G4 vs Emerging Markets: where are we in the economic cycle?

  • In 2017, we saw a broad-based cyclical upswing in global growth, driven in part by emerging economies. After nearly a decade in decline, the growth gap between emerging markets and developed markets is rising once more, but where is each group in its respective economic cycle?
  • Using a range of activity measures, we apply statistical estimations of current trends to measure the implied stages of the cycle for a basket of emerging market economies versus the G4.
  • Although both groups were synchronised before the crisis, their respective economic cycles have since diverged. G4 economies appear to be well into their cycle, but EMs could be at the beginnings of a new cycle, with scope for growth to rise further as they return to trend – a positive signal for EM currency investors.

With thanks to Canberk Yalcin for his research assistance and contribution.

Global growth has picked up over the past year, driven in part by emerging market economies. In the IMF’s recently published World Economic Outlook Projections, World output growth in 2017 was estimated at 3.7%, versus 3.2% in 2016. Similarly, Emerging Market and Developing Economies grew by 4.7%, versus 4.4% in the previous year. This cyclical upswing has been the broadest-based growth spurt we’ve seen since 2010, supported in particular by a pickup in global trade, investment and manufacturing; with business and consumer confidence also following suit. Assuming global financial conditions remain favourable, and with world trade expanding, emerging markets are positioned to continue benefiting from these tailwinds, especially those with large export shares.

Of particular interest to investors in emerging market currencies is the growth gap between emerging market (EM) and developed market (DM) economies. In Figure 1, we compare the average growth rate for a basket of 17 EMs[1] versus a G4 basket (US, Euro area, Japan and UK). As can be seen from the chart, the gap between these two baskets wavered around 4% for much of the 2000s, leading up to the Global Financial Crisis (GFC), partly supported by strong EM tailwinds from increasing global trade and financial integration. But this gap then shrunk persistently from 2008 to 2015, to a meagre 1.0%. In the past two years, though, this gap has begun to expand once more. So could this be a positive signal for EM currency investors?

Figure 1: The EM-G4 growth gap has begun to expand once more

Sources: Record, Bloomberg. Data to Q3 2017. [1] EM universe: Brazil, Chile,  China, Colombia, Czech Rep., Hungary, Indonesia, Mexico, Peru, Philippines, Romania, Russia, South Africa, South Korea, Taiwan, Thailand and Turkey. India, Malaysia and Poland are not included due to data limitations.

A key question remains; at what point in their respective economic cycles are DMs and EMs right now? Historically, economies tend to operate within cycles of activity, fluctuating between expansions and contractions, around a longer-term trend. So an economy’s current position in the cycle is critical in determining growth potential over the short-to-medium term. Although a wide range of methods are available to try to estimate economic cycles using forecasts of long-term growth trends, they all rely on macroeconomic assumptions and projections. To overcome such assumptions, a more objective method is to focus purely on statistical, contemporaneous trends.

Drawing on a methodology previously used by Goldman Sachs, we use z-scores[2] to observe a range of activity measures versus the statistical trend (Figure 2). First, we examine Gross Domestic Product (GDP), Gross Capital Formation (GCF) and Industrial Production (IP) data, aggregated into evenly-weighted indices based on our standard Record EM universe of 20 emerging economies* and our standard basket of G4 developed economies; creating six aggregate series in total. Second, we convert each aggregate series into log form. Finally, we measure the gap of each log series versus its 2000-2017 linear trend, and plot this gap as a z-score[2]. This allows us to observe the deviation of each log series from its trend, measured in standard deviations, so that all six of our series are directly comparable.

Figure 2: Emerging markets could be at the beginning of a new cycle

Sources: Record, Bloomberg. Data to Q3 2017. *Due to data availability: (a) EM GDP is the aggregate of 17 countries including Brazil, Chile, China, Colombia, Czech Rep., Hungary, Indonesia, Mexico, Peru, Philippines, Romania, Russia, South Africa, South Korea, Taiwan, Thailand and Turkey; (b) EM IP is the aggregate of 10 countries including China, Czech Rep., Hungary, Indonesia, Mexico, Poland, South Africa, South Korea, Taiwan and Turkey; and (c) EM GCF is the aggregate of 7 countries including Brazil, Colombia, Czech Rep., Hungary, Philippines, South Korea and Taiwan.

