RMI 3567 Temple Blackrock Investment Institute and Uncertainty Case Study Use the Blackrock article “Understanding Uncertainty” for question 1 and the Blac

RMI 3567 Temple Blackrock Investment Institute and Uncertainty Case Study Use the Blackrock article “Understanding Uncertainty” for question 1 and the Blackrock interactive web site (https://www.blackrock.com/institutions/en-us/insig… for question 2. 1)Provide Blackrock’s definition of uncertainty, how it differs from risk, and the implications of ignoring uncertainty.2)On the capital markets assumptions page of Blackrock’s interactive site, select the US as the country and click on the “Show mean uncertainty box”. a.For the 5 year return time period, provide an explanation for the differences that you see both in terms of return and uncertainty across the asset classes shown at the bottom of the graph. b.Describe what happens to expected return and mean return uncertainty as the time period increases across the time period options. What is a possible explanation for these trends?3)Find a recent article that reports on the international impact of the coronavirus. Indicate the article and describe whether it discusses the impact of the coronavirus in terms of risk, uncertainty, or both. FOR INSTITUTIONAL, PROFESSIONAL, QUALIFIED INVESTORS AND QUALIFIED CLIENTS ONLY
BLACKROCK
INVESTMENT
INSTITUTE
Portfolio Perspectives
Understanding uncertainty
APRIL 2019
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FOR INSTITUTIONAL, PROFESSIONAL, QUALIFIED INVESTORS AND QUALIFIED CLIENTS ONLY
Summary
The incorporation of uncertainty is an important part of our revamped approach to portfolio
construction. By incorporating uncertainty we recognise that mean expected returns for assets
are estimated with error rather than assuming they are known, as is the case with mean variance
techniques. We consider the distribution around the mean, effectively reducing the weight placed
on our mean (central) estimate. How much uncertainty should we take into account? We
highlight some criteria used to identify a suitable amount of uncertainty in our process. They
include the back-tested predictive power of our asset class return models, the historic volatility of
assets and the desire for diverse portfolios when optimising. Uncertainty in mean returns feeds in
to our stochastic simulations, that give a range of potential return pathways from five years out to
the long term. When constructing portfolios, these simulated pathways and our mean return
uncertainty enable us to use robust optimisation techniques that generally lead to less
concentrated portfolios compared with those portfolios resulting from mean variance
optimisation. It also gives exibility to focus on certain upside or downside scenarios when
constructing portfolios to fit client needs.
False sense of security
Most people would be suspicious of any meteorologist claiming precision about the weather next week or
even just tomorrow. Meteorology has advanced over the years, yet any realistic weather forecast
acknowledges an element of uncertainty, and probability, to avoid giving a false sense of security. We find a
parallel to the process of estimating asset returns and building portfolios. We can analyse historical data,
study correlations and build models to create forward-looking views. Yet claiming the long-run mean is
known with certainty would be foolish. In this Portfolio perspectives, we take a deeper look into how we
incorporate uncertainty in our portfolio construction process by introducing mean return uncertainty — a
distribution of values for the mean expected return for each asset class.
Distinguishing between uncertainty and risk is important. We de ne uncertainty as the range of outcomes
for the mean and risk as the range of outcomes around the mean. For example, instead of saying an asset
has a mean return of 6%, we say a mean return in range of 5-7% even if the risk, or volatility, of the asset
stays the same. We believe overlooking uncertainty, combined with common mean variance optimisation
(MVO) techniques, can lead to undesirable results such as unstable or overly concentrated asset allocations
without the use of ad-hoc constraints. Our approach does two important things: First, it acknowledges that
we should not place full conviction on a speci c value for the mean expected return. Instead, we allow for
other potential return pathways where the mean expected return is different. Observing market data over the
last 20 years only gives us information on one state of the world, or one regime. We cannot base our
expected returns only on historic observation as future regimes can differ from the past, resulting in
structural changes to the mean return. Second, our uncertainty varies by asset class. Why is this important?
A lower ability to estimate returns for one asset class (for example when an asset’s returns are poorly
explained by well-known public market factors) should be re ected with a wider uncertainty range, all else
equal. For two assets of the same mean risk and return, we would hold less of the asset where we have
greater uncertainty in the mean return assuming investors are averse to uncertainty.
