WM CIO Global Asset Allocation – Investing with UBS WM

Investing with
UBS Wealth Management
a b
The concepts behind our solutions
WM CIO Global Asset Allocation
Mads N. S. Pedersen, Group Managing Director, UBS CIO WM Global Investment Office, Head Global Asset Allocation
Christophe de Montrichard, Managing Director, UBS CIO WM Global Investment Office, GAA Head Strategic Asset Allocation
Carolina Moura-Alves, Managing Director, UBS CIO WM Global Investment Office, GAA Head Fixed Income Strategies
Oliver Malitius, Executive Director, UBS CIO WM Global Investment Office, GAA Head Quantitative Strategies
Philipp Schöttler, Executive Director, UBS CIO WM Global Investment Office, GAA Head DM Credit Strategies
Katarina Cohrs, Director, UBS CIO WM Global Investment Office, GAA TAA & Investment Methodologies
For marketing purpose only
This publication does not constitute UBS independent research as it has
not been drafted in accordance with the statutory regulations regarding
the independence of financial research.

Foreword
We face a world in transition. The drivers
of global growth are changing, valuations
of many assets are high, and we expect to
enter an environment of rising US interest
rates for the first time in close to a decade.
Successfully guiding portfolios through
this world in transition will undoubtedly
raise various questions:
How to protect against downside in an
increasingly volatile market? How to build
effective fixed income portfolios in an
environment of rising rates? How to gen-
erate strong risk-adjusted performance
when little excess return is on offer in tra-
ditional markets?
In the following pages we introduce a
series of new investment concepts to
answer them.
We are not the only ones trying to answer
these questions. But by applying the
advanced methodologies of some of the
most successful risk managers, hedge
funds, and endowments for use by private
investors, we believe CIO has created a
unique set of solutions.
These new investment concepts represent
the latest step in on our road to enhance
the investment content we provide, and
we look forward to engaging with you all
to make this content as relevant for your
needs in the future.
We hope you find this publication infor-
mative and helpful in navigating portfolios
through a world in transition.
Mark H. Haefele
Global Chief Investment Officer
Jürg Zeltner
President
Jürg Zeltner Mark H. Haefele

Introduction
Dear reader,
In the Chief Investment Office (CIO) in UBS Wealth
Management, our mission is to help our clients preserve
and grow their assets. In the CIO Global Asset Alloca-
tion team, we believe that clients can best achieve their
goals by investing in global portfolios that profit from
the only free lunch in finance, diversification.
This document introduces you to investment concepts
which, in various ways, take advantage of such global
diversification benefits. These concepts form the back-
bone of the investment solutions UBS Wealth Manage-
ment offers clients in its discretionary and advisory
products and mandates. The cutting-edge methodology
we employ in our portfolios spans the range of invest-
ment management approaches: from defining our tradi-
tional Strategic Asset Allocations (SAAs); through
systematically managing portfolio risk exposure in Sys-
tematic Allocation Portfolios; harvesting liquidity
premia in global bond markets to create well-balanced
Global Credit Opportunities; to building portfolios
inspired by the large university endowments that explic-
itly benefit from the greater return potential of private
market investment, a concept we call for simplicity’s
sake Endowment Style Portfolio.
Our Strategic Asset Allocation concept, with associ-
ated portfolios covering various risk levels, is meant for
investors who seek the best trade-off between expected
return and expected risk via investments in traditional
relatively liquid global markets.
Some investors become understandably concerned or
nervous when stock markets experience a large correc-
tion, though they are comfortable holding equities
through smaller drawdowns. The Systematic Alloca-
tion Portfolio concept, by drawing on the principles
of market momentum and the persistence of trends in
asset classes like bonds and equities, tailors to these
investors. This concept has historically outperformed
traditional SAAs over the last 20 years, and we believe
will continue doing so provided the world remains a
place where financial markets exhibit persistent trends.
We are aware that other investors focus on income
generation, and feel uncomfortable with the volatility
associated with equity investments. While we ordinarily
advise clients to take advantage of “full” diversification
across all main asset classes, we respect this investment
constraint and have developed the Global Credit
Opportunities concept in response. We partially offset
the lack of equity investment by allocating to a well-
diversified set of credit sub-asset classes – across
regions, currencies and central banks, credit quality and
the liquidity spectrum. These allocations nonetheless
come with a cost: lower liquidity than one has in tradi-
tional portfolios.
Investors who take a very long-term view and do not
require “liquidity,” or the ability to buy and sell individ-
ual asset classes or an entire portfolio in any given year,
should consider investing according to the principles
established by large university endowments, i.e. follow-
ing the Endowment Style Portfolio. This concept
departs from traditional SAAs by taking large positions
in illiquid investments in private markets, real estate,
private debt and private equity. It aims at preserving
capital and wealth over many generations, and we
recommend that the investments be built up over a
number of years.
For investors who wish to keep it simple, by having a
globally well diversified portfolio with an optimal asset
class mix, but don’t wish to run the risk or harvest the
benefits of active selection of individual bonds or equi-
ties, we have constructed a set of Global Beta Port
folios tracking as closely as possible the individual asset
classes on index level.
Thank you and kind regards,
Mads N. S. Pedersen
Head Global Asset Allocation
Mads N. S. Pedersen

4
Contents
02 Foreword
03 Introduction
05 Strategic Asset
Allocation (SAA)
Methodology and
Portfolios
21 Systematic
Allocation
Portfolio (SAP)
43 Global Credit
Opportunities (GCO)
Portfolio
57 Endowment-Style
Portfolio (ESP)
77 Global Beta Portfolio
(Gl. BP)
SAP-Nr.: 84612EN
April 2016

Strategic Asset Allocation
(SAA) Methodology and
Portfolios
SAAs: The driving force of our portfolios
a b

Please always read in conjunction with the glossary and the risk information at the end of the document. 7
Strategic Asset Allocation
(SAA) Methodology and
Portfolios
SAAs: The driving force of our portfolios
a b
SAAs: What they are and why they are important
The strategic asset allocation (SAA) constitutes the backbone of a long-term
investment portfolio. It structures a portfolio at the asset class level to match the
specific investment objectives and risk tolerance of clients (their Financial Situa-
tion and Personality) while offering them the best risk/return trade-off for the
given level of risk accepted. Creating the right portfolio for the long run lies at
the heart of how we advise on our clients’ wealth, and the SAA is integral to it.
Our SAAs will be the main driver of our portfolios’ performance contributing
about 80% to the portfolios’ risk and return over time. By design the bulk of
each portfolio is allocated to longer term investments. In this sense, the SAA is
even more important than short-term market timing or securities selection,
which are also parts of our investment approach.
SAAs: What they are based on
Diversification is the only free lunch1
in the investment world. A diversified port-
folio combines a number of different asset classes with different risk and return
characteristics. The asset classes range from government bonds through corpo-
rate credit to high yield and emerging market bonds; from developed and emerg-
ing market equities to hedge funds and private markets. By constructing appro-
priate SAAs one can achieve a better risk/return ratio than would be the case
with a narrower portfolio consisting of fewer asset classes – or even just a single
asset class. Ultimately, the optimal SAA is one that, relative to others, realizes
better returns while bearing less risk.
1
AQR Ilmanen & Villalm
“Alpha beyond expected
returns” 2012.
UBS Strategic Asset Allocations are an essential part of our disciplined style of managing
and growing our clients’ wealth. These SAAs ensure that our clients remain on course to
their financial goals and steer clear of common investment dangers by investing in a well-
diversified manner. Our SAA methodology is anchored on our experts-based Capital Market
Assumptions (CMAs) and the annual review process of our SAAs and CMAs. This method-
ology is also the basis for our other CIO solutions such as the Endowment-Style Portfolio
(ESP) and the Global Credit Opportunities (GCO) Portfolio which complement our tradi-
tional SAAs Portfolios with additional investment concepts and return drivers.
Please always read in conjunction with the glossary and the risk information at the end of the document.

8 Please always read in conjunction with the glossary and the risk information at the end of the document.
SAAs: Begin with the right proﬁle
Whether your primary investment objective is to protect or to grow your wealth,
understanding your objectives, investment time horizon, and risk tolerance, is
crucial. Hence, we have developed different SAAs which exhibit different levels
of risk in order to offer portfolios fitting different types of clients.
SAAs: The fruit of quantitative and qualitative analysis
Constructing portfolios involves choosing not only the right asset classes but
also the optimal asset weighting to achieve the best possible return for the low-
est amount of risk. We base our portfolio building on a solid quantitative meth-
odology that combines risk estimations based on factor analysis2
over several
business cycles to derive risk estimations including asset class volatilities and
correlations. We complement this statistical estimation of risk with return expec-
tations for the next single business cycle from our asset class experts. The com-
bination of these risk and return elements constitutes our Capital Market
Assumptions (CMAs) which represent our expectations of markets over the next
5 to 10 years. This dual approach enables us to apply our seasoned market
judgment within a robust quantitative framework, and is a method favored by
such leading institutional investors as the Yale Endowment3
. Ultimately these
quantitative and qualitative inputs enable our Asset Allocation team to devise
each of our Strategic Asset Allocations (SAAs).
SAAs: A dynamic process
To ensure reasonable assumptions we review our SAAs and CMAs on an ongoing
basis and expect SAA adjustments every 18 to 36 months. One of the elements
triggering these adjustments is changes to our Capital Market Assumption
(CMAs). These represent our expectation for each asset class of return, volatility
and correlations over 5 to 10 years. These are reviewed at least annually or after
major market adjustments. Hence our SAAs are anchored on our long-term views
but also account for structural market adjustments over time (for examplethe
“new” interest rate environment post the Global Financial Crisis).
2
Professor Heinz Muller –
Consultant from
St. Gallen University
3
Annual Report – 2012
The Yale Endowment –
Investment Policy
SAAs: How clients can take advantage of them
We believe that achieving superior investment results depends on an SAA at the
core of one’s investment portfolio. SAAs can be implemented in the form of a
discretionary solution that UBS manages, or/and as part of a set-up in which
clients adhere to a certain portfolio structure or plan but direct and decide on the
specific investments themselves. These portfolios are constructed to provide
an expected return in line with each client’s financial aspirations and within a
reasonable investment time frame (five to ten years for most clients).
2Please always read in conjunction with the glossary and the risk information at the end of the document.
Strategic Asset Allocation (SAA) Methodology and Portfolios
Asset allocation that meets the challenge
of change
Constructing and managing SAAs at UBS is a complex
team undertaking. It involves our most experienced strate-
gist and asset class experts; and includes our risk office
professionals and quantitative portfolio construction spe-
cialists. All of them provide their insight to our Global
Chief Investment Office (GCIO), where our asset alloca-
tion team constructs internationally diversified portfolio
strategies tailored to our clients.