A few key observations can be made from the above chart. First, it is clear that both G4 and EM economies were in a steady and synchronised expansionary phase in the lead up to the GFC, followed by a sharp contraction in 2009. But, since then, G4 and EM cycles appear to have diverged. While EM activity appears to have seen a pronounced bounce back in 2010-11, G4 economies languished behind, taking five years to recover back to trend. And, then, as the G4 recovery from the crisis has continued to extend, EMs once again began to fall behind their respective trends. It is clear that the G4 and EM economic cycles no longer appear to work in synchronisation. Most important of all though, in the past year EMs appear to have reached a turning point. While G4 economies clearly appear to be well into the cycle, EMs could be at the beginnings of a fresh new cycle, with plenty of scope for activity growth to rise further, as they return to trend.

As noted before, such cycle analysis relies heavily on the long-term trend assumption. If the long-term series trends turn out to be significantly different from our statistically-observed 2000-2017 log trends, the results could be somewhat different. We can try to mitigate this by cross-checking our findings with long-term growth forecasts identified by other recognised institutions. Figure 3 shows that G4 growth is not only above the recent historical trend average, but also above long-term forecasts produced by the IMF, OECD, World Bank and PwC. Meanwhile, we see a more mixed picture for EMs; while current growth is significantly below the recent trend average, other forecasts for long-term growth are relatively close to current levels.

Figure 3: Real GDP growth forecasts

Sources: Record, IMF, OECD, World Bank, PwC, Bloomberg. Data to Q3 2017. *Emerging Markets include Brazil, Chile, China, Colombia, Czech Rep., Hungary, Indonesia, Mexico, Peru, Philippines, Romania, Russia, South Africa, South Korea, Taiwan, Thailand and Turkey.

So, while it is never possible to pinpoint the exact stage of the cycle, the analysis suggests a good possibility that EM is in the early stages of a new cycle. And, one way or another, EM is unlikely to be as late stage as G4. Cumulative productivity growth differentials between our EM universe and G4 basket are forecasted to remain strong over the medium term (Figure 4). And an unprecedented divergence in Manufacturing PMIs (Purchasing Manager Index, Figure 5) suggests further potential for EM upside, as recent DM strength eventually feeds through to EMs. Looking ahead, plenty of growth potential remains, both for EM economies and for EM currency investors.

Figure 4: Productivity growth differentials forecasted to remain strong

Sources: Record, Bloomberg, IMF. Data as at October 2017.

Figure 5: DM activity acceleration could feed further through to EM

Sources: Record, Goldman Sachs, Bloomberg. Data to December 2017.



[2] In statistics, a z-score (also known as standard score) measures the number of standard deviations by which one data point deviates from the mean of a series. In this case, our z-scores measure the standard deviations by which our log series deviate from their 2000Q1-2017Q3 trends.

The dilemmas of Pravin Gordhan

  • On 22nd February, Finance minister Gordhan presented his annual budget to the national assembly.
  • Gordhan faced a painful trade-off between managing South Africa’s eye-watering debt situation, supporting stagnant private consumption and political sustainability in the most unequal country in the world
  • We simulate South Africa’s debt/GDP path under different assumptions, and argue that the economy still has a long way to go to achieve fiscal sustainability

On 22nd February, South African finance minister Gordhan presented his annual budget to the National Congress. In a budget hailed as “pro-poor but not populist”, Gordhan attempted to get a grip on the public finances through a combination of additional “sin taxes”, taxes on fuel, and a rise in the top marginal rate of tax to 45%. This allowed for increases in social grants and targeted support for South Africans seeking to buy a home.

Gordhan’s preference for balancing the budget through taxes on the wealthy, and maintaining social transfers, is understandable. In the last year that the World Bank published data for South Africa (2011), the country had a Gini coefficient of 63.4, making it the most unequal economy in the world. There are also pragmatic reasons for wanting to avoid hitting poorer consumers, too: South Africa’s painfully high 27.1% unemployment rate, low and falling levels of consumer confidence and declining retail sales (-2.3% MoM for December) mean that the economy can ill-afford further hits to private consumption. An environment of rising global yields leaves the SARB little room for offsetting monetary easing.

In his speech, Gordhan was cognisant of the tension between key goals: attaining fiscal sustainability, reducing inequality, and achieving growth. These three are not necessarily in conflict (higher growