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FIXED INCOME

ATTRIBUTION

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table of contents

fixed income attribution

01 | Foreword

02 | Fixed income in demand

03 | Generating additional returns

04 | New fixed income tools

05 | Limitations of traditional attribution models

06 | Designing a new attribution solution

07 | Refining the approach

08 | Assembling the blocks

10 | A practical example

15 | Summary

16 | Appendix

18 | About

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Today’s 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.

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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 hedge—namely 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 today’s 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 Cintioli

Product Manager Product Director RBC Investor Services StatPro

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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 types—particularly 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% Equity 40% Money Market 18% Bond 20% 10% 12% Equity Money Market Bond 24% 11%

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85 75 65 55 45 35 25 15 Ju n ’ 05 Ju n ’ 08 Sep ’ 05 Sep ’ 08 Dec ’05 Dec ’08 M ar ’ 06 M ar ’ 08 M ar ’ 11 Ju n ’ 06 Ju n ’ 09 Ju n ’ 11 Sept ’06 Sept ’09 Sept ’11 Dec ’06 Dec ’09 Dec ’11 M ar ’ 07 M ar ’ 09 M ar ’ 12 Ju n ’ 07 Ju n ’ 10 Ju n ’ 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 portfolio’s 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”

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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.

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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 manager’s 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 manager’s 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 portfolio’s 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 =∑ßnFn+residual (where ß 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.

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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 bond—the 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.

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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 bond’s sensitivity to these changes will provide a clear picture of where returns were made.

Source: RBC Investor Services’ Investment Analytics Interactive

pricing

functions risk sensitivity numbers

changes in risk factors

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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.

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Source: RBC Investor Services’ Investment Analytics Interactive Source: RBC Investor Services’ Investment Analytics Interactive

Source: RBC Investor Services’ Investment Analytics Interactive

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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 fund’s 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 sector’s weighting increased above the original 50% allocation to 52.5% while also contributing more to the overall return. The portfolio’s response to changes in credit spreads had the largest influence on the portfolio’s 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.

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Source: RBC Investor Services’ Investment Analytics Interactive

Source: RBC Investor Services’ Investment Analytics Interactive

Portfolio

returns are

dependent on

a variety of

contributing

factors.

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Source: RBC Economics Research, Economic and Financial Market Outlook

Source: RBC Investor Services’ Investment Analytics Interactive Source: RBC Investor Services’ Investment Analytics Interactive

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

Another important assessment is to investigate the portfolio’s 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.

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a practical example cont.

Source: RBC Investor Services’ Investment Analytics Interactive

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.

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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.

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rbc investor services

RBC Investor Services is a specialist provider of investor services to asset managers, financial institutions and other institutional investors worldwide. Our unique approach to domestic and cross-border solutions, combined with award-winning client service and presence in 15 markets, helps our clients achieve their ambitions.

RBC Investor Services ranks among the world’s top 10 global custodians with USD 3.0 trillion (CAD 3.0 trillion) in client assets under administration and is a wholly owned subsidiary of Royal Bank of Canada, one of the largest and most financially sound banks in the world.

statpro

StatPro is a global provider of portfolio analytics for the investment community. Our cloud-based services provide vital analysis of portfolio performance, attribution and risk. Hundreds of investment professionals use our cloud services directly or through a fund administrator/partner to perform analysis, reporting and distribution every day.

With nearly 20 years of experience and expertise, we believe analytics should be sophisticated yet simple and useful as well as secure. StatPro data coverage includes global equities, global bonds, global mutual funds, most families of benchmarks, FX rates, sector classifications and much else besides.

StatPro has grown its recurring revenue from less than £1 million in 1999 to around £30 million at end December 2012 and currently enjoys a renewal rate of approximately 93%. StatPro floated on the main market of the London Stock Exchange in May 2000 and transferred its listing to AIM in June 2003. The Group has operations in Europe, North America, South Africa, Asia and Australia and approximately 350 clients in 30 countries around the world. Approximately 80% of recurring revenues are generated outside the UK.

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For more information on fixed

income attribution or to discuss

other risk and investment analytics

services and solutions, please

contact us:

rbc investor services americas

John Lockbaum

Head, Investor Services, Canada john.lockbaum@rbc.com

Brent Reuter

Head, Investor Services, US brent.reuter@rbc.com

europe, middle east and africa Sébastien Danloy

Head, Investor Services, Europe & Offshore sebastien.danloy@rbc.com

Padraig Kenny

Head, Investor Services, Ireland padraig.kenny@rbc.com

Simon Shapland

Head, Investor Services, UK simon.shapland@rbc.com

Philippe Legrand

Head, Investor Services, France philippe.legrand@rbc.com

Marco Siero

Head, Investor Services, Switzerland marco.siero@rbc.com

José María Alonso-Gamo

Head, Investor Services, Spain jm.alonsogamo@rbc.com

Mauro Dognini

Head, Investor Services, Italy mauro.dognini@rbc.com

Marc Vermeiren

Head, Investor Services, Belgium marc.vermeiren@rbc.com

Cormac Sheedy

Senior Executive Officer, Middle East and Africa

cormac.sheedy@rbc.com

asia pacific David Travers

Head, Investor Services, Asia Pacific david.travers@rbc.com

Andrew Gordon

Head, Investor Services, Hong Kong and North Asia andrew.gordon@rbc.com

Diana Senanayake

Head, Investor Services, Malaysia and Singapore diana.senanayake@rbc.com

statpro Andrew Peddar

Group Chief Operating Officer, Boston andrew.peddar@statpro.com

Dario Cintioli

Product Director, Milan dario.cintioli@statpro.com

Kate Maryniak

Head of Business Analysis, London kate.maryniak@statpro.com

James Harkness

Manager Business Development, Toronto

james.harkness@statpro.com

Chris Leverette

Risk Analyst, Client Services, Toronto chris.Leverette@statpro.com

Rachael Cooper

Global Marketing Manager, London rachael.cooper@statpro.com @Rachael_StatPro

Swati Bhoumick

Marketing Manager, Boston swati.bhoumick@statpro.com @Swati_StatPro

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References

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