectives (e.g., to outperform an index or a blended benchmark).

These models provide a clearer picture of the drivers of excess

returns and help focus on the potential sources of risk.

The Brinson-Fachler model, widely used for equities, highlights

the allocation effects (i.e., tactical positioning) and the selection

effects (i.e., security selection). This model is often referred to as

a sector-based or segmented attribution method as the measure

of tactical positioning is based on a comparison of the portfolio

sector breakdown to the benchmark sector breakdown (e.g., is the

portfolio underweight in a specific sector). However, the portfolio

does not have to be decomposed by sector. It can be segmented

by geography, market-cap, or any other relevant breakdown.

Regardless of the segmentation scheme selected, it must be

consistent with the investment process and objectives. From

there, the Brinson-Fachler model quantifies security selection

skills within each segment.

To a large measure, this model does indeed pinpoint how a

managers skill adds value to a portfolio. But while sector-based

models can be customised to any investment segmentation

scheme to pinpoint outperformance, they are not consistent

with how fixed income managers make investment decisions.

Fixed income managers typically do not partition the market

into broad segments when outlining a strategy. Instead, they

investigate sources of return and risk that will make a positive

portfolio impact. For example, key drivers of bond fund returns

would include interest rate levels, credit spread movements,

steepening yield curves, etc. The challenge for a manager is

determining how to segment a portfolio that allows for many of

the dimensions of risk and return to be captured simultaneously.

A manager may be able to partition the portfolio by one

dimension (e.g., interest rate sensitivity), but how would other

dimensions be incorporated?

One alternative is a linear factor-based approach where the most

common risk factors are identified and the portfolios sensitivity

(also referred to as betas, loadings, or coefficients) to each factor

is calculated using an analytical method. The return of the

portfolio can be summed up as:

R =nFn+residual (where is the sensitivity to a risk factor)

Most investors recognise this approach from the equity world

where the two most cited factor models are the CAPM and the

Fama-French model.

And while this approach can simultaneously incorporate many

risk factors such as spread duration and implied volatility, it

is rather restrictive when applied to complex portfolios with

non-linear securities. Even at a very basic level, it is clear that

there is an inverse relationship between interest rates and

fixed-rate bond prices. More importantly, an attribution model for

fixed income portfolios should incorporate all nuances of fixed

income instruments such as callability, putability, mortgage

prepayment assumptions, etc., most of which are not linear.

The solution lies in a fixed income attribution system that is

consistent with the fixed income investment process and captures

the key aspects of the various types of fixed income securities.

In addition, such a system must be able to display the attribution

results in a clear and transparent manner so that all portfolio

stakeholders (i.e., managers, investors, investment boards, etc.)

can contribute to important oversight decisions.


designing a new attribution solution

By using the fixed income investment process as the starting

point, it is possible to design a fixed income attribution solution

that is consistent with the process and also has the benefit of

incorporating the myriad of fixed income instruments. Furthermore,

using this starting point allows for a basic view of fixed income

decomposition and more detailed exploration and discussion.

The investment process uses two key building blocks for fixed

income returns: the level of interest rates and spreads and the

changes in interest rates and spreads. Finding the level of rates

and spreads is rather simple, but many fixed income managers

and traders spend substantial time and effort into modeling the

changes in rates and spreads and understanding the corresponding

repercussions. For example, many investment banks have their

own models for forecasting future interest rate environments and

how changes will affect mortgage prepayments. There are many

other contributors to return, including currency exposure and

inflation, but the starting point for most managers is the level

of rates.

The term used to describe the level of rates and spreads is carry.

Carry is the yield that an investor will receive by simply holding

the bondthe first basic contributor to fixed income returns.

Carry is typically broken up into systematic carry and specific

carry. Systematic carry is the return that can be obtained by

investing in the interbank (LIBOR) curve and the specific carry is

the return added to the systematic carry to compensate for taking

added risk associated with an issuer, seniority, sector, etc. The

combination of specific and systematic carry is analogous to the

yield-to-maturity for standard fixed-rate bonds.

Source: RBC Investor Services Investment Analytics Interactive

Changes in interest rates are usually modeled as changes in the

shape of the yield curve. Like carry, changes in the shape of the

yield curve are sources of investment return. When every point

in the yield curve is moved simultaneously by the same amount

(i.e., 10 bps), this movement is referred to as a shift. Other types

of movements that are common to yield curves are twist and

butterfly. Twist measures the effect of the change around one

point on the curve resulting in a steepening or flattening of the

curve. Butterfly measures the effect of a change in the curvature

of the yield curve. Together, twist and butterfly are called

non-parallel movements.

One other effect that can be included as a source of return is roll

down. Roll down measures the effect of the change in maturity

over time as the bond converges to par. A robust fixed income

system should be able to attribute changes to the shape of the

yield curve into these effects simultaneously, since these are the

most common yield curve movements.

The other major changes that should be measured and clearly

highlighted are the effect of changes in the credit spreads (or

perceived changes in the credit quality of the instrument) and

the effect of foreign currency exposures (relevant to multicurrency

portfolios).

Other contributors to return such as inflation, convexity and

optionality can be included in the overall presentation but must

be included in the calculations. However, to be clear and

transparent, it is mandatory that the major contributors of carry,

changes in yield curve shape, changes in spreads and the effects

of currency exposure are presented explicitly.


refining the approach

Fixed income investing requires a different attribution approach that pinpoints the sources of risk

and return. It is clear that traditional sector-based attribution alone cannot adequately help investors

dissect performance figures.

Factor-based models are an improvement although their linear nature does not capture the nuances

associated with fixed income instruments. For example, in a more complex portfolio, it is crucial to

capture all the optionality embedded in callable bonds, the effects of convexity and all the

implications of effective time-to-maturity for mortgage pools. To capture all these nuances, the

engine driving a fixed income attribution system requires a robust library of pricing functions. These

pricing functions can then compute risk (sensitivity) figures consistently and thoroughly. By applying

these sensitivities to actual changes in the market environment, an advanced attribution report can

be produced. In this manner, all non-linear effects are captured and the attribution model aligns with

the investment process.

A clear example is reflected in an assessment of the risk factors affecting a simple domestic fixed-rate

bond, namely interest rates and credit spreads. The pricing function attached to this bond will input

interest rates and credit spreads and a computation process will use this function to output

sensitivity numbers. The sensitivity numbers are modified duration, modified spread duration, and

convexity. These numbers will then be applied to the changes in interest rates and credit spreads that

occurred in the time period in question. The combination of these changes and the bonds sensitivity

to these changes will provide a clear picture of where returns were made.


pricing functions

risk sensitivity numbers

changes in risk factors


assembling the blocks

The ideal building blocks of a robust and reliable attribution

report should target sources of return (i.e., the effects of carry,

rate changes, spread changes and currency exposure) and identify

what drives the underlying calculations (i.e., pricing functions

and consistent methodology for sensitivity numbers). A system

that does not incorporate these aspects may expose investors

to latent risk that may not be apparent from a traditional

performance review.

Once the building blocks are assembled, a system can be used to

highlight other more complex exposures and sensitivities, such as

credit sensitivities at different shifts and DV01s across currencies.

Another important element of the fixed income investment

process is portfolio stress testing. Regardless of the investment

strategy of a fixed income portfolio (i.e., active or passive), the

investment team must be able to quickly react to macroeconomic

and market changes that affect their holdings. To supplement the

fixed income attribution analysis and reports, stress tests (both

engineered and historical) can be applied to the portfolio. For

market shocks that are not representative of normal market

movements (e.g., extreme events from history or engineered

events that could potentially occur), a tool that employs full

pricing functions should be used. Sensitivities to these larger

market movements can be reliably used to shock each underlying

instrument in the portfolio to obtain an overall portfolio response

to market events. This is a particularly important and relevant

exercise as future market movements are not likely to occur in 10

bps, 50 bps or 100 bps increments.

An advanced model can further segregate other contributors

to return beyond the common carry, yield, and spread aspects.

For more complex securities, contribution including convexity,

inflation, and convertibility can be displayed.

An important element of the fixed income investment process is portfolio stress testing.


a practical example

The following example illustrates the concepts discussed previously, from the perspective of both

a passive investor (with more interest in recent fund performance) and an active manager (with

interest in assessing the funds exposure to future shocks and making allocation decisions

accordingly).

The accompanying screenshots depict an attribution platform that shows the performance of a

sample global bond portfolio initially set up as a 50-50 split between government bonds and

corporate issues, from January 2012 to January 2013.

From a historical perspective, it is evident that the portfolio is yielding less in 2013 than in 2012, yet

still above US LIBOR, which could reflect a general movement in rates. What is most revealing,

however, is that a majority of the portfolio returns are coming from corporates and their associated

spread movement.

From the return breakdown, the corporate sectors weighting increased above the original 50%

allocation to 52.5% while also contributing more to the overall return. The portfolios response to

changes in credit spreads had the largest influence on the portfolios returns, as the health of

industrial companies improved. A robust system should provide drill down capabilities in order to

examine contribution sources at a more granular level, as displayed here.

While this detail provides investors with an indication as to what is generating the returns, an active

manager might prefer to use an attribution system and incorporate it into the investment process.

Typically, an investment process has three steps:

1. Set investment objectives and policies

2. Select an investment strategy

3. Allocate funds to individual assets

An attribution system can be integrated into the second step. Once the objectives are established, a

strategy needs to be chosen and implemented. This involves evaluating and forecasting market conditions

then positioning the portfolio in light of this evaluation by allocating to existing or new assets. It is

also important to assess and forecast market conditions at a macro level. Based on a summary of RBC

Economics year-end research, inflation is projected to remain flat and some yield curves are expected to

steepen slightly. For the sample portfolio, the relevant forecasts would be in regard to the health of

the economy. With inflation in check, central banks can focus on employment and growth. Therefore,

GDP, in North America in particular, is expected to rise and credit spreads are expected to narrow.




Portfolio returns are dependent on a variety of contributing factors.