Please always read in conjunction with the glossary and the risk information at the end of the document. 9 3Please always read in conjunction with the glossary and the risk information at the end of the document.
SAA construction in detail
Defining our SAA approach
Our SAA approach entails a predefined asset class allocation toward which the
portfolio is to be rebalanced either at regular intervals or when some predefined
deviation limits are reached. This approach aims to keep the portfolio within a
predefined risk level while generating returns from both the long-term return
expectations of each asset class and from the mean-reverting behavior of asset
class performance.
This approach rests on our assumptions of the long-term up-trending perfor-
mance of those asset classes we select while accepting expected short to mid-
term drawdowns and market volatility – the level of return and risk depends on
the chosen SAAs. The SAA approach focuses on asset classes (for example US
equities) and not single securities (for example, Company XYZ). We find that in
the long-term, an asset allocation approach to investing is more predictable and
offers a better risk/return ratio than other approaches, such as shorter-term
market timing and security selection. However, these other strategies can also
add value to a portfolio in certain cases and we recommend to implement them
in conjunction with an SAA, although they should not constitute the main driver
of the portfolio’s long-term risk and return.
The art and science of creating SAAs
Constructing an SAA is both an art and a science; it requires a robust quantita-
tive framework and seasoned judgment. The quantitative framework supplies a
detailed understanding of the behavior of financial markets – how different
markets behave differently during different economic periods. Our qualitative
assessments – i.e. the seasoned judgment provided by our asset class and asset
allocation experts – complement this framework by capturing the subtleties,
dynamic nature, structural changes and likely future developments of various
markets.
Our construction process involves (see ﬁg. 1):
• Defining the investment universe
• Estimating multi-business-cycle “equilibrium” asset class returns and
a covariance matrix
• Estimating single-business-cycle (five-to-ten-year) asset class returns
• Consolidating asset class estimates within one quantitative platform
• Constructing SAAs based on optimal risk and return trade-off, including
testing portfolios across history and possible future market stress
Figure 1: UBS CIO SAA construction process
Final SAA
Decision
Consolidation
Covariance
Matrix & Return
Estimates
Single Business
Cycle Expected
Returns
Investment
Universe –
Deﬁnition
and Analysis
Multi-Business
Cycle “equilibrium“
Covariance Matrix
& Returns
For illustrative purposes only.

10 Please always read in conjunction with the glossary and the risk information at the end of the document.
The three main contributors to SAA construction at UBS
1. A quantitative SAA construction platform
To construct the SAAs we bring together on a quantitative platform quantitative
and qualitative estimations for each asset class and across all asset classes. We
can then perform various optimizations and simulations which are used as input
by the CIO asset allocation experts. The key functions within the quantitative
tool include:
a. Time series analyzer – per asset class
b. Capital market assumptions (CMAs)
b(1) Multi-business-cycle “equilibrium” returns and covariance matrix
b(2) Integration of single-business-cycle expected returns (5 year horizon)
c. Optimization and simulation functions
a. Time series analyzer
A thorough understanding of the behavior of each market is an essential step
in portfolio construction. We need to know how assets behave during different
market cycles and shocks, both individually and in relation to each other.
First, we analyze each asset class individually and look, among others, at the fol-
lowing parameters: 1) the return probability distribution, including skewness
and kurtosis; 2) the volatility, both up and down; 3) the drawdowns and time
under water; 4) the return patterns during financial crises; 5) the rolling returns
and volatility during different holding periods; 6) the related risk-adjusted
returns; and 7) liquidity constraints.
Second, we analyze the co-movement of asset classes, i.e. their correlations in a
covariance matrix. This analysis is performed over different rolling periods (example:
rolling three and five-year windows). Furthermore, the change in correlations over
time is assessed to determine patterns such as increasing correlations during mar-
ket crises. This analysis allows us to determine whether changes in correlation over
time are of a cyclical nature or the result of structural changes in the economy and
to analyze the effects of short-term adverse deviations from “normal” patterns. For
examples, see fig. 2.

b. Capital market assumptions (CMAs)
Constructing internally consistent CMAs enables us to bring together many of the
risk and return parameters of each asset class used in our SAAs to estimate the over-
all expected return and volatility of any given SAA. The CMAs consist of two key
elements: 1) the covariance matrices (volatility and correlations) and 2) the expected
risk/return premiums (based on risk factors and described in detail in fig. 3). The CIO
CMAs blend are multi-business cycle “equilibrium” estimations with our asset class
experts’ single-business-cycle expected returns. Within the quantitative framework
several quantitative optimization functions are available based on the CMAs. They
are used as inputs by our asset allocation experts to create the UBS SAAs.
The CIO CMAs are reviewed annually or after major market adjustments. Hence,
annually each CIO asset class expert provides an update of their specific asset
class long-term return estimate. Also the covariance matrix is recalibrated annu-
ally to include the respective asset classes’ latest time series data. The estimates
for the money markets will be updated monthly to account for on the ongoing
central bank rate adjustments. The update of the CMAs is provided as input for
several other parts of the investment process including the Investor Profile and
investment solutions material which display for investors the expected risk and
return expected for each CIO SAAs and related investment solutions.
Source: UBS
Figure 2: Time series analysis of Equities (Economic and Monetary Union)

12 Please always read in conjunction with the glossary and the risk information at the end of the document. 6Please always read in conjunction with the glossary and the risk information at the end of the document.
For illustrative purposes only. Source: UBS CIO
Figure 3: CMA construction
b(1) Multi business cycle “equilibrium” covariance matrix
The covariance matrix consists of estimates of volatilities and correlations for
each asset class. These estimates are considered “equilibrium” estimates that
describe the average behavior of assets over different market cycles. The esti-
mates are based on the longest time series available for each asset class, which
is generally more than 20 years and includes three to four business cycles.
The long-term covariance matrix is chosen for two reasons. First, correlation
forecasting is prone to estimation errors. For instance, while we know that cor-
relations tend to increase during crisis periods and take this into account within
our simulation framework, it is difficult to predict changing correlation patterns
between asset classes. Second, we find that volatilities over a mid to long-term
holding period are generally stable; hence we find that the long-term history is a
reasonable base case assumption for the forward-looking covariance matrix.
Given that we estimate the covariance of over 100 different asset and sub asset
classes, we cannot do a direct historical estimation. Such an approach would
necessitate longer time series than are historically available and could result in an
inconsistent matrix without the mathematical properties needed for optimiza-
tion and/or simulation purposes. To circumvent this we have developed a propri-
etary factor approach to our covariance construction in which we model the
identified asset classes based on a subset of market factors.
We have a selection of ”factors” that we believe broadly represent the global
financial markets. All other asset classes are regressed either directly against this
set or indirectly via a layering process. The regression process is iterative to allow
the analysis of correlation patterns over different periods and thus take into
account any structural changes in the economy (for example, the creation of the
Eurozone).

4
Including Prof. Heinz
Mueller of the Faculty
of Mathematics and
Statistics at St. Gallen
University, Switzerland
This construction process has been established and is maintained by the UBS
quantitative team. The work is done in collaboration with and supervised by lead-
ing academics in the field4
. The resulting covariance matrix is both fully consis-
tent (i.e. positive definite) while incorporating as much information as is available
for each asset class (use of the longest possible time series per asset class). See
fig. 4 for an illustration.
Source: UBS
Figure 4: Factor regression approach and the resulting correlation matrix
b(2) Experts for each asset class – integration of single-business-cycle expected
returns
The estimated returns for each asset class are based on a combination of the
implied “equilibrium” returns and adjustments based on return estimations for
the next five to ten years developed by the respective asset class experts within
the Asset Allocation team in the CIO WM Global Investment Office. This dual
approach provides a robust quantitative framework for forecasting long-term
returns while incorporating our assessment of certain imbalances we find in cer-
tain markets.

The “equilibrium” approach assumes that financial markets are in balance and
that investors will be compensated with returns proportional to the inherent risk
of each asset class and to its particular characteristic within the broad invest-
ment universe (including correlation, liquidity, etc.). Deriving “equilibrium”
assumptions starts with calculating the amount of return investors expect per
unit of risk (calculated as volatility) over the long term. This calculation is based
on the equity risk premium (ERP), which represents the return above a “risk-
free” investment (such as highly rated government bonds) that investors expect
over the long term for investing in equities5
. The ERP is used as the reference
point for a process called “reverse optimization,” which calibrates the risk pre-
miums of all other asset classes based on their individual risk, long-term correla-
tions (found in b.1), size within the investment universe, and the ERP itself.
In the short to medium term, the ERP may be lower or higher than its “equilib-
rium” value depending on where one is within the business cycle or current
aggregate investor market sentiments (“fear & greed”) among other factors.
We take these deviations into account when estimating expected returns over
our chosen forward-looking five to ten-year horizon. Furthermore, we estimate
the speed and magnitude of the change in this deviation over time as the ERP
converges to its long-term level. The assessment of the deviation and conver-
gence path of the ERP is based on the medium-term return expectations of our
asset class experts. The adjustments from “equilibrium” value that they estimate
are done both on the ERP itself and on individual asset classes. Many asset
class-specific issues come into account within the asset class expert estimations
which also justify deviation from “equilibrium” risk premium (for example for
corporate bonds: spread compression, default/recovery rates, etc.). A short
description of how the five to ten-year expectations are derived is found below
in section 2: Asset class experts – expected returns.
The ”risk-free” investment (such as highly rated government bonds) is also used
as a building block for the annual calculation of the long-term expected returns.
The expected return for each asset class is the sum of the asset class’ expected
premium (derived as described above) and the expected ”risk-free” rate. We
use highly rated government bonds as a proxy for the ”risk-free” rate. As we do
for the ERP, we estimate both an ”equilibrium” value and a development path
from the current (i.e. possibly non-”equilibrium”) level to the long-term value of
the ”risk-free” rates. The ”risk free” rate is updated monthly to account for any
central bank rates adjustments over time. The calculation of each asset class
total return is however set annually as described above.
c. Optimization and simulation functions
A proprietary quantitative tool captures all the UBS CMAs and provides port-
folio analytical capabilities including optimization, simulation function. This tool
(see fig. 5) enables us to conduct several optimization functions based on the
CMAs described above, including the volatility, return and correlation estimates.
Hence, we can establish related estimation of the efficient frontier, in both con-
strained and unconstrained portfolios. As previously mentioned, the efficient
frontier is not considered as a final SAA but rather as a key input used by our
asset allocation experts to decide final SAA proposals.
5
See Fernandez [2008]
and Asness [2011].
The “risk free” rate
is a theoretical rate
investors could expect
with no risk. In practice,
however, highly rated
government rates are
used as proxies for
this rate and they may
include some risk of the
government not paying
back its debt.

Another important function needed to assess the parameters of each SAA is a
forward-looking Monte Carlo simulation engine – a proprietary tool which
attempts to simulate multiple different hypothetical market conditions to esti-
mate the possible dispersion of performance results (see fig. 6). It enables us to
determine a statistical distribution of the different expected outcomes of an
SAA’s performance over time from best to worst, as well as their related devel-
opment paths. Based on this simulation tool we can then assess the expected
probability distribution for individual SAAs. This approach takes into account not
only the specific return distribution of each asset class in the portfolio but also
their correlations. Furthermore, these simulations can factor-in decreases or
increases of correlations among asset classes as seen during certain market
periods in order to estimate possible portfolio outcomes during these scenarios.
Source: UBS
Figure 5: Optimization Suite
Source: UBS
Figure 6: Monte Carlo simulation tool

2. Asset class experts – expected returns
Asset class experts within the CIO Global Chief Investment Office calculate the
return that each asset class will generate over the next five to ten years. Their
estimates are anchored in an “equilibrium” framework that takes into account
the likely risk patterns and related returns across asset classes. This assessment is
supplemented by business cycle-specific evaluations and anticipated develop-
ments in yields to determine the likely total return outcomes from both a top-
down and a bottom-up approach. The final return estimates also include the
multifaceted and asset class-specific modeling of each asset class expert. They
are derived from a peer-reviewed methodology and a common set of macro-
economic expectations for the period. This dual approach (“equilibrium” statisti-
cal modeling + asset expert modeling) ensures a consistent approach across
asset classes while considering each asset class’ specific factors on a forward-
looking basis.
We develop a path of expected returns per asset class over the period. For
instance, if we expect short-term interest rates to rise, this information is reﬂected
in the quantitative simulation tool.
Yield surfaces – calculating ﬁxed income expected returns
The yield surfaces we develop for each relevant bond currency constitute the
core of our approach to estimating the expected return of fixed income asset
classes. They represent our forecast for the “risk free” yield curve (e.g. US Treas-
ury, German sovereign, etc.) over the next 10 years, and form the basis of our
expected total return calculation for government and credit bonds over the SAA
time horizon.
Long-term bond returns depend on two main components: 1) the “risk free”
rate, i.e. the rate at which highly rated governments borrow (example: US
10-year Treasury bonds); and 2) the credit spread, i.e. the additional compensa-
tion investors require to assume the risk of default (and other risks, e.g. liquidity,
regulatory, etc.) associated with a given bond issue. These two components are
usually described simply as the “risk free” (i.e. government bond) rate of return
and the credit risk (i.e. credit spread) rate of return. For any finite time horizon,
bond returns also depend on changes in the yield surface that lead to both loss/
gains and changes in yield levels.
The “risk free“ rate of return depends for the most part on the duration of a
comparable “risk free” bond investment and the expected evolution of “risk
free” yields, i.e. the yield surface. Specifically, the yield surface represents the
current “risk free” yield curve, i.e. the current bond yield level for each bond
maturity, and our forecast for the next 10 years of the changes to each maturity-
specific yield. To illustrate, we show below the current yield curve and our
expected yield curve in 10 years’ time for both USD and EUR in fig. 7. We can see
the current one-year USD government yield around 0.6%, and we expect it to
rise over the next 10 years to about 3%, while the yield of the 10-year US gov-
ernment bond is expected to increase from 1.9% to 3.4% over 10 years.

Our forecasts indicate rising government interest rates for both USD and EUR
that will stem from the US Federal Reserve and the European Central Bank
normalizing their monetary policy. Rising interest rates do not necessarily imply
negative returns for high grade and credit sub-asset classes over the medium
term, as shown in the table detailing the asset allocations.
We construct the yield surface by combining qualitative inputs from CIO Global
Chief Investment Office fixed income experts with proprietary quantitative mod-
els. Our approach uses quantitative models to forecast the short end (money
market rate) and the long end of the yield curve (10-year maturity) over a five-
to-10-year period. We then derive the yield curve for each year by interpolation.
Since our models are designed to describe long-term equilibria yield curves, we
reﬂect our short-term expectations of yield curve developments through a
qualitative overlay.
Using the yield surface (see fig. 8) we can mechanically derive the expected
return for different types of “risk free” bond investments. This approach is suit-
able for both “buy and hold to maturity” strategies (e.g. buying and holding a
single bond) and for duration-targeted approaches (e.g. purchasing a fund which
replicates a bond index that contains bonds with maturities of five to seven years
only). In the process we account for the income generation, the roll-down effect
and the reinvestment of interest.
Ultimately, yield surface return modeling provides a framework for systematically
capturing both the evolution of yields and how this affects the expected return
of different types of “risk free“ bond investments. By breaking down the com-
ponents of bond returns and identifying and forecasting their respective return
drivers, we can be more confident in our return estimate.
-1.0%
-0.5%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
1 2 3 4 5 6 7 8 9 10
Yield
Maturity
USD Yield Curves
in 10 years
Current
-1.0%
-0.5%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
1 2 3 4 5 6 7 8 9 10
Yield
Maturity
EUR Yield Curves
in 10 years
Current
Make sure this version is used in all papers
with this chart.
Also change yield surface charts to "2016"
as already done in ESP paper
Source: UBS, February 2016
Figure 7: USD and EUR yield curve expectations

We also forecast the course of credit spreads and corporate defaults as well as
recovery rates for each credit sub-asset class included in the SAAs. We combine
these forecasts with the yield surface for the relevant currency to generate our
long-term return expectations for the whole fixed income investment universe.
3. Asset allocation experts – SAA decision
Building on the quantitative platform described above and its expert assessment,
the Global CIO asset allocation team determines the SAA compositions (see
table 1 for 2016 allocations). The objective is twofold: Firstly, the SAAs should
take advantage of the best possible return drivers and low correlations between
asset classes, hence minimizing the expected risks for any given level of expected
return. Secondly, the SAAs construction should take into account the uncertainty
of forecasting, hence spread risk among risk factors and markets.
The SAA composition considers several factors in the final mix which include:
1) optimizations functions including mean-variance, diversification-index and
maximum drawdown-based approaches (using both “equilibrium” and expert
based return estimates), 2) stochastic simulation (Monte Carlo), and to assess
potential “fat-tail” events 3) stress test scenarios (historic and prospective) and
4) maximum drawdown and recovery analysis.
Finally, it is important to acknowledge that financial forecasting is an uncertain
endeavor with a multitude of input factors that can only be approximately
quantified and/or modeled, if they can be at all. Determining the composition
of each SAA is therefore not a purely quantitative optimization. The final SAA
decision lies with the CIO asset allocation team and is based on the extensive
and multifaceted quantitative/qualitative evaluations mentioned above.
USD Yield
Surface
in 10 years
2016
CHF Yield
Surface
in 10 years
2016
Header
Multi-Business
Cycle "equilibrium"
Covariance Matrix
& Returns
Investment
Universe-
Definition
and Analysis
Consolidation
Covariance
Matrix & Return
Estimates
Single Business
Cycle Expected
Returns
Optimization
& Final SAA
Decision
Header
Multi-Business
Cycle "equilibrium"
Covariance Matrix
& Returns
Investment
Universe-
Definition
and Analysis
Consolidation
Covariance
Matrix & Return
Estimates
Single Business
Cycle Expected
Returns
Final SAA
Decision
Source: UBS
Figure 8: USD and CHF yield surfaces

Source: UBS CIO
The above asset classes and allocations are indicative only and can be changed at any time at UBS’s discretion without informing
the client. Information valid as of 2016. Please always read in conjunction with the glossary and the risk information at the end of the
document.
Table 1: UBS CIO SAAs in USD including Capital Market Assumptions (CMAs)
USD Fixed Income Income Yield Balanced Growth Equities
FX
Hedged
Expected
5 Yrs
Return
p.a.
Expected
Volatility
p.a.
LIQUIDITY 5% 5% 5% 5% 5% 5% 0%
Cash USD 5% 5% 5% 5% 5% 5% 2.1% 0.5%
BONDS 95% 69% 50% 33% 17% 5% 0.0% 0.0%
USD high grade bonds 1-3 years 10% 0% 0% 0% 0% 0% 1.8% 1.6%
USD corporate bonds 1-5y 7% 4% 0% 0% 0% 0% 2.5% 3.0%
USD corporate intermediate bonds (IG) 23% 20% 15% 8% 2% 0% 2.7% 4.2%
USD high yield bonds 3% 3% 3% 3% 3% 0% 5.0% 8.8%
EUR high yield bonds 2% 2% 2% 2% 2% X 4.3% 8.5%
EM sovereign bonds (USD) 3% 3% 3% 2% 3% 0% 5.4% 9.1%
EM corporate bonds (USD) 2% 2% 2% 2% 0% 0% 4.8% 9.9%
EQUITIES 10% 25% 42% 62% 90% 0.0% 0.0%
US 0% 5% 12% 20% 32% 44% 7.5% 15.4%
EM 0% 0% 4% 6% 9% 13% 8.5% 24.1%
Eurozone 0% 0% 4% 6% 8% 10% X 10.0% 18.4%
UK 0% 3% 3% 5% 7% 9% X 8.4% 15.0%
Japan 0% 0% 0% 3% 4% 6% X 9.2% 19.8%
Canada 0% 0% 0% 0% 0% 3% X 8.0% 15.0%
Australia 0% 0% 0% 0% 0% 3% X 8.8% 14.9%
Switzerland 0% 2% 2% 2% 2% 2% X 9.1% 14.9%
HEDGE FUNDS 16% 20% 20% 16% 0.0% 0.0%
Hedge Funds 0.0% 16% 20% 20% 16% 0% 5.2% 5.9%
0%
TOTAL 100% 100% 100% 100% 100% 100%
Expected 5 yrs Return p.a. 2.5% 3.6% 4.7% 5.6% 6.6% 7.6%
Expected Volatility p.a. 3.4% 4.1% 6.0% 8.1% 10.7% 13.5%
Sharpe ratio 0.13 0.38 0.43 0.44 0.43 0.41

Systematic Allocation
Portfolio (SAP)
SAP: Systematically driven equity exposure
a b

Systematic Allocation Portfolio: What is the underlying concept and
how does it ﬁt into CIO’s existing portfolios?
The Systematic Allocation Portfolio relies exclusively on the proprietary UBS CIO
World Equity Market Model to define its underlying equity allocation, which
chieﬂy determines market risk exposure. This model is an integral part of our
Global Tactical Asset Allocation investment process, in which we combine quan-
titative and qualitative inputs to derive our six-month investment views. It uses
macroeconomic variables and momentum as inputs, combined with proprietary
filtering techniques2
. We have used this model in a live environment since mid-
2011, and it leverages the 15+ years of investment experience of its developers.
In the context of the Systematic Allocation Portfolio, we design three SAAs with
different risk profiles – Defensive, Medium and Dynamic, following the same
principles used in defining the CIO SAAs.3
We then define the tactical equity
allocation according to the signal from the CIO World Equity Market Model.
1
Mark H. Haefele, Mads N. S.
Pedersen, and Katarina Cohrs,
Global Tactical Asset Allocation
(TAA) Methodology, UBS CIO WM
Global Investment Office (2015)
2
Matthias W. Uhl, Mads N. S.
Pedersen, and Oliver Malitius,
What’s in the News? Using News
Sentiment Momentum for Tactical
Asset Allocation, The Journal of
Portfolio Management, Vol. 41,
No. 2: pp. 100–112 (2015)
3
Mads N. S. Pedersen, and
Christophe de Montrichard,
Strategic Asset Allocation (SAA)
Methodology, UBS CIO WM
Global Investment Office (2014)
Portfolio (SAP)
SAP: Systematically driven equity exposure
a b
The Systematic Allocation Portfolio uses a quantitative macroeconomic and financial frame-
work to determine the portfolio risk level. It translates the signal of the CIO World Equity
Market Model1
to make large asset allocation changes, with equity allocation moves ranging
from 10% to 40%. The CIO World Equity Market Model is designed to capture market and
business cycle trends. It applies the principles of momentum and frequency analysis to mar-
ket-price data and key financial and macroeconomic variables in a unique, proprietary way.
The portfolio’s risk exposure changes significantly over time, enabling clients to participate
fully in strongly up-trending equity markets and to lessen their exposure to equity risk
in strongly down-trending and volatile equity markets. The Systematic Allocation Portfolio
complements the range of existing CIO Strategic Asset Allocations (SAAs). It is distinguished
by how it adheres to the CIO World Equity Market Model’s assessment of the financial
market risk environment and allocates a larger part of the risk budget and risk management
to the Tactical Asset Allocation (TAA).

Systematic Allocation Portfolio (SAP)
Depending on the CIO World Equity Market Model signal, we increase or
decrease the Systematic Allocation Portfolio allocation to equities. This increase/
decrease is matched by the corresponding decrease/increase in the high grade
bond allocation. In other words, if the model indicates rising markets ahead, we
buy equities and sell high grade bonds, and vice versa. The main principle behind
the Systematic Allocation Portfolio is full participation in strongly up-trending
equity markets (high allocation) and low exposure to risk in strongly down-
trending and volatile equity markets (low allocation). Historical analysis shows
that allocating to equities according to Fig. 1 has delivered risk/return character-
istics that outperform static portfolios with no asset allocation changes. During
clear economic and equity market trends, the strategy should outperform, e.g.
during 2001 the Systematic Allocation Portfolio would have been constantly in
low allocation while during 2004 the Systematic Allocation Portfolio would have
been constantly in high allocation. However, in years such as 2012 it would have
underperformed with an higher-than-average number of signal changes (his-
torically, the signal changed 3.7 times on average per year).
Fig. 1: Equity allocation for diﬀerent Systematic Allocation Portfolios
following a three-level approach
Fig. 1, 8
10%
20%20%
40%
60%
30%
55%
80%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Defensive Medium Dynamic
Low Medium High
+20%
+30%
+40%
+10%
+15%
+20%
Equity Exposure
Source UBS CIO, for illustrative purposes only
Fig. 2: Historical risk/return of Systematic Allocation Portfolios
compared with CIO SAAs
CIO SAA Fixed
Income
CIO SAA Income
CIO SAA Yield
CIO SAA Balanced
CIO SAA Growth
CIO SAA Equity
Defensive
Medium
Dynamic
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
10.0%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0%
Return(p.a.)
Volatility (p.a.)
Period: May 2003 to January 2016. USD reference currency portfolios.

Please always read in conjunction with the glossary and the risk information at the end of the document. 25 3
Table 1: Historical risk/return of Systematic Allocation Portfolios
compared with CIO SAAs
Portfolios Return p.a. Volatility Max Draw Down
S.A.P. Dynamic 9.4% 8.7% –17%
S.A.P. Medium 8.0% 6.0% –12%
S.A.P. Defensive 6.4% 3.9% –10%
CIO SAA Equity 7.7% 12.8% –45%
CIO SAA Growth 6.8% 10.1% –40%
CIO SAA Balanced 6.1% 7.7% –30%
CIO SAA Yield 5.5% 5.7% –20%
CIO SAA Income 4.9% 4.1% –10%
CIO SAA Fixed Income 4.3% 3.5% –5%
Period: May 2003 to January 2016: USD reference currency portfolios
S.A.P.: Systematic Allocation Portfolio
Fig. 2 and Table 1 compare the performances of the three Systematic Allocation
Portfolios with the more static portfolios comprised of the six CIO SAA risk
profiles. The Systematic Allocation Portfolios superior risk/return characteristics
are to a large extent explained by the fact that markets exhibit strong trends
and adhere to reﬂexive feedback loops4
, which are captured, at least partly, by
the CIO World Equity Market Model.
Our technology, designed to capture trends, enables us to significantly limit
drawdowns, i.e. peak-to-trough declines. Below we illustrate this feature of the
model exemplified by the Systematic Allocation Portfolio Medium:
Fig. 3: Reduced drawdown with dynamic equity allocation,
Systematic Allocation Portfolio Medium
-30%
-25%
-20%
-15%
-10%
-5%
0%
12/89 12/92 12/95 12/98 12/01 12/04 12/07 12/10 12/13
Static Allocation Systematic Allocation
Fig. 3
Draw Down
Weekly Data: 29.12.1989 – 26.02.2016
4
George Soros, Financial Markets,
The Soros Lectures (2010)

Fig. 3 compares the drawdown of a static allocation (Systematic Allocation Port-
folio Medium, medium allocation) with a systematic and dynamic equity allocation
for this portfolio. The static allocation has historically suffered sporadic draw-
downs that can approach 30%, while the systematic allocation shows evenly dis-
tributed drawdowns and limits a portfolio’s drawdown risk. As a result, the Sys-
tematic Allocation Portfolio Medium exhibits historical outperformance and
improved risk/return characteristics, e.g. a higher Sharpe ratio, as depicted by Fig.
4. We also considered the standard 60/40 portfolio (60% world government
bonds and 40% world equities) in our historical analysis to highlight the benefit
of specifying the static allocation following the principles used in defining the
CIO SAAs. Over the long period considered, the Systematic Allocation Portfolio
Medium clearly outperformed, after trading costs, by switching the equity alloca-
tion 3.7 times per year on average.
However, the model is not as effective or accurate when equity markets exhibit
weak trends, be they positive or negative. We think of this trade-off between
lack of trend and effectiveness as a temporary “insurance” cost the investor
must bear: the additional performance generated by clear up-trending and
down-trending equity markets comes at the cost of underperformance during
weakly trending markets, such as occurred in 2012. By construction, the Sys-
tematic Allocation Portfolio is clearly exposed to two types of model risk:
1) Equity market dynamics not captured accurately – e.g. a situation in which
some model inputs stop describing market behavior.
2) Trendless equity markets – e.g. frequent up/down movements in stock prices
with no clear direction.
In both cases we should expect the Systematic Allocation Portfolio to underper-
form the static asset allocation. We mitigate this situation by designing our
model on the basis of extensive historical analysis that spans decades, and on
the dynamics of equity markets and the macroeconomic variables that inﬂuence
them. We believe equity markets do exhibit trends, as history clearly demon-
strates, and that these trends will persist in the future.
Fig. 4: Performance simulation for Medium Systematic
Allocation Portfolio USD
Fig. 4
Weekly Data: 29.12.1989 – 26.02.2016
Total Return
Static S.A.P.
Return p.a.
Volatility
Max Draw down
Alpha p.a.
Switches p.a.
474%
6.9%
6.9%
-30.3%
821%
8.8%
5.8%
-11.6%
1.9%
3.7
Return / Vol. 1.510.99
60/40
389%
6.2%
6.4%
-23.0%
0.97
Systematic Allocation Portfolio (S.A.P.): Signal strongly positive (>=25%): 55% equities;
Signal negative (<0%): 10% equities; Signal weak positive (0%-25%): 40% equities
Costs: 0.3% per 100% Turnover, weekly rebalancing
Index Equity Exposure
0%
20%
40%
60%
80%
100%
100
200
300
400
500
600
700
800
900
1'000
12/89 12/93 12/97 12/01 12/05 12/09 12/13
Equity Exposure
Static Allocation

Systematic Allocation Portfolio: What does the CIO World Equity Market
Model capture?
Financial market participants often quote the colloquial saying “the trend is
your friend.” Some regard it as less than serious, but there is indeed truth to it.
The reasons are simple: human behavior and the laws of economics. Humans
herd by nature, which in a financial market context means that investors tend
to buy stocks that have recently gone up in price and sell stocks that have
recently gone down. Simply put, investors follow trends – also called momen-
tum. Momentum applies to economic activity as well: manufacturing usually
changes steadily over time, earnings increase or decrease steadily, and employ-
ment rises gradually after recessions and step-wise in a recovery. During a typi-
cal business cycle, the momentum of several key variables is self-reinforcing. If,
for instance, financial conditions improve, corporate bond spreads decline,
which makes it cheaper to finance company operations and M&A. Company
earnings rise, optimism returns, growth gets paid into equities and asset prices
tend to go up. This momentum in financial markets usually leads to a stabiliza-
tion in the real economy.
The CIO World Equity Market Model is designed to capture market and business
cycle trends by applying the principles of momentum and frequency analysis to
market-price data and key financial and macroeconomic variables in a unique,
proprietary way. The model consists of three primary components: a business
cycle component mostly based on US macro data and global corporate earnings;
an equity market momentum component that combines momentum signals
from a set of industrialized countries (represented in the MSCI World); and a risk
component that includes three different market-risk measures. These compo-
nents are ultimately aggregated with weightings of 60% and 40% respectively
(with the risk signal included in the momentum component) to generate a signal
bounded between –100% and +100% (see Appendix for more details).
The basic principle behind the model, shown in Fig. 5, is that it signals an
increase in equity allocation when equity markets are trending up and the busi-
ness cycle is improving. Conversely, it calls for a lower exposure to equities
when equity markets are trending down and the business cycle is worsening.
Fig. 5: Equity allocation managed by the signal of the CIO World Equity
Market Model
Fig. 5
-75%
-50%
-25%
0%
25%
50%
75%
1'200
1'700
2'200
2'700
3'200
3'700
12/05 12/07 12/09 12/11 12/13 12/15
Signal World Index
World Market
Low Risk, positive
Momentum
High Risk, negative
Momentum
Signal
3-level
allocation
Equity
High
Medium
Low

Systematic Allocation Portfolio: How does it behave in diﬀerent
market environments?
By design, the CIO World Equity Market Model will clearly indicate periods of
positive or negative performance in equity markets, provided there is a well-
defined underlying trend. The model will not be as effective in periods with
weak trends, whether they are positive or negative. In other words, in equity
markets that feature only slightly positive/negative performance and frequent
market up/down movements, the model will be less accurate. We think of this
trade-off between lack of trend and effectiveness as a temporary “insurance”
cost the investor must bear: the additional performance generated by clear up-
trending and down-trending equity markets comes at the cost of underperfor-
mance during weakly trending markets. Strong positive signals correspond to
strong positive equity returns at low market volatility in contrast to strong nega-
tive signals corresponding to negative equity returns with high market volatility.
Weak signals give a mixed performance picture. Fig. 6 illustrates the world
equity market’s return per annum and its volatility along the y-axis. The x-axis
shows the strength of the CIO World Equity Market Indicator. The further to the
left, the more positive the signal and correspondingly higher equity returns in a
low volatility environment. The further to the right, the more negative the signal
and correspondingly lower equity returns in a high volatility environment. When
equity markets are only marginally positive/negative and the signal is rather
muted in either direction, as depicted by the red box within the graph, the Sys-
tematic Allocation Portfolio is likely to lag more static investment concepts.
Fig. 6: Relationship between CIO World Equity Market Indicator and
equity markets
5%
10%
15%
20%
25%
30%
35%
40%
-30%
-20%
-10%
0%
10%
20%
30%
80% 60% 40% 20% 0% -20% -40% -60%
Return World Equity Market Volatility
Fig. 6
positive
Return p.a. Volatility
UBS/CIO World Equity Market Indicator
negative
Based on daily data 1989 - 2016
Strong positive signal – low
volatility and high returns –
overweight equities
Negative signal – high volatility
and low or negative returns –
underweight equities

Using quantitative signals to drive
equity exposure
Constructing and developing new investment concepts at UBS
is a comprehensive team undertaking. The Systematic Alloca-
tion Portfolio is an investment concept that applies a quantita-
tive TAA to a diversified, multi-asset class portfolio. The strategy
is based on a systematic assessment of the risk environment in
financial markets and allocates a larger part of the risk budget
and risk management to the TAA.
Systematic Allocation Portfolio: Systematically driven equity exposure
At the core of the Systematic Allocation Portfolio is a pre-specified adjustment
that determines the equity allocation. Specifically, we define three levels of
equity allocation: low, medium and high. Stocks are bought/sold against high
grade bonds, which means the high-grade bond allocation will be lowest in the
high level and highest in the low. The CIO World Equity Market Model signal is
used to determine the respective level, as follows:
• Signal negative (< 0%): Low equity allocation
• Signal positive (0% <= signal < 25%): Medium equity allocation
• Signal strongly positive (>= 25%): High equity allocation
Fig. 7: Applying a three-level approach to the CIO World Equity Market
Indicator
-75%
-50%
-25%
0%
25%
50%
75%
0
50
100
150
200
250
300
350
400
450
500
1989 1993 1997 2001 2005 2009 2013
Positive Signals Negative Signals World Equities
Fig. 7
MSCI World Index UBS/CIO World Equity Market Indicator
Medium
Allo-
cation
High
Equity
Allo-
cation
Low
Equity
Allo-
cation
0%
25%
Weekly Data: 29.12.1989 – 26.02.2016

Systematic Allocation Portfolio: Asset allocation
We construct three different types of portfolios: Defensive, Medium and
Dynamic. Each corresponds to a different client risk profile and all three port-
folios have three different equity allocation possibilities, as detailed in the
diagram below:
We chose a three-level approach based on how the model behaved in different
market environments. Our analysis suggests that positive but mediocre signals
have a mixed relationship with positive equity market performance. Therefore,
we prefer a smaller allocation (medium) if the signal is lower than 25% and only
overweight equities fully if the signal exceeds 25% (high). Additionally, we set
the overweight/underweight asymmetrically, with a greater underweight than
overweight. We chose these asymmetric levels to benefit explicitly from the
drawdown-reduction capability of the model, which we describe in more detail
in a later section.
The three allocations are multi-asset class and include high grade bonds, corpo-
rate and emerging market bonds, equities and hedge funds. Fig. 9 illustrates in
detail the three-level asset allocation for each Systematic Allocation Portfolio:
Fig. 8: Equity allocation for diﬀerent Systematic Allocation Portfolios
depending on three-level approachFig. 1, 8
10%
20%20%
40%
60%
30%
55%
80%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Low Medium High
+20%
+30%
+40%
+10%
+15%
+20%
Equity Exposure

The allocation to equity markets, high grade, corporate and emerging market
bonds derives from the corresponding CIO SAA profiles and are revised yearly
within the CIO SAA process. Fig. 9 details the two sub-asset classes whose
allocation depends on whether the model signals low, medium or high equity
allocation: high grade bonds and world equities. We also show how the port-
folios in the low allocation remain partly invested (except for Defensive) in the
domestic equity market only. The investment in corporate and emerging market
bonds remains stable, as does the hedge fund allocation. In addition to USD, we
also defined Systematic Allocation Portfolios in EUR, CHF and GBP, adjusting
the bond and equity allocations accordingly. All asset classes, with the exception
of EM equities, are hedged to the reference currency of the portfolio in the low,
medium and high allocation.
Systematic Allocation Portfolio: Risk and return analysis
To analyze the risk/return characteristics of the Systematic Allocation Portfolio,
we simulated its historical performance, including the purely quantitative TAA
as determined by the historical CIO World Equity Market Model signal. We also
compared it with a reference portfolio (static allocation), which we define as the
asset allocation corresponding to the medium equity allocation. The results in
the plot below for the Systematic Allocation Medium portfolio clearly demon-
strate both the additional performance contribution and, more importantly, the
significant volatility and drawdown reduction brought by the quantitative TAA.
We obtained similar results when performing the same historical risk/return
analysis for both Systematic Allocation Portfolios Defensive and Dynamic, as
well as for the different reference currencies.
Fig. 9: Detailed asset allocation per Systematic Allocation Portfolio USD using
three-level approach
Systematic Allocatin Portfolio USD
Equity Allocation Low Medium High Low Medium High Low Medium High
LIQUIDITY 2% 2% 2% 2% 2% 2% 2% 2% 2%
BONDS 88% 68% 58% 78% 48% 33% 68% 28% 8%
USD high grade bonds 1-5 years 18% 8% 3% 20% 8% 20% 10%
USD high grade bonds 5-10 years 38% 28% 23% 32% 14% 7% 40% 10%
USD corporate intermediate bond (IG) 22% 22% 22% 16% 16% 16%
USD high yield bonds 3% 3% 3% 3% 3% 3% 3% 3% 3%
EUR high yield bonds 2% 2% 2% 2% 2% 2% 2% 2% 2% X
EM sovereign bonds (USD) 3% 3% 3% 3% 3% 3% 3% 3% 3%
EM corporate bonds (USD) 2% 2% 2% 2% 2% 2%
EQUITIES 20% 30% 10% 40% 55% 20% 60% 80%
Equities AC World 10% 15% 20% X
Equities USA 10% 10% 10% 18% 18% 20% 30% 30%
Equities Emerging Markets 6% 6% 9% 9%
Equities Eurozone 4% 4% 6% 6% 8% 8% X
Equities United Kingdom 4% 4% 5% 5% 7% 7% X
Equities Japan 3% 3% 4% 4% X
Equities Switzerland 2% 2% 2% 2% 2% 2% X
HEDGE FUNDS 10% 10% 10% 10% 10% 10% 10% 10% 10% X
TOTAL 100% 100% 100% 100% 100% 100% 100% 100% 100%
EQUITY SHIFTS -20% 10% -30% 15% -40% 20%
Defensive Medium Dynamic FX
Hedged

This analysis highlights, in particular, the drawdown-reduction capabilities of
the Systematic Allocation Portfolio and its suitability for clients who wish to be
underinvested in strongly down-trending equity markets, such as occurred dur-
ing the 2002–03 and 2008–09 recessions.
Fig. 11 displays in detail a similar ability to reduce drawdowns for all the three
risk profiles. Obviously, the CIO World Equity Market Model determines the
equity allocation for all risk profiles simultaneously, so the number of switches
and the periods each portfolio remains in low, medium and high allocation is
the same. On average, the strategy changed the asset allocation 3.7 times per
year. In a given year, however, the number of changes depends on the state of
the business cycle and the momentum of the equity market. During clear eco-
nomic and equity market trends, the strategy can hold a position for a long
time, e.g. the Systematic Allocation Portfolio was in low allocation during 2001
and fully invested (high allocation) during 2004. In other years, such as 2012
during the euro crisis, it may change more often.
Fig. 10: Performance simulation for Medium Systematic Allocation
Portfolio USD
Fig. 10
Index Equity Exposure
Total Return
Static S.A.P.
Return p.a.
Volatility
Max Draw down
Alpha p.a.
Switches p.a.
474%
6.9%
6.9%
-30.3%
821%
8.8%
5.8%
-11.6%
1.9%
3.7
Return / Vol. 1.510.99
Weekly Data: 29.12.1989 – 26.02.2016
60/40
389%
6.2%
6.4%
-23.0%
0.97
Systematic Allocation Portfolio (S.A.P.): Signal strongly positive (>=25%): 55% equities;
Signal negative (<0%): 10% equities; Signal weak positive (0%-25%): 40% equities
Costs: 0.3% per 100% Turnover, weekly rebalancing
0%
20%
40%
60%
80%
100%
100
200
300
400
500
600
700
800
900
1'000
12/89 12/93 12/97 12/01 12/05 12/09 12/13
Equity Exposure
Static Allocation

Fig. 11: Detailed simulation results for Systematic Allocation Portfolios
in USD
Year Static S.A.P. Static S.A.P. Static S.A.P. Switches
1990 3.4% 5.5% -1.3% 4.1% -4.4% 2.9% 4
1991 18.3% 15.3% 20.3% 15.2% 21.4% 14.5% 2
1992 9.9% 8.6% 9.3% 6.5% 9.0% 4.9% 7
1993 15.1% 16.3% 18.4% 20.4% 20.1% 22.7% 1
1994 -4.2% -4.6% -3.7% -4.3% -2.8% -3.6% 7
1995 20.3% 20.6% 19.3% 19.9% 19.5% 20.2% 6
1996 10.5% 11.2% 12.8% 13.9% 15.6% 17.0% 3
1997 13.7% 12.9% 13.6% 12.7% 15.3% 14.3% 5
1998 9.3% 10.5% 9.2% 12.3% 11.9% 16.2% 4
1999 8.4% 9.7% 16.3% 18.4% 22.6% 25.6% 5
2000 5.4% 6.6% -0.7% 1.6% -5.5% -2.2% 2
2001 2.9% 7.8% -1.1% 5.6% -5.2% 3.2% -
2002 1.3% 5.9% -5.5% 0.5% -12.3% -4.6% 6
2003 10.9% 11.3% 15.9% 17.4% 19.8% 21.9% 4
2004 6.5% 7.5% 9.0% 10.6% 10.7% 12.9% -
2005 5.3% 6.5% 9.2% 11.1% 11.8% 14.3% 4
2006 8.1% 8.9% 11.2% 12.2% 14.0% 15.2% 4
2007 6.3% 5.9% 7.6% 6.8% 8.8% 7.7% 4
2008 -12.7% -1.6% -23.2% -6.4% -30.7% -9.3% 2
2009 17.1% 17.2% 23.4% 21.3% 26.9% 23.6% 2
2010 8.4% 8.6% 9.2% 9.6% 9.8% 10.2% 4
2011 3.0% 1.7% -0.7% -0.8% -3.2% -3.1% 2
2012 9.1% 9.0% 10.9% 10.6% 11.7% 11.3% 6
2013 5.0% 6.0% 9.8% 11.3% 15.4% 17.6% 6
2014 6.0% 5.0% 6.3% 5.1% 6.8% 5.3% 5
2015 1.1% 1.9% 0.2% 2.6% -0.1% 3.3% 3
2016 -0.6% 0.9% -2.6% 0.2% -4.1% -0.4% -
Total Return 481% 666% 474% 821% 475% 992%
mean Return p.a. 6.9% 8.1% 6.9% 8.8% 6.9% 9.5%
Volatility p.a. 4.4% 4.1% 6.9% 5.8% 9.8% 8.1%
Sharpe Ratio 1.57 1.96 0.99 1.51 0.70 1.18
Max Draw Down -17.4% -9.2% -30.3% -11.6% -40.1% -16.7%
Switches 3.7 3.7 3.7
% High 48% 48% 48%
% Medium 25% 25% 25%
% Low 27% 27% 27%
Turnover p.a. 53% 80% 107%
Simulation until 26.02.2016 including transaction costs
We also outline the Systematic Allocation Portfolio historical risk/return charac-
teristics in the context of CIO SAAs (Fig. 13). The plot shows how the volatility of
the Systematic Allocation Portfolio Defensive, Medium and Dynamic resembles
that of CIO SAA Income, CIO SAA Balanced and CIO SAA Growth, respectively,
even though the constituent asset classes differ. Specifically, the equity allocation
of the Systematic Allocation Portfolio follows a three-level rule with a wide
range, while the CIO SAA equity allocation is set at a determined level (Fig. 12).
Fig. 12: Equity allocation for Systematic Allocation Portfolio and
UBS SAA compared
Fig. 12
Equity Allocation
Systematic Allocation Portfolio versus CIO SAA
Defensive CIO SAA Income
0% - 20% - 30% 10%
Medium CIO SAA Balanced
10% - 40% - 55% 42%
Dynamic CIO SAA Growth
20% - 60% - 80% 62%

When interpreting the results of Fig. 13, we should bear in mind an important
aspect: the period considered included the global financial crisis of 2008–09.
While the Systematic Allocation Portfolios would have been underweight for
most of it due to a negative signal, we assume for the purpose of this simulation
that the CIO SAAs would have remained unchanged throughout.
As mentioned earlier, the CIO World Equity Market Model can be used to man-
age portfolio drawdowns, since it is designed to give a negative signal in strongly
down-trending equity markets. We simulated the historical drawdown of the
Systematic Allocation Portfolio Medium and compared it with the reference
portfolio (static allocation), as defined earlier. While the Systematic Allocation
Portfolio does not always manage drawdowns better than the reference port-
folio, it clearly outperforms it in periods of recession, such as 2002–03 and
2008–09. Remarkably, the drawdowns of the medium Systematic Allocation
Portfolio profile stay in a range of 5% to 12% through different crises, events
and recessions. This supports our view that the CIO World Equity Market Indica-
tor may be able to capture the next crises in a similar manner.
Fig. 13: Risk/return of Systematic Allocation Portfolios compared
with UBS SAA
CIO SAA Fixed
Income
CIO SAA Income
CIO SAA Yield
CIO SAA Balanced
CIO SAA Growth
CIO SAA Equity
Defensive
Medium
Dynamic
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
10.0%
0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0%
Return(p.a.)
Volatility (p.a.)
Period: May 2003 to January 2016. USD reference currency portfolios.

Fig. 15: Capital allocation and risk contribution for
Medium Portfolio USD
Fig.15
24%
1%
26%
11%
40%
87%
10%
1%
0%
20%
40%
60%
80%
100%
Capital Allocation Risk Contribution
Govern. Bonds Credit Equities Hedge Funds
•  Hedge Funds are
diversifying
•  Equities are dominating
portfolio risk
•  Credit contribute less risk
than capital allocation
•  Gov. Bonds are
diversifying
•  Main risk of the
portfolio is managed
by TAA Model!
Weekly Data: 29.12.1989 – 26.02.2016
Systematic Allocation Portfolio: Managing portfolio risk via equity
allocation
The Systematic Allocation Portfolio manages its equity allocation, the main risk
contributor, by adhering to the market and business cycle assessment of the CIO
World Equity Market Model. It simultaneously manages the portfolio risk level.
Fig. 15 depicts a simplified asset allocation of the Medium profile, comparing
the capital allocation of the individual asset classes in one bar to their risk con-
tribution in a second bar. On average, the static Medium profile with medium
equity allocation displayed an annualized volatility of 6.9%. With 40% of the
capital invested in equities, 26% in credit, 24% in government bonds and 10%
in hedge funds, the portfolio is well diversified.
-30%
-25%
-20%
-15%
-10%
-5%
0%
12/89 12/92 12/95 12/98 12/01 12/04 12/07 12/10 12/13
Static Allocation Systematic Allocation
Fig. 14
Draw Down
Weekly Data: 29.12.1989 – 26.02.2016
Collapse of dot-
com bubble
Financial crisis
EURO crisis
Asian crisis
Recession on
Kuwait crisis
China slow
down
Bond market crisis
Fig. 14: Drawdown management of Systematic Allocation Portfolio
Medium Portfolio USD

Fig. 16: Mapping UBS risk proﬁles to Systematic Allocation Portfolios
Fig. 16
Fixed Income
Income
Yield
Balanced
Growth
Equity
UBS CIO SAA Systematic
Allocation
Portfolio
Dynamic
Medium
Defensive
Analysis of the contribution of the different asset classes to the overall portfolio
volatility, however, reveals that capital allocation and the risk contribution of the
respective asset classes diverge greatly. The equity allocation clearly dominates
the portfolio risk. Hedge funds, credit and government bonds contribute only
modestly to it. By actively managing its equity risk, the Systematic Allocation
Portfolio addresses almost 90% of its overall risk.
How to map Systematic Allocation Portfolios to UBS client risk proﬁles?
UBS client risk profiles are determined by the ability and willingness of our
clients to take risk. Risk is measured in volatility and potential (maximum)
drawdown of a certain portfolio. UBS has defined six different risk profiles with
corresponding SAA portfolios from Equity to Fixed Income with declining vola-
tility and drawdown risk (see Fig.2 and Table 1). While we expect the CIO World
Equity Market Model to reduce the drawdown risk and volatility of the Systematic
Allocation Portfolios, the potential risk of the large equity exposure during
“High” allocation mode has to be taken into account as well. We consider the
risk profile of the Systematic Allocation Portfolios to be comparable to (Fig. 16):
Systematic Allocation Portfolio Dynamic has a similar risk profile to CIO SAA
Growth. The equity allocation is similar, and we expect the Systematic Alloca-
tion Portfolio Dynamic to generate less volatility and suffer fewer drawdowns
due to its lower exposure to equities during crises and recessions.
Systematic Allocation Portfolio Medium has a similar risk profile to CIO SAA
Balanced. The equity allocation of both portfolios is similar. Again, the potential
swings in equity allocation for Systematic Allocation Portfolio Medium is larger
than for the Balanced risk profile, but the portfolio risk should be reduced by
the systematic equity allocation based on the CIO World Equity Market Model
signal.
Systematic Allocation Portfolio Defensive has a similar risk profile to CIO SAA
Yield. Clients with a lower risk profile should not invest in Systematic Allocation
portfolios as the most conservative (Defensive) portfolio may invest up to 30%
in equity markets.

Fig. 17: Structure of the CIO World Equity Market ModelFig. 17
Sub-ComponentsWeights
Assessment of
momentum across
World equity
markets
Momentum in Equity Markets
40%
Short/Medium Term Trend
Long Term Trend
Market Risk Indicators
daily
daily
daily
Momentum in Business Cycle
60%
Trend in Global Earnings
Business Activity Indicators
Business Risk Indicators
monthly
weekly
weekly
Assessment of
earnings dynamics
and business cycle
activity driving
equity markets
Overall indication for
equity market investment:
•  Be fully invested at
strong positive signals
•  Be partly invested at
weaker signals
•  Underweight equities on
negative signals
Current earnings situation
Indication for future earnings
Investors expectations
Short term volatility
Bull-/bear market
Current trend
Appendix: CIO World Equity Market
Model
Deﬁning our approach
The CIO World Equity Market Model processes market and economic data to
generate a signal that recommends ascribing an overweight, neutral or under-
weight position to equity investments within a portfolio. The model consists of
three primary components: a business cycle component mostly based on US
macro data and global corporate earnings; a momentum component that com-
bines momentum signals from a set of industrialized countries (represented in
the MSCI World); and a risk component that includes three different market-risk
measures. These components are ultimately aggregated with weightings of
60% and 40% respectively (with the risk signal included in the momentum
component) to generate a signal bounded between –100% and +100%. The
model is calibrated to generate approximately three to five signal changes per
year, which is roughly in line with the stated objective of postulating a tactical
position for the next six months.
Combining trends and business cycle indicators
We start with a set of statistically tested quantitative indicators to get an in-
depth understanding of current equity market movements and the dynamics of
the current business cycle. The design of the CIO World Equity Market Model is
based on two simple principles: 1) equity investors co-own the companies they
invest in, so the value of the equity investment is directly related to the current
and future income stream of the company; 2) beyond business and earnings
dynamics, equity markets are often affected by crises, political change, central
bank interventions and many other unpredictable events best captured quanti-
tatively in the equity market price momentum.
Thus, we have constructed the CIO World Equity Market Model using two
modules: Momentum in Business Cycle and Momentum in World Equity Markets
(Fig. 17).

Fig. 18: Business Cycle signals and world equity market
Fig. 18
MSCI World Index Business Cycle Signal
Environmentokforequity
investments
Environmentdifficultfor
equityinvestments
Weekly Data: 29.12.1989 – 26.02.2016
-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
0
50
100
150
200
250
300
350
400
450
500
1989 1993 1997 2001 2005 2009 2013
The Business Cycle module assesses the business cycle and especially earnings
dynamics, which are crucial to equity market performance. The methodology
derives from the momentum model described below. The module has three
main sections with almost equal weightings. Within “Trend in Global Earnings”
we assess the current dynamics in reported (trailing) earnings for all developed
country equity markets. The section “Business Activity Indicators” summarizes
the signals from a set of leading indicators that have predictive power for earn-
ings growth (e.g. Purchasing Manager indices, retail sales, etc.). The last section,
“Business Risk Indicators,” captures the perception of market participants on
risk and economic developments. We calculate the three components on weekly
and monthly data according to its availability and combine them to generate
the Business Cycle signal. It combines current earnings trends and business
activity with investor expectations about the business cycle (Fig. 18).
A positive signal indicates robust earnings growth, a positive trend for leading
macro indicators and a declining risk premium demanded by market partici-
pants – a favorable environment for equity investments. A strong negative sig-
nal points to a recession, falling earnings and higher risk premiums demanded
by investors – an environment in which high grade bonds are preferred and
equity markets exhibit high volatility and, most likely, negative returns. In the
scenario where Business Cycle signals are weak, the CIO World Equity Market
Model signal is likely to be driven by the current momentum of the equity
market, which is captured by the momentum sub-model.
The Momentum in World Equity Markets module (Fig. 17) provides us with a
deep and comprehensive description of current market movements. The meth-
odology behind the calculations of market momentum derives from electronic
engineering and frequency analysis. Applying this technology to describe the
dynamics of the equity market is a unique, proprietary approach developed in
house5
. This module has three main sections as well. Short and medium-term
filters separate daily noise from the underlying market movements of recent
5
Matthias W. Uhl, Mads N. S.
Pedersen, and Oliver Malitius,
What’s in the News? Using News
Sentiment Momentum for Tactical
Asset Allocation, The Journal of
Portfolio Management, Vol. 41,
No. 2: pp. 100–112 (2015)

Fig. 19: Illustration of frequency ﬁltering on equity market index
Fig. 20
Germany Index Signal per Frequency
Signals
transformed … and added up
Example Index: German Equity, 31.8.2010 – 21.9.2011
Example Frequencies: 10-20, 30-60, 50-100 days
1'500
1'600
1'700
1'800
1'900
2'000
2'100
2'200
08/10 10/10 12/10 02/11 04/11 06/11 08/11
days and weeks. The signal from this section is more trading oriented, giving
buy and sell recommendations about five to 10 times per year on average. The
long-term filter works more as a regime indicator and signals periods of bull and
bear markets: buy and sell recommendations change one to three times annu-
ally on average. Fig. 19 illustrates the effect of applying filtering technology to
a specific equity market.
Each equity market in the MSCI World is analyzed with the same set of fre-
quency filters and lengths of frequencies. We combine the signals from differ-
ent frequencies and filters, as illustrated in Fig. 19, and derive an overall trend
indicator for each equity market by normalizing the different signals using a roll-
ing window of one to two years of past data. The normalized signals now show
the same distribution and values between +3 and –3 and are easy to combine
to produce the overall trend indicator for a specific equity market. The use of a
rolling window for the normalization has an additional interesting effect: the
momentum model “learns.” The rolling normalization helps to adapt to chang-
ing volatility regimes in the equity market. With the additional treatment of out-
liers, we finally get a pure trend signal for each market that ranges between
+100% and –100%. We consider momentum in our model as a combination of
market trend and market risk. With the help of the third section, we combine
these trend filter signals with market risk indicators to derive the final momen-
tum signal for each market. The short-term market risk indicators react to sud-
den market crises and complement the longer-term trend filters.
The momentum signal for the world equity market is calculated as an aggrega-
tion of each single equity-market momentum signal within the MSCI World
Index (Fig. 20). A strong positive signal indicates a strong trend and low risk in
most equity markets within MSCI World: it’s a time we want to be invested in
equity markets. A strong negative signal points to a negative trend and/or
extreme volatility in most equity markets – a period we try to avoid and invest
in high grade bonds instead of equities.

Fig. 20: Momentum signals and world equity market
Fig. 20
-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
0
50
100
150
200
250
300
350
400
450
500
1989 1993 1997 2001 2005 2009 2013
MSCI World Index Momentum Signal
Momentumpositive
Marketrising
Momentumnegative
Marketvolatile
Weekly Data: 29.12.1979 – 26.02.2016
CIO World Equity Market Model Signal
After generating the Business Cycle and World Equity Market Momentum
signal, we combine them, applying the respective weightings, to obtain the
CIO World Equity Market Indicator:
Fig. 21: Total model signals and world equity market
Fig. 21
-75%
-50%
-25%
0%
25%
50%
75%
0
50
100
150
200
250
300
350
400
450
500
1989 1993 1997 2001 2005 2009 2013
MSCI World Index UBS/CIO World Equity Market Indicator
Weekly Data: 29.12.1989 – 26.02.2016

Fig. 22: Analyzing world equity market performance at certain
TAA signal levels
Fig. 22
Recommendation Signal Return p.a. Volatility
Low allocation
High allocation
% Time
>25%
<0%
48%
27%
13.9%
-6.1%
10.7%
22.5%
MSCI World local
Weekly data from 1989 – 2016
Medium allocation 0% - 25% 25% 3.4% 13.6%
Strong positive signals now stem from the good support provided by the busi-
ness cycle and strong momentum in equity markets – a period the Systematic
Allocation Portfolio is fully exposed to the equity market (high allocation). Nega-
tive signals may stem from the combination of a recession and negative market
momentum – when the portfolio risk for the Systematic Allocation Portfolio is
reduced significantly (low allocation to equities). During mixed signals, the Sys-
tematic Allocation Portfolio has a medium exposure to the equity market.
We analyzed the model’s forecast ability to distinguish between different mar-
ket regimes by computing the annualized returns and volatility of the MSCI
World Index after different model signal ranges. We found, as shown in Fig. 22,
that the model distinguishes between periods of low volatility and strong posi-
tive returns, moderate volatility and moderate positive returns, and high volatil-
ity and negative returns. This analysis justifies our choice of a three-level
approach for the Systematic Allocation Portfolio.

Global Credit Opportunities
(GCO) Portfolio
GCO: Liquidity premia enhance returns
a b

GCO: What is the underlying concept and how does it ﬁt into CIO’s
existing portfolios (SAAs)?
If diversification is the only free lunch in the investment world, it makes sense to
consider every potential source of return when constructing a portfolio. We act
on this observation in developing our CIO SAAs2
, which we construct from a
broad range of asset classes. We emphasize more-liquid asset classes that we
believe suit the vast majority of our clients. However, less-liquid asset classes can
provide an additional, diversifying source of return – i.e. the liquidity premium –
to investors. Liquidity premia are particularly found in the more niche areas of the
credit markets, of which high yield and emerging market bonds are well-known
examples. These bond investments by nature generate income. So it is possible to
construct a well-balanced, high income-producing portfolio that benefits directly
from liquidity premia. It should be noted there is a trade-off between high income
generation and liquidity of the underlying investments, but not between income
and duration (interest rate exposure). Specifically, the GCO qualifies as a low-
duration investment, positioned against uncertain interest rate hiking cycles and
the volatile fixed income market regime we are currently experiencing.
GCO: What is the investment universe?
The GCO focuses on fixed income, though it can also invest in hedge funds. The
portfolio takes advantage of the full credit spectrum across the capital structure,
from investment grade credit to private debt, in various regions worldwide. The
allocation to hedge funds plays primarily a diversification role; and it is not
expected to have a strong income generation component. By design, the differ-
ent credit sub-asset classes contribute in similar proportions to the portfolio’s
overall risk. Additionally, the GCO invests in credit whose liquidity profile makes
it generally compatible with monthly or quarterly liquidity requirements.
The Global Credit Opportunities (GCO) offers exposure to traditionally illiquid asset classes.
It constitutes an alternative Strategic Asset Allocation (SAA) that complements the range of
CIO SAAs. It is designed for clients who invest for the long term and seek to capture high
recurring income and the additional sources of return present in less-liquid investments1
.
1
J. Dick-Nielsen,
P. Feldhütter, and
D. Lando., Corporate
bond liquidity before
and after the onset of
the subprime crisis.
Journal of Financial
Economics, 2011;
Longstaff, Francis A.,
Portfolio Claustropho-
bia: Asset Pricing in
Markets with Illiquid
Assets, American
Economic Review, 2009
2
Mads N. S. Pedersen,
and Christophe de
Montrichard, Strategic
Asset Allocation (SAA)
Methodology,
UBS CIO WM Global
Investment Office
(GCO) Portfolio
GCO: Liquidity premia enhance returns
a b

Global Credit Opportunities (GCO)
To illustrate the interplay between liquidity premia, expected return and credit
quality in the different credit sub-asset classes, we have drawn a representative
risk/return scatter plot in fig. 1. It represents the additional return benefit of
capitalizing on liquidity premia:
GCO: How can clients incorporate this portfolio in their overall asset
allocation?
GCO is designed for clients who want to generate recurring income from their
investments, do not require constant and immediate access to the capital they
invest, and favor preserving capital over the medium term. As such, it can be
viewed as:
• a stand-alone portfolio that provides a source of recurring income to help
fund the client’s lifestyle or cover other recurring expenses
• the core allocation in a core-satellite portfolio setup, with the client supple-
menting it by investing in other asset classes, e.g. equities
• a satellite in a core-satellite portfolio setup, an illiquid investment that pro-
vides recurring income
• a wealth-preservation (“stay rich”) portfolio that can be expected to preserve
invested capital’s real value
The risk and return profile of the GCO is comparable to that of the CIO SAA
Yield. In line with our CIO SAA methodology, the GCO can be paired with other
asset allocations. There is an important difference, however, between the GCO
and the CIO SAA Yield: the former focuses on credit and is ill-suited to clients
unable to accept the volatility associated with credit crises. We note however
that historical simulations show that the GCO recovered its value faster than
CIO SAA Yield during the 2008–09 credit crisis, despite its high allocation to
less-liquid credit sub-asset classes.
Clients can use the GCO allocation as part of a set-up in which they adhere to
a certain portfolio structure or plan but direct/decide on the specific invest-
ments themselves. The GCO’s relative lack of liquidity places constraints on
making investments that have no market price impact, therefore the portfolio
Source UBS CIO For illustrative purposes only.
Core
Credit

Hedge
Funds

ABS/Bank

Capital

Private
Debt

Building
blocks
Figure 1: Representative risk/return scatter plot

implementation period can range up to between nine and twelve months. It is
a multi-year investment by design, so its performance does not depend on
timing the market correctly. Given the unique characteristics and complexity of
the credit sub-asset classes that offer higher liquidity premia, specialist fund
managers are needed to populate the portfolio. UBS’s open architecture
regarding third-party investment fund providers offers a distinct advantage in
implementing the GCO’s asset allocation.
Harvesting liquidity premia in a portfolio context
Constructing and developing new investment concepts at UBS is a comprehen-
sive team undertaking. It involves our most experienced strategists and asset
class experts; and it includes our risk office professionals and quantitative port-
folio construction specialists. The GCO is an investment concept that enables
clients to benefit from existing liquidity premia available in financial markets in
a diversified and well-balanced portfolio.
GCO: Begin with the right building blocks
To achieve the dual goals of generating a high amount of income while captur-
ing the returns fueled by liquidity premia, we must select the right sub-asset
classes as building blocks. Credit, as mentioned above, features prominently.
We consider the full spectrum in terms of credit worthiness (rating), location
(developed and emerging market), complexity (subordinated and structured
credit) and liquidity. At the most liquid end of the spectrum we look at invest-
ment grade corporate bonds, and at the illiquid end we consider private debt.
The GCO investment universe covers the whole capital structure except equity
capital (see fig. 2).
Source UBS CIO For illustrative purposes only.
Figure 2: Typical capital structure of corporate entities, ﬁnancial and
non-ﬁnancial

GCO: Asset allocation
At the core of the GCO asset allocation are different credit sub-asset classes,
namely, investment grade corporate, high yield corporate, emerging market sov-
ereign and emerging market corporate bonds. We invest in these assets in differ-
ent currencies, subject to different central bank policies, as a way of gaining fur-
ther diversification. We hedge the currency risk because the volatility associated
with currency movements would worsen the GCO’s risk and return characteris-
tics. The recent past has demonstrated the effect diverging central bank policies
(e.g. those of the US Federal Reserve and the European Central Bank) can have
on the performance of credit sub-asset classes. We complement our allocation
to traditional credit by adding senior secured loans, bank capital and asset-
backed securities and private debt. Additionally, we recommend hedge funds.
This allocation primarily plays a diversification role – while we capture liquidity
premia in it, we do not assume it will generate significant income.
Table 1 provides three examples of a USD-focused GCO asset allocation. They
range from one focused on the more-liquid credit segments to one that includes
bank capital, asset-backed securities and hedge funds and private debt. It illus-
trates the risk-and-return benefit of adding less-liquid asset classes to the core
GCO asset allocation.
Table 1 also displays the estimated yield to maturity (YTM) of each GCO option,
which can be considered a proxy for the annual recurring income it generates.
For comparison, the current YTM of a comparable risk profile, in this case the
CIO SAA Yield USD, is c. 1.8%, i.e. approximately 3.2%–3.8% lower.
GCO: Risk and return analysis
1. GCO in the context of CIO SAAs
To demonstrate how the GCO fits in with our existing CIO SAAs in terms of risk
and return characteristics, we analyzed historical and forward-looking risk and
return estimates. They are shown in fig. 3 in the context of the risk and return
characteristics of CIO SAAs. The plot clearly indicates that GCO’s volatility mir-
rors that of the CIO SAA Yield, even though the constituent asset classes differ
(the CIO SAA Yield has an allocation of 25% to equities vs. 0% for the GCO).
USD
Global Credit
Opportunities
Global Credit
Opportunities
with HFs
Global Credit
Opportunities
with HFs &
ABS & PD
FX
Hedged
Expected
10 Yrs
Return
p.a.
Current
Yield to
Maturity
(YTM)
Expected
Volatility
p.a.
LIQUIDITY 5% 5% 5%
Cash USD 5% 5% 5% 2.6% 0.6% 0.5%
BONDS 95% 77% 65% 0.0% 0.0%
USD corporate intermediate bonds (IG) 15% 15% 0% 3.4% 3.0% 4.2%
EUR securitized ABS 0% 0% 5% X 4.2% 1.1% 4.3%
EUR subord. financial bonds 5% 5% 5% X 4.0% 2.5% 10.0%
USD senior loans 15% 15% 15% 6.3% 8.1% 7.4%
EUR senior loans 10% 10% 10% X 6.5% 6.0% 7.1%
US high yield short duration 5% 5% 5% 4.9% 9.1% 7.4%
USD high yield bonds 10% 10% 8% 5.5% 8.5% 8.8%
EUR high yield bonds 10% 7% 7% X 5.0% 5.5% 8.5%
EM sovereign bonds (USD) 10% 5% 5% 5.5% 6.1% 9.1%
EM corporate bonds (USD) 10% 5% 5% 5.2% 6.0% 9.9%
Asia credit (USD) 5% 0% 0% 4.4% 4.8% 7.4%
HEDGE FUNDS 18% 18% 0.0% 0.0%
Hedge Funds 0% 18% 18% 6.1% 5.0% 5.9%
PRIVATE MARKETS 12%
Private debt 12% 8.5% 10.0% 9.8%
TOTAL 100% 100% 100%
Expected 10 yrs Return p.a. 5.0% 5.2% 5.8%
YTM estimate 6.0% 5.0% 4.3%
Expected Volatility p.a. 5.8% 5.3% 5.9%
Sharpe ratio 0.42 0.50 0.55
Duration 3.8 2.8 1.7
Max. Drawdown -22% -22% -26%
Source UBS CIO, February 2016 – pending QIS update For illustrative purposes only.
Table 1: Examples of USD-focused GCO asset allocation

WM CIO Global Asset Allocation – Investing with UBS WM

WM CIO Global Asset Allocation – Investing with UBS WM

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