Authors
Contributors
Philipp Hildebrand
Jean Boivin
Laszlo Antal
Natalie Gill
Christian Olinger
Michael Palframan
Misha van Beek
Anthony Chan
Paul Henderson
Vivek Paul
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Our expected returns with uncertainty are illustrated in the Banding together chart below. The central path of
mean returns is informed by our long-run expectations of macro factors and the ve-year return estimates
derived from our asset class models. Using a Monte Carlo simulation we generate thousands of potential
return pathways centred around a distribution of mean pathways. The lighter shaded areas show an
interquartile range between the 25th and 75th percentile of these return pathways. The difference in the size
of mean return uncertainty between government bonds and EM debt in the chart below comes down to facets
of each asset class. Well-understood factors can explain much of the returns from US government bonds but
less so for EM debt. EM debt also has higher volatility than US government bonds. Together, these dampen
our conviction in the mean path of returns for EM debt relative to US government bonds.
Banding together
Mean return uncertainty and return pathways on a 5-to-25-year horizon
This information is not intended as a recommendation to invest in any particular asset class or strategy or as a promise – or even
estimate – of future performance. Source: BlackRock Investment Institute, April 2019. Data as of 28 February, 2019. Notes: Return
assumptions are total nominal returns. US dollar return expectations for all asset classes are shown in unhedged terms, with the exception
of global ex-US Treasuries, hedge funds, and global ex-US large cap equities. Our CMAs generate market, or beta, geometric return
expectations. Asset return expectations are gross of fees. For a list of indices used, visit our Capital Market Assumptions website at
blackrock.com/institutions/en-us/insights/portfolio-design/capital-market-assumptions and click on the information icon in the Asset
class return and volatility expectations table. We use BlackRock proxies for selected private markets because of lack of sufficient data. These
proxies represent the mix of risk factor exposures that we believe represents the economic sensitivity of the given asset class. There are two
sets of bands around our mean return expectation. The darker bands show our estimates of uncertainty in our mean return estimates. The
lighter bands are based on the 25th and 75th percentile of expected return outcomes – the interquartile range – of potential return pathways
(Garlappi, Wang and Uppal, 2006 ). Indices are unmanaged and used for illustrative purposes only. They are not intended to be indicative of
any fund or strategy’s performance. It is not possible to invest directly in an index.
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Should uncertainty converge over time?
An assumption still commonly held in traditional portfolio construction techniques is that simulated asset
returns (on an annualised cumulative basis) converge to a single point — the mean return — in the long-run.
Our belief that there is mean return uncertainty leads to a different conclusion. The chart Questioning
convergence compares two return simulations for US equities — one where there is no mean return
uncertainty (the left chart) and one where we assume mean return uncertainty (the right chart).
Simulated returns with no uncertainty
With no uncertainty in the mean return, we see only a smaller, light yellow range. The range reflects the
inherent risk of asset returns, or the standard deviation. It falls through time before eventually converging to
the mean return estimate, or 7.5%, in this example. This will be a familiar picture to many. Simulated returns
may have a forward-looking estimate of mean returns but assume no uncertainty in that mean. Importantly,
the range of outcomes (the light yellow) is often based only on historic volatility.
Simulated returns with uncertainty
With uncertainty added, we see both a dark and light yellow region. The range of outcomes does not and
should never vanish to zero in theory and we see that the dark yellow region is persistent. Why? Collapsing to
zero would require that the long-term expected return is known exactly (7.5% in our example). Instead of
converging to a single point, the range of outcomes converges to the dark yellow band of uncertainty around
our mean estimate at a horizon beyond 50 years. Another important consequence of adding uncertainty? The
range of potential return pathways is wider than the range excluding uncertainty. This is because we now
allow for more than just a single possible path of mean expected returns.
Questioning convergence
Medium to long-term return pathways and mean return estimates for US large-cap equities
This information is not intended as a recommendation to invest in any particular asset class or strategy or as a promise – or even an
estimate – of future performance. Source: BlackRock Investment Institute, April 2019. Data as of 28 February 2019. Notes: The line shows
our mean (central) return estimate for large-cap US equities (MSCI USA) on a five-year to long-term horizon. Assumptions are total return
and in US dollars. The interquartile range in light yellow shows the potential return pathways between the 25th and 75th percentiles
generated by our stochastic simulation. The chart on the left shows this range based on the historical volatility of the asset class alone. The
chart on the right includes the mean return uncertainty in orange. We discuss the factors determining the size of the mean return
uncertainty above. Indices are not intended to be indicative of any fund or strategy’s performance. It is not possible to invest directly in an
index.
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From theory to practice
The right amount of uncertainty
Incorporating uncertainty brings the portfolio construction process closer to real-world investing experience:
reality can stray considerably from any central expectation and the central expectation can be wrong, so we
do not want to place all our bets on getting this right. How much uncertainty should we consider in return
estimates? There is no single answer but several criteria guided our decision. We highlight the following:
1. Uncertainty and return simulations The range of potential return pathways widens when including mean
return uncertainty as shown in the Questioning convergence chart. But the bands representing the mean
return uncertainty should be narrower than this full range of pathways including the historical volatility of an
asset. This is intuitive: We would expect the range of daily stock prices to be larger than the range of the
rolling average of those stock prices. This determines the relative scale of the shaded areas on the chart – the
dark area should be narrower than the light.
2. Uncertainty and our cyclical views We use asset class models, that capture the cyclical economic and
market dynamics, to estimate returns at the ve-year horizon. These views inform the ve-year mean
expected return in our simulations. At this horizon, the uncertainty band should be in line with the predictive
power of our asset class models. In other words, we cannot have greater conviction in our five-year expected
returns than is warranted by the back-testing of our asset class models.
3. Uncertainty and diversi cation A portfolio construction framework incorporating uncertainty should
deliver a solution that is between the MVO solution, that places full conviction on mean returns, and the
minimum-variance solution, that places no conviction in mean returns. Too little uncertainty and an
optimisation will retrieve something close to mean-variance portfolio — typically very concentrated and
requiring constraints. Too much uncertainty and an optimisation will retrieve something close to a minimumvariance solution that prioritises diversification only. We are looking for a sensible middle ground.
Portfolio implications
A key benefit, in our view, of incorporating uncertainty is that we can allow for different conviction levels in
return expectations. Suppose an investor is choosing between two assets with the same expected return, and
similar risk, the con dence in estimating the return is likely the deciding factor in making a choice between
the two. We believe that the preferred asset should be the one where the investor has the higher conviction –
and this preference should intensify as an investor’s aversion to uncertainty grows.
A large part of the uncertainty around our mean return estimates comes from the quality and availability of
data that feeds into our models for various asset classes. Our conviction levels in our estimates for public
assets – especially xed income and equities – with a long history of granular, observable data are higher
than those for certain private market assets where the absence of indices and benchmarks requires the use of
proxies.
To design portfolios or to derive our strategic views, we use our simulated return expectations in a robust
optimisation. We follow a methodology that identifies the portfolio that performs ‘least badly’ over a range of
scenarios, it is therefore robust to errors where we have over-estimated returns. Robust optimisation
generally requires fewer constraints to retrieve a diverse portfolio than MVO. This approach also brings
greater flexibility; we can specify a level of uncertainty aversion that fits a particular investor. When designing
our strategic views we specify a level of uncertainty aversion that focuses on downside scenarios and is
be tting our current views on the cycle.
Our bottom line: Uncertainty in expected returns is well-known but often overlooked. Tackling uncertainty is
hard. We believe we have developed a systematic way of explicitly allowing for uncertainty in our CMAs that
ultimately leads to more resilient and investor-specific portfolios.
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BlackRock Investment Institute
The BlackRock Investment Institute (BII) leverages the firm’s expertise to provide insights on the global
economy, markets, geopolitics and long-term asset allocation – all to help our clients and portfolio managers
navigate financial markets. As a unique center of excellence, BII offers strategic and tactical market views,
publications and digital tools that are underpinned by proprietary research.
BlackRock’s Long-Term Capital Market Assumption Disclosures: This information is not intended as a recommendation to invest in any
particular asset class or strategy or product or as a promise of future performance. Note that these asset class assumptions are passive, and do not
consider the impact of active management. All estimates in this document are in US dollar terms unless noted otherwise. Given the complex riskreward trade-offs involved, we advise clients to rely on their own judgment as well as quantitative optimisation approaches in setting strategic
allocations to all the asset classes and strategies. References to future returns are not promises or even estimates of actual returns a client
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and is not intended to provide, and should not be relied on for, accounting, legal, or tax advice. The outputs of the assumptions are provided for
illustration purposes only and are subject to significant limitations. “Expected” return estimates are subject to uncertainty and error. Expected
returns for each asset class can be conditional on economic scenarios; in the event a particular scenario comes to pass, actual returns could be
significantly higher or lower than forecasted. Because of the inherent limitations of all models, potential investors should not rely exclusively on the
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actual trading, liquidity constraints, fees, expenses, taxes and other factors that could impact future returns.
Index Disclosures: Index returns are for illustrative purposes only and do not represent any actual fund performance. Index performance returns
do not reflect any management fees, transaction costs or expenses. Indices are unmanaged and one cannot invest directly in an index.
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