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2016 Emerging Market Fixed Income Outlook What to expect and where to consider investing 2016 INVESTMENT MANAGEMENT

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  • 2016 Emerging Market Fixed Income Outlook What to expect and where to consider investing

    2016

    INVESTMENT MANAGEMENT

  • 1

    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    2016 Emerging Market Fixed Income Outlook: What to expect and where to consider investing

    Looking ahead to 2016, we expect another challenging year for Emerging Market (EM) fixed income against a backdrop of rising global yields and continued fundamental deterioration in many countries. Nevertheless, we think much of this weakness is already priced in, and using our baseline assumption of stabilization in the U.S. dollar and commodity prices, we expect positive performance for EM fixed income over 2016. To help guide asset allocation, we use a Black-Litterman model that uses expected return assumptions and a conviction level to compute optimal portfolio allocations.

    Display 1: Model Recommendations and Return Forecasts

    EM Asset ClassModel Recommended

    Positioning2016 Expected

    Total Return2015 Total

    Return (YTD)

    Sovereign “External Debt” Underweight 2.7% 2.0%

    External “Corporate Debt” Overweight 3.2% 1.9%

    Sovereign “Domestic Debt” Overweight 9.6% -13.5%

    Source: MSIM and JPMorgan. Data as of December 4, 2015. EM sovereign domestic debt represented by the JPM Emerging Market Bond Index Global (EMBIG) Index. EM sovereign external debt represented by the JPM Global Bond Index-Emerging Markets (GBI–EM) Global Diversified. EM external corporate debt represented by the JPM Corporate Emerging Market Bond Index (CEMBI) Global Div.

    Forecasts/estimates are based on current market conditions, subject to change, and may not necessarily come to pass. There can be no assurance that actual market returns will mirror the team’s expected market returns shown. Actual results may significantly differ. Additionally, no representation is being made that any account, will or is likely to achieve results similar to those shown. Past performance is no guarantee of future results. The indices are shown for illustrative purposes only and are not meant to depict the performance of a specific investment.

    Investing in emerging markets fixed income involves additional risks. See disclosure page for more information.

    In forming our baseline assumptions, the following themes guide our outlook for EM fixed income.

    The bearishness of 2015 would be hard to repeat. That bearish view was driven by a relentless flow of negative EM headlines over the last 24 months but we expect 2016 to be better, as negativity dissipates and compelling valuations assert themselves. Concerns surrounding EM over 2015 ranged from fears of how a collapse in commodity prices would affect those countries relying on commodity exports, the impact of a rate hike by the U.S. Federal Reserve (“Fed”) and a possible repeat of the 2013 Taper Tantrum, to worries about a hard landing in Chinese growth impacting already vulnerable EM economies facing global growth headwinds.

    Despite the pessimism, both EM sovereign and external corporate debt have returned roughly 2% year-to-date, attractive performance when compared to much of the global equity and credit markets. The negative view on EM was primarily expressed in EM currencies, which

    AUTHOR

    MORGAN STANLEY INVESTMENT MANAGEMENT EMERGING MARKETS DEBT TEAM

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    experienced an extraordinary sell-off over the last 18 months, in some cases reaching levels we consider attractive. Clearly, countries that suffered large declines in the prices of their main exports needed to adjust, and sharp currency depreciation helps in that adjustment process. While many view EM currency performance as a sign of weakness, we view it as a sign of strength. One of the main reasons why we do not expect a fully-fledged EM crisis is that many emerging economies learned their lessons in 2013 and allowed floating exchange rates to act as a shock absorber for their economies. Moreover, despite recent growth disappointments and widening fiscal deficits, many EM economies still have ample foreign currency reserves, often in excess of their sovereign external debt. Hence floating exchange rates and net external creditor status provide EM policymakers with the tools to buy time to engage in the necessary reforms that may help bolster the medium-term economic outlook for their countries.

    It is unlikely that the Fed lift-off will trigger a broad-based sell-off in EM fixed income. There are four main reasons why we believe a repeat of the 2013 Taper Tantrum is unlikely. 1. The Fed lift-off shouldn’t come as a surprise to investors. At this stage, we would argue what matters more is the pace of rate hikes and not the exact timing of the lift off. 2. The Fed lift-off is not a signal for concerted monetary tightening elsewhere. The Bank of Japan (BoJ) and the European Central Bank (ECB) still hold a neutral to easing monetary policy stance, which could provide a partial offset to U.S. tightening.3. A more stable investor base. During the 2013 Taper Tantrum correction, close to $70 billion left the EM fixed income asset class, with roughly another $32 billion leaving in 2014 to 2015.1 This money has not come back, i.e., “tourists” have not returned, which should result in a relatively more stable investor composition. 4. Cheaper valuations and wider spreads provide a partial cushion against higher U.S. interest rates. Despite positive year-to-date performance, EM sovereign spreads have widened to levels similar to those around the peak of the Taper Tantrum.

    We are likely facing an environment of gradually increasing global interest rates. Despite an imminent Fed hike being largely priced in, we believe higher interest rates will contribute to the headwinds facing EM. While some countries are better positioned to handle such an environment, others with weak fundamentals, especially in the form of large current account and fiscal deficits (i.e., they are reliant on international financial markets for financing) are likely to experience protracted underperformance. From a sovereign debt perspective, we would argue that many commodity exporters such as Venezuela, Brazil, Colombia and Peru have seen a significant deterioration in their economic

    fundamentals since 2013 and remain vulnerable, while countries that have done their homework such as Mexico, Central Europe and parts of EM Asia, should outperform. For EM corporates, the direct impact of higher interest rates stems from the economic environment and fundamental position of the countries they operate in, while the indirect impact is reliant on the quality and currency of denomination of their balance sheets. Rollover risks are less of an imminent concern since many EM corporates took advantage of the period of extraordinary easy money under quantitative easing (QE) to lengthen the maturities of their outstanding debt, engaging in active liability management. Therefore even if U.S. rates were to move higher and funding markets were to tighten, many EM corporates would have ample time to adjust.

    EM fixed income will likely be less vulnerable than generally assumed; we expect EM currencies to stabilize in 2016 as the USD rally peters out in the second half. In this environment, performance for EM domestic debt would be driven by carry (primarily yield), as well as currency appreciation. In particular, we expect those countries that have experienced significant trade-weighted currency depreciation in excess of a potentially negative move in their terms-of-trade2 to begin to recover next year. Within domestic currency bonds the opportunity is more limited, in our view, to countries that are oversold fundamentally or have room to ease monetary policy further given more dovish ECB/BoJ policy actions and/or lower oil prices. What remains as one of our biggest worries is that those countries with the means to buy time to re-start long-delayed reforms fail to do so and waste precious foreign currency reserves for no sustainable gain. In such an environment, it will pay to be selective within EM fixed income during this challenging period for global economic growth.

    In the following sections, we provide greater detail on:• EM fixed income asset class return estimates for 2016 • The sensitivity of the estimates to alternative oil

    price scenarios• The implications of the return scenarios on the optimal

    portfolio allocation decisions for an investor that can invest opportunistically across EM fixed income

    2 The ratio of an index of a country’s export prices to an index of its import prices.

    “For EM corporates, the direct impact of higher interest rates stems from the economic environment and fundamental position of the countries they operate in, while the indirect impact is reliant on the quality and currency of denomination of their balance sheets.”

    1 Source: JP Morgan, Standard Chartered.

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    We expect 2016’s performance for sovereign and external corporate debt to be similar to 2015, with a total return in the lower single digits. Sovereign domestic debt could potentially outperform if EM currencies stabilize, and in some cases, recover. We broadly expect EM fundamentals to continue to deteriorate modestly next year, but the poor growth performances this year by Brazil and Russia are unlikely to be repeated, which would drive a modest EM growth recovery in 2016.

    Our “fair value” frameworks for EM external sovereign and corporate debt, as well as for EM domestic currency bonds and currencies, allow us to model return potential in the various EM fixed income investment choices subject to a set of assumptions. The fair value models are briefly described in the Model Appendix, and the country-by-country results for sovereign and external corporate debt are also provided. As inputs, we use Bloomberg consensus end-2016 forecasts for 10-year U.S. Treasuries (2.86%), EUR/USD (1.07) and 10-year Bunds (1.09%), while for oil we assume stable prices at current levels ($43.3 per barrel for Brent) so as not to bias the outcome in favor of one asset class or another (we test the oil sensitivity of the output in alternative trials). Risk aversion, as proxied by the VIX,3 is assumed to remain stable at its three-year historical average. In general, we assume that dislocations relative to “fair value” close with a six-month half-life. For key country-specific macro variables where we disagree with the one-year-ahead Bloomberg consensus estimates, we override these inputs with our estimates, most noticeably in Brazil and the Ukraine, where we are more negative on the outlook. Even though our models are designed to reasonably capture the impact of macroeconomic variables on asset prices, their valuation signals are weakened in situations of heightened political volatility (as in the case of Brazil, for example), poor market liquidity, and for assets trading at distressed levels (such as debt instruments in Venezuela, Mongolia or the Ukraine).

    Display 2: Our 2016 total return expectations

    Emerging Markets

    Return SourceSovereign

    External DebtExternal

    Corporate DebtSovereign

    Domestic Debt

    Carry 6.3% 6.0% 6.8%

    Spread change 0.6% 0.1%

    UST yield change -4.2% -2.9%

    Local yield change -1.6%

    FX 4.5%

    Total Return 2.7% 3.2% 9.6%

    Source: MSIM. Data as of December 8, 2015. EM sovereign domestic debt represented by the JPM EMBIG Index. EM sovereign external debt represented by the JPM Global Bond Index-Emerging Markets (GBI–EM) Global Diversified: EM external debt represented by the JPM CEMBI Global Div.

    Forecasts/estimates are based on current market conditions, subject to change, and may not necessarily come to pass. There can be no assurance that actual market returns will mirror the team’s expected market returns shown. Actual results may significantly differ. Additionally, no representation is being made that any account, will or is likely to achieve results similar to those shown. The indices are shown for illustrative purposes only and are not meant to depict the performance of a specific investment. See disclosure page for more information.

    Sovereign External DebtWe expect a marginal tightening of sovereign external debt spreads (8 basis points) and a total return of 2.7 percent in 2016, and anticipate Venezuela being the top performer while the Ukraine is the most significant underperformer. Display 2 shows the sovereign external debt expected return breakdown. Carry (+6.3%) provides a very sizable cushion for the expected widening in U.S. Treasuries (which detracts 4.2 percentage points from returns), while the spread tightening we predict is too small to contribute meaningfully to returns (+0.6%.) In our baseline scenario, Venezuela is the largest positive contributor to expected returns (see Appendix) based on already depressed valuations coupled with the opposition’s victory in National Assembly elections in early December, which may lead to revived hopes of regime change and a friendly restructuring of external debt next year. High-carry Ecuador should also outperform, despite our expectations of a marginal widening of spreads, while in the case of El Salvador, spread tightening is the main potential source of positive returns. On the opposite side of the spectrum, we expect Ukraine to be the main detractor to expected returns; current valuations seem too high and, in our view, do not accurately reflect the country’s very fragile debt sustainability situation, messy domestic politics and uncertain geopolitics with neighboring Russia. Finally, we believe Turkey and Brazil will also underperform. In the case of Turkey, policy uncertainty and a vulnerable external

    2016 Return Forecast Details

    3 The Volatility Index (VIX) is the ticker symbol for the Chicago Board Options Exchange Market Volatility Index, a popular measure of the implied volatility of S&P 500 index options. It represents one measure of the market’s expectation of stock market volatility over the next 30-day period. The VIX is quoted in percentage points and translates, roughly, to the expected movement in the S&P 500 index over the next 30-day period, which is then annualized.

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    sector at a time of imminent Fed liftoff may cause spreads to widen. Our less optimistic than consensus view on fiscal developments and poor growth dynamics largely drive the spread widening we predict for Brazil next year.

    External Corporate DebtWe expect external corporate debt to have a total return of +3.2% in 2016, mostly driven by carry but weighed down by an expected increase in U.S. rates. In fact, the anticipated carry return is about 6%, while wider U.S. Treasury yields should lower total returns by 2.9 percentage points. Meanwhile, the contribution of spread tightening is minimal (+0.1%), as we see a negligible decline in EM corporate spreads next year. Our model suggests that Mongolia and the Ukraine are expected to outperform next year, although we interpret the results cautiously since several corporates in those two countries are trading at a distressed level. In the case of the Ukraine, carry is expected to be the main driver of outperformance, but spread tightening may also add to expected returns, despite our sovereign spread widening forecast next year.4 We predict Brazilian corporates to be the worst performer in 2016, delivering expected returns of -3.5%, and reflecting our bearish view on Brazil sovereign debt next year. Finally, Turkish corporates are expected to underperform in line with our negative assessment of the sovereign.

    Sovereign Domestic DebtWe expect sovereign domestic debt to return 9.6% (in USD) in 2016. The largest portion of this, 6.7%, comes from carry. FX appreciation adds 4.4%, while capital appreciation subtracts -1.6%. In our baseline scenario, Indonesia is the top performer. Indonesian rates and currency adjusted significantly in 2015, and we expect gains in 2016 to be driven by both carry and currency appreciation. A tame inflationary environment coupled with slowing growth will likely prompt the central bank to enter into an easing cycle, contributing to capital appreciation. We expect Brazil to perform roughly in line with the sovereign domestic debt market. While we continue to expect a further weakening of the Brazilian real (BRL) in 2016, driven by deteriorating macroeconomic fundamentals and messy domestic politics, the drag on performance should be well-cushioned by the carry on Brazilian bonds, which is the highest in the investable sovereign domestic debt universe. Our overall positive, model generated, return forecast for domestic debt is not reliant on our Brazil return forecast. In a situation where Brazil returns were zero for next year, EM local returns would decline by 80 basis points (bps) to a still significant 8.8%. For sovereign domestic debt performance, the primary importance of the broad U.S. dollar outlook is followed a close second by the likely path and stability of the Chinese Renminbi (CNY). While decisions about the direction of the CNY will be made largely on political grounds, we believe that the CNY eventually needs to weaken to combat the significant real effective rate appreciation it has experienced while it has been de-facto pegged to the U.S. dollar (USD) during a time of USD strength. A move towards an exchange rate managed against a trade-weighted basket is, in our view, is likely already underway.

    4 This is due to the fact we did not find a significant relation between corporate and sovereign spreads for Ukraine.

    Display 3: 2016 Total return expectations under alternative oil price scenariosSovereign External Debt External Corporate Debt Sovereign Domestic Debt

    Return SourceBaseline

    (Brent at Spot)Brent at $61

    Brent 50% down

    Baseline (Brent at Spot)

    Brent at $61

    Brent 50% down

    Baseline (Brent at Spot)

    Brent at $61

    Brent 50% down

    Carry 6.3% 6.3% 6.3% 6.0% 6.0% 6.0% 6.8% 6.8% 6.8%

    Spread change 0.6% 2.2% -5.0% 0.1% 1.2% -1.2%

    UST yield change -4.2% -4.2% -4.2% -2.9% -2.9% -2.9%

    Local yield change -1.6% -2.0% -1.0%

    FX 4.5% 8.0% -4.6%

    Total Return 2.7% 4.3% -2.9% 3.2% 4.3% 1.9% 9.6% 12.8% 1.2%

    Source: MSIM. Data as of December 8, 2015. EM sovereign domestic debt represented by the JPM EMBIG Index. EM sovereign external debt represented by the JPM Global Bond Index-Emerging Markets (GBI–EM) Global Diversified: EM external debt represented by the JPM CEMBI Global Div.

    Forecasts/estimates are based on current market conditions, subject to change, and may not necessarily come to pass. There can be no assurance that actual market returns will mirror the team’s expected market returns shown. Actual results may significantly differ. Additionally, no representation is being made that any account, will or is likely to achieve results similar to those shown. The indices are shown for illustrative purposes only and are not meant to depict the performance of a specific investment. See disclosure page for more information.

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    Alternative Oil Price ScenariosTo investigate the sensitivity of expected returns to inputs we consider two alternative oil price scenarios. In particular, we explore a bullish scenario of Brent at $61 (per barrel) by year-end 2016 (in line with our expectations, and implying a 50% rise from current levels) and a symmetric downside scenario of a 50% decline in oil price from already depressed levels. We caveat that these are exogenous changes to one variable in the model, keeping all others constant.5

    EM domestic debt sharply outperforms in the $61-oil scenario, while EM corporates offer superior returns under the low oil-price scenario. When assuming Brent at $61, EM domestic debt outperforms the other two asset classes dramatically, with a total return of 12.8% (3 percentage points above the baseline), versus 4.3% returns for both EM corporates and hard currency sovereign (roughly 1% above the baseline). In the bearish (50% drop in oil prices) scenario, EM corporates are more resilient, delivering expected returns of +1.9%, versus sovereign external debt (-2.9%) and sovereign domestic debt (+1.15%). For the latter, we find that currencies are more sensitive to changes in oil price assumptions than local rates, the key driver of the alternative return scenarios. Finally, in terms of individual country performance we find, unsurprisingly, that oil-dependent economies such as Russia, Venezuela, Colombia and Kazakhstan tend to outperform in an improving oil price scenario, while commodity importers such as Turkey or those in Central and Eastern Europe tend to underperform (this is consistent across the three EM asset classes).

    5 In future work, we will consider the response of other explanatory variables to a shock in oil prices and their impact on assets’ expected returns

    Portfolio Management Implications using the Black-Litterman FrameworkWe applied the Black-Litterman framework to analyze the implications of our 2016 return forecasts on a hypothetical opportunistic EM portfolio. Under the baseline scenario, the optimal portfolio features overweight allocations to EM domestic and external corporate debt, and an underweight exposure to sovereign external debt. Our Black-Litterman implementation prescribes overweight exposures (using the initial portfolio as benchmark) to EM corporates (44% weight), and EM domestic (41% weight), while recommending an underweight exposure to EM hard currency sovereign debt (11.7% weight), and a small allocation to U.S. Treasuries. Under the more optimistic scenario of Brent at $61, the optimal portfolio exhibits overweight exposures to both sovereign domestic debt and external corporate debt, while cutting exposure to sovereign external debt even further. In a downside scenario of a 50% decline in oil, the Black-Litterman framework recommends increasing our overweight to EM corporates, and to U.S. Treasuries, while sharply underweighting exposures to EM sovereigns in both hard-currency and local currencies. Display 5 depicts the Black-Litterman optimal portfolios under the three scenarios.

    Display 4: 2016 EM sovereign domestic debt expected returns (baseline)

    25

    20

    15

    10

    5

    0

    -5

    -10

    -15Indonesia India Malaysia KoreaColombia Mexico Chile South

    AfricaRussia GBI TurkeyThailand PolandBrazil Peru Philippines Hungary Romania China

    ■ Capital Appreciation ■ Carry ■ FX Total Return Expectations

    Source: MSIM. Data as of December 8, 2015. Forecasts/estimates are based on current market conditions, subject to change, and may not necessarily come to pass. There can be no assurance that actual market returns will mirror the team’s expected market returns shown. Actual results may significantly differ. Additionally, no representation is being made that any account, will or is likely to achieve results similar to those shown. The indices are shown for illustrative purposes only and are not meant to depict the performance of a specific investment. See disclosure page for more information.

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    Display 5: Market and Black Litterman portfolio weights

    Source: MSIM. Data as of December 8, 2015. The hypothetical emerging market debt portfolio is provided for illustrative purposes only and is not meant to depict the emerging market debt asset allocation of any specific strategy or investment. Investors should carefully review their investment objectives, risk tolerance, and time horizon with their financial professional prior to implementing an asset allocation to emerging markets debt.

    ConclusionIn this piece, we have discussed our 2016 total return expectations for the EM fixed income asset classes and presented a framework that we hope can guide investment decisions in opportunistic EM fixed income portfolios. Using our suite of “fair-value” models, we obtained expected return estimates for the three EM sub-asset classes. These views on asset returns, as well as their associated conviction levels, were then applied to a Black-Litterman model that computes optimal portfolio allocations among a specific asset universe. We found that in our baseline scenario, the model recommends overweight exposures in EM external corporate debt and EM sovereign domestic debt, and an underweight allocation to EM sovereign external debt. We also considered alternative scenarios for oil, and find that EM corporate expected returns are the most resilient to a bearish oil price scenario. We acknowledge that our framework, based on “fair-value” models to inform our views on asset returns, has some limitations, particularly when attempting to properly capture idiosyncratic dynamics in certain countries and corporates (such as fast-changing political landscapes, specific corporates under distress, and poor market liquidity conditions). Furthermore, it remains silent on the timing of expected returns realization (though implicit in our bullish EM local view is a scenario of EM FX stabilization vs USD, which we see materializing during the H216). All in all, we believe that the proposed approach provides valuable signals and can effectively help guide investment decisions in the context of an EM-dedicated portfolio.

    70%

    60%

    50%

    40%

    30%

    20%

    10%

    0%Sov. External Debt Sov. Domestic Debt Ext. Corp. Debt US Treasuries

    ■ Equilibrium portfolio ■ Baseline (Brent at Spot) ■ Brent at $61 ■ Brent 50% down

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    Sovereign External DebtWe forecasted 2016 sovereign external debt total returns using our “fair-value” sovereign spread model framework. We modelled over 90% of countries included in the JPM EMBI Global Index (a fair representation of the investable sovereign external debt universe, the “index” in our opinion), and we assumed that a set of domestic, and external variables explained spread variation for any given country. In particular, domestic variables included: GDP growth, fiscal balance, external debt, the stock of international reserves, current account balance, Central Bank policy rate, inflation rate, and a political risk rating indicator.6 In terms of external variables, empirical research7 has shown that global liquidity conditions, commodity prices, and global risk aversion play a major role in explaining sovereign spreads. We thus included the following external variables in our model: the VIX index, the 3-month and 10-year U.S. Treasury yields (or Bund rates, for Euro-centric credits), Brent oil price, and U.S. High Yield spreads. We estimated the relationship between sovereign spreads and explanatory variables country-by-country via ordinary-least-squares (OLS)8 techniques, using a sample period ranging from Jan 2010 through Oct 2015. We evaluated the equations using the 2016 year end Bloomberg consensus estimates, or, alternatively, we used our own forecasts when we disagreed with consensus. Total expected sovereign external debt returns were obtained by considering the following sources of returns: carry and returns due to changes in 10-year U.S. Treasury yields and in sovereign external debt spreads. Total expected returns for the overall index were calculated via a weighted average of individual countries’ returns, using market capitalization in the index as the weighting factor.

    External Corporate DebtWe assumed a country’s EM corporate spread to be driven by its sovereign counterpart. In particular, we used a linear positive relation between a country’s corporate spread and its sovereign spread (using the JPM CEMBI BD Index and JPM EMBI Index to represent the corporate and sovereign

    “Fair-Value” Model Appendix

    6 The Political Risk Rating Indicator data comes from PRS Group, and includes assessments on areas such as government stability, law and order, socioeconomic conditions, and corruption, among others. 7 See Longstaff, F., Pan, J., Pedersen, L., and Singleton K. 2011. “How Sovereign is Sovereign Credit Risk?” American Economic Journal - Macroeconomics 3: 75-103.8 Ordinary Least Squares (OLS) is a statistical technique for estimating unknown parameters in a linear relationship.9 Sample period ranges from Nov 18, 2012-Nov 17, 2015.10 We use 5-year CDS spreads whenever the sovereign is not represented in the EMBIG index. For those countries where neither EMBIG nor CDS spreads are available, we assume that fair value corporate spreads would adjust in the same proportion as our predicted change in EMBIG spreads.

    11 We assume full convergence of spreads to fair-value by year end 2016, in line with our assumptions for the sovereign case.12 Using forward looking expectations is helpful in three ways. First, econometrically, it helps avoid the adverse effects from reverse causality and serial correlation. Second, instead of relying on data points that come out with a significant lag, we are able to incorporate forward looking expectations. Third, it allows us to plug in our own forward looking expectations when we hold out-of-consensus views or want to explore the implication of various scenarios.13 We have explored the SERV model in more detail in a recently released paper. See Morgan Stanley Investment Management. 2015. Investment Focus, “Are Emerging Market Foreign Currencies Finally Attractive?”

    markets, respectively) to estimate corporate “fair-value”. We estimated these relations via OLS, using daily observations for the last three years.9,10 We understand that this approach does not take into account particular circumstances of corporates in some countries, but, as a first approximation, we believe our framework provides a reasonable indication of where corporate spreads are heading next year. Plugging our “fair-value” sovereign spread forecasts into the equations, we obtained “fair-value” EM corporate spread forecasts. Finally, total expected returns for each country’s corporates were obtained by considering carry and duration sources of return.11 Our EM corporate CEMBI Broad Diversified 2016 expected return is equal to the market-cap weighted average of each country’s individual expected return.

    Sovereign Domestic DebtThe team’s estimates of total local currency bond returns were guided by two models. The first was the Long-Term “Fair Value” (LTFV) model, which provided a tool for assessing “fair-value” local yields. The second was the Structural Exchange Rate Valuation (SERV) model which provided an assessment of “fair-value” for the exchange rate. The total expected return combined the carry and capital appreciation calculations from the LTFV model and the currency return component from the SERV model.

    The LTFV evaluates 5-year yields across countries considering domestic and global factors. We decomposed local rates into three broad determinants: i) domestic short-term rates (highly influenced by central bank policy), ii) domestic term premia, and iii) additional term premia demanded by foreign investors. Local factors included monetary policy rates, inflation, growth, and the current account balance. International factors included U.S. Treasury Yields, VIX, MOVE, oil, FX implied volatility, the U.S. Fed Funds Rate, and a dummy for the global financial crisis. Where possible, we used interpolated one-year forward expectations of key macroeconomic variables.12 The SERV FX model is a Behavioral Equilibrium Exchange Rate (BEER) model that uses a similar set of domestic and global variable to assess the fair value exchange rate.13

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    In a typical portfolio choice problem, an investor faces the task of allocating a given amount of wealth among a set of assets. His/her ultimate investment decision will depend on the assets’ return, volatility and correlations profile, as well as his/her tolerance to risk. The Black-Litterman approach provides a framework to help solve such a portfolio choice problem. In particular, it lays out a method to compute expected returns by optimally blending two relevant pieces of information: assets’ expected returns implied by current market holdings,14 and the investor’s own views on returns (adjusted by his conviction levels). The optimal portfolio weight on each asset is then obtained from a formula that depends on the investor’s risk tolerance parameter, the volatility/correlation of asset returns, and the expected return computed within the Black-Litterman framework.

    We believe the classical mean-variance optimization (MVO) framework proposed by Markowitz15 has several pitfalls. In particular, portfolios computed within the Markowitz paradigm are usually unintuitive, highly concentrated on a small number of assets, and with weights that are extremely sensitive to small changes in inputs. In addition, Michaud (1989)16 showed that mean-variance optimizers are actually ‘estimation-error maximizers’: they significantly over(under)weight securities with large (small) estimated returns, negative (positive) correlations and small (large) variances, but those are precisely the securities that are most likely to have large estimation errors. Telling evidence of the practical difficulties encountered by the MVO approach is the fact that the portfolios it generates are outperformed by naïve ones featuring equal weights on each asset.

    The Black-Litterman framework17 attempts to resolve the practical problems of the Markowitz approach by using the market equilibrium as a reference point, and by incorporating investor’s views, which may lead to optimal portfolio weights that differ from the market allocations. More precisely, the basic idea behind Black-Litterman is that there is a prior distribution of expected returns, whose mean can be implied from market equilibrium. This prior is then optimally combined (using Bayes’ Theorem) with investor’s views about asset returns to obtain posterior distributions for both expected returns and the variance-covariance matrix. The optimizer then uses those inputs to compute optimal portfolio weights. By incorporating some notion of central value or reference point, the Black-Litterman approach resembles the shrinkage method,18 but with a more solid grounding in economic theory.

    In our implementation, we assume an investable set comprising EM sovereign external, external corporate, and sovereign domestic debt, as well as U.S. Treasury bonds. Furthermore, we assume the market currently holds equal weights on each EM asset subclass, while having zero exposure to U.S. Treasuries. From this starting point, and using our estimates of the volatilities/correlations of these four assets plus a calibration of the risk aversion parameter from market data, we obtain the market implied expected returns. We then combine market-implied returns with our views on expected returns for the four assets to obtain Black-Litterman expected returns. We also specify conviction levels for our views: EM domestic debt 20%, EM external debt 40%, EM corporate debt 60%, and finally, a 50% conviction level for US Treasury returns. Given the higher volatility of currencies, we put a lower conviction level on our expected returns for EM domestic debt.19

    Technical Appendix on Black-Litterman

    14 In the Black-Litterman approach, the expected returns implied from the market portfolio are called ‘equilibrium returns’. This is due to the fact that the framework explicitly assumes a capital market in equilibrium, and comfortable with the current weights in the market portfolio. In most applications, asset weights are derived from market capitalizations of each asset. In our EM-focused implementation, we define the market portfolio as the one allocating equal weight on each EM sub-asset class.15 Markowitz, H. 1952. Portfolio Selection. The Journal of Finance 7 (1): 77–91.16 Michaud, R. 1989. “The Markowitz Optimization Enigma: Is ‘Optimized’ Optimal.” Financial Analysts Journal, Jan-Feb 1989.

    17 Black, F. and Litterman, R. 1990. “Asset Allocation: Combining Investor Views with Market Equilibrium.” Goldman Sachs Fixed Income Research Note.18 Shrinkage estimation of the variance-covariance matrix of returns tackles the MVO’s instability issues, by ‘shrinking’ extreme coefficients towards more central values. For a more detailed explanation, please refer to Ledoit, O. and Wolf, M. 2003. “Honey, I Shrunk the Sample Covariance Matrix.” Working paper. 19 Our implementation restricts weights to be in the interval (0%-100%.) Furthermore, they should add up to 100% (that is, a fully invested portfolio).

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    This material is for Professional Clients only, except in the U.S. where the material may be redistributed or used with the general public.

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    EM Sovereign Domestic Debt represented by the JPM EMBIG Index: The JPMorgan Emerging Markets Bond Index Global tracks total returns for traded U.S. dollar-denominated debt instruments in the emerging markets. Included in the EMBI Global are US dollar-denominated Brady bonds, Eurobonds, and traded loans issued by sovereign and quasi-sovereign entities. The EMBI Global is a traditional, market capitalization weighted index.

    EM Sovereign External Debt represented by the JPM Global Bond Index-Emerging Markets (GBI–EM) Global Diversified: The JPMorgan Government Bond Index-Emerging Markets (GBI-EM) tracks local cur-rency bonds issued by Emerging Market governments. GBI-EM Global is positioned as the investable benchmark that includes only those countries that are accessible by most of the international investor base (Excludes China and India as of September 2013). The Diversified version limits the weights of countries with larger debt stocks and redistributes those weights to countries with smaller weights. The maximum weight to a country is capped at 10%. The excess is redistributed to those countries that have a market capitalization of less than 10%. The portion that is redistributed is based on the market capitalization of each country, which preserves the relative size of each market within the index.

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    Forecasts/estimates are based on current market conditions, subject to change, and may not necessarily come to pass. There can be no assurance that actual market returns will mirror the team’s expected market returns shown. Actual results may significantly differ. Additionally, no representation is being made that any account, will or is likely to achieve results similar to those shown. The indices are shown for illustrative purposes only and are not meant to depict the performance of a specific investment. See disclosure page for more information.

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    Country Current Spread2016F Fair Value

    EMBIG Spread2016F

    Return20

    Venezuela 2393 1743 47.57

    El Salvador 585 425 15.16

    Ecuador 1218 1240 10.91

    Lebanon 477 353 9.62

    Costa Rica 505 432 8.32

    Indonesia 318 234 7.35

    South Africa 325 253 5.97

    Sri Lanka 459 422 5.26

    Croatia 295 256 3.71

    Mexico 277 239 3.16

    EM Sovereign External Debt 408 400 2.67

    Chile 229 193 2.63

    Argentina 466 455 2.56

    Serbia 243 235 1.96

    Colombia 282 263 1.73

    Hungary 180 179 0.42

    Poland 91 79 0.18

    Korea 56 49 -0.01

    Lithuania 105 110 -0.03

    China 171 182 -0.49

    Uruguay 272 262 -0.53

    India 151 181 -0.54

    Malaysia 220 236 -0.79

    Romania 170 178 -1.21

    Panama 208 213 -1.39

    Philippines 119 117 -1.65

    Kazakhstan 368 409 -1.70

    Russia 261 347 -2.62

    Peru 220 247 -4.11

    Brazil 415 567 -6.91

    Turkey 271 365 -6.95

    Ukraine 714 1464 -32.72

    Appendix – Sovereign External Debt Total Return Expectations

    20 We assumed the EM Sovereign External Debt (represented by the JPM EMBI Global Index) spread misalignment is fully corrected by yearend 2016.