Source: RBC Economics Research, Economic and Financial Market Outlook



a practical example cont.


Please refer to the Appendix for additional economic indicators and insights.

Another important assessment is to investigate the portfolios exposure based on currency. The exposure

graphs of the sample portfolio are categorised by interest exposure and credit spread exposure.

The DV01 figures on page 12 reflect interest rates, spreads and inflation. The sensitivity to spreads in

the US and Canada are higher than in the other major markets.

With market projections and portfolio exposures in hand, allocation decisions can then be made. The

decision-making process can be broken down by factor classifications with risk factors that affect a

portfolio can be classified into yield curve factors, non-yield curve factors, and issuer specific factors.

Based on the example provided, changes in the shape of the yield curve did not contribute

significantly to the overall portfolio return. However, since the economic data presented indicated

the possibility of a slight steepening of the curve in the near future in North America and the

Eurozone (and larger changes in Australia and New Zealand), and also given the general rise in rates,

portfolio positioning can be undertaken in anticipation.

A general rise in rates poses the most concern for instruments with higher duration. From the

interest rate exposure graph and the DV01 graph, North America appears to have higher duration

and is expected to experience greater losses if rates rise. There are several ways a manager can alter

the portfolio. First, existing assets can be shifted to lower-duration assets (from US to Euro, for

example). Second, existing high-duration bonds can be swapped for new bonds in the same category

but with lower duration. And finally, the manager can enter into interest rate agreements, typically

interest rate futures. To lower portfolio duration, a manager would typically short (sell) interest

rate futures.

The expected steepening of the yield curve poses different questions and challenges. Non-parallel

shifts like a steepening are resolved using three common strategies: bullet, barbell, and ladder. A

bullet strategy concentrates the portfolio around a single point on the yield curve. A barbell strategy

concentrates the portfolio around two points on the curve, typically on either side of a potential

bullet and a ladder strategy evenly allocates the assets across the yield curve points.

None of the strategies are exclusive to any yield curve change. That is, bullets may not always be

employed for flattenings, or ladders will not always be employed for shifts. The choice among the

three strategies will depend on a thorough analysis of duration and convexity of each asset and the

anticipated changes in the yield curve at every point. A robust attribution system should provide

the duration and convexity for each bond under such an analysis.


a practical example cont.


From the yield curve risk factors, the discussion shifts to non-yield curve risk factors, which could

include optionality, volatility and pre-payments. Again, this requires a thorough analysis of each

bond. For example, when assessing callable bonds in light of rising rates, a manager would typically

examine the convexity figures of each bond and assess the impact the negative convexity inherent in

callable bonds. Callable bonds are also affected by interest rate volatility (vega). Both changes in

rates and in volatility can make up a sizable portion of the outperformance between callable and

non-callable bonds. Rising rates also pose issues for mortgage pools as changes in rates can have

simultaneous increases and decreases in prepayments, although not to the same degree. An

attribution system with full pricing functions should be able to provide a manager with convexity

numbers, vega contribution, and the impact of rising rates on the price of a mortgage pool.

Issuer-specific risk factors generally centre on the credit quality of an issuer, which can be affected

by sector, capital structure and company-specific operational concerns. The RBC Economics analysis

included does not break down anticipated credit spread movements by sector, but it generally

implies that there will be a tightening of credit spreads across the board. From the exposure graphs,

the portfolio stands to make gains in an environment of credit spread tightening, more so in North

America than in any other region.

It is important to note that the impact of all risk factors (yield curve, non-yield curve, and issuer) is

simultaneous. While a manager may want to move bonds from higher duration US bonds in light of

rising rates, this decision must be tempered with the realisation that US bonds stand to gain the most

in a credit spread narrowing environment. An analysis of each bond and its corresponding risk

numbers from the attribution system would be helpful in this exercise.


summary

Allocations to fixed income instruments are continuing upwards,

suggesting that it is now more important than ever before to have

a clear line of sight to where returns have been generated and

where they are expected to be generated. Moreover, investment

professionals and investors today are demanding greater

transparency and targeted solutions.

While conventional Brinson-based attribution models have

advantages, they are limited in their ability to provide useful and

relevant fixed income oversight. Traditionally, these models do

not incorporate the more targeted attributes that are unique to

fixed income portfolios. Encompassing attributes such as interest

rate sensitivity, convexity and optionality facilitates a more

precise examination of fixed income portfolio returns. Combining

that approach with a sophisticated attribution tool that aligns

with the fixed income investment process also contributes to

more informed and strategic investment decisions.

Fixed income investing is complex and requires a combination of

ongoing assessment, monitoring as well as critical decision-

making in response to macroeconomic and dynamic market

conditions. Complementing your fixed income management with

an analytical model that captures the distinct qualities associated

with that strategy can play an essential role in adding quantitative

and qualitative value that benefits all stakeholders.

Fixed income investing is complex and requires a combination of ongoing assessment, monitoring as well as critical decision-making in response to macroeconomic and dynamic market conditions.


appendix


Source: RBC Economics Research, Economic and Financial Market Outlook


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