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    Country Current Spread2016F Fair

    Value Spread2016F

    Return21

    Mongolia*,22 10868 4991 155.60

    Ukraine*,22 2324 1646 39.98

    Zambia* 1489 1162 27.68

    Iraq* 1516 1232 23.24

    Jamaica* 795 562 17.40

    Colombia 609 475 12.27

    Indonesia 755 617 12.08

    Panama 363 159 11.93

    Guatemala* 533 395 11.04

    Argentina 629 534 9.76

    Dominican Republic*

    822 794 8.82

    Paraguay* 561 466 8.64

    Nigeria* 1005 1062 8.14

    Philippines 482 414 7.38

    El Salvador 567 519 6.89

    Mexico 375 314 5.91

    Bahrain** 418 383 5.61

    Ghana* 712 775 5.57

    Bangladesh*** 562 551 5.46

    Croatia 501 473 5.36

    South Africa 420 384 4.86

    Kuwait*** 411 403 4.70

    Macau*** 506 495 4.59

    Israel** 310 261 4.42

    Barbados*** 501 491 4.36

    Azerbaijan* 639 692 4.25

    Appendix – External Corporate Debt Total Return Expectations

    Country Current Spread2016F Fair

    Value Spread2016F

    Return21

    Kazakhstan 745 829 4.17

    Poland 317 288 3.41

    EM External Corporate Debt

    397 394 3.23

    Chile 352 340 2.78

    India 286 274 2.53

    UAE*** 277 272 2.36

    Hong Kong*** 264 258 2.19

    Korea 167 143 2.02

    Malaysia 222 214 1.98

    Thailand** 232 216 1.96

    Jordan*** 265 260 1.94

    Singapore*** 192 188 1.76

    T&T* 433 469 1.76

    Russia 474 548 1.42

    Morocco* 345 336 1.33

    Peru 372 387 1.32

    Hungary 215 221 1.26

    Taiwan*** 166 163 0.81

    Oman*** 76 74 0.42

    Saudi Arabia** 112 120 0.38

    Egypt* 226 266 0.23

    China 331 380 -0.08

    Qatar** 167 180 -0.15

    Czech Republic** 172 166 -0.58

    Turkey 342 474 -2.39

    Brazil 690 883 -3.52

    EM External Corporate Debt as represented by the JPM CEMBI BD Index.* We assumed that the 2016 EMBIG subindex will adjust in same relative terms as the EMBIG index.** We used CDS spreads as proxy for sovereign spreads, and assumed the 2016F for CDS spread adjusted in same relative terms as the EMBIG index.*** No data on sovereign spreads is available. We assumed that 2016F JPM CEMBI BD subindex adjusted in same relative terms as the JPM EMBIG index.21 Forecast return assumes the full spread misalignnment is corrected within a year.22 High expected returns in these countries are due to specific names that trade at highly distressed levels. To the extent they are ongoing concerns, we made the decision to keep them in our expected return calculations. However, given their very low share on the index, the impact of these extreme outliers on the aggregate corporate index return is negligible (in the case of Mongolia, the name represents 4.8 bps of the index, while in Ukraine, the two distressed corporates comprise about 20 bps of the JPM CEMBI Broad Diversified Index market cap.)

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

    Country FX CarryCapital

    Appreciation Total Return

    Indonesia 6.56 8.55 5.93 21.04

    India 13.62 7.79 -4.06 17.35

    Malaysia 10.15 4.08 1.11 15.33

    Colombia 12.55 6.60 -4.62 14.53

    Mexico 9.08 5.52 -0.60 14.00

    Chile 9.71 4.33 -0.70 13.34

    South Africa23 2.89 7.84 0.83 11.55

    Russia 1.02 8.77 0.15 9.95

    EM Sovereign Domestic Debt 4.45 6.75 -1.62 9.59

    Thailand 5.97 2.14 -0.17 7.94

    Brazil -5.75 15.24 -1.62 7.87

    Turkey 5.26 10.44 -9.04 6.66

    Korea 4.43 1.86 -0.67 5.63

    Peru 0.99 6.15 -1.62 5.52

    Poland 6.42 1.80 -3.65 4.57

    Philippines 4.54 3.40 -6.02 1.92

    Hungary -3.33 1.84 -0.31 -1.81

    Romania -2.53 2.53 -4.19 -4.19

    China -6.09 3.34 -6.17 -8.92

    Sovereign Domestic Debt Total Return Expectations

    EM Sovereign Domestic Debt represented by the JPM GBI-EM GD Index.23 Estimate was made December 8, 2015, which was before recent changes to the Finance Ministry were announced.

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    2016 EMERGING MARKET FIXED INCOME OUTLOOK: WHAT TO EXPECT AND WHERE TO CONSIDER INVESTING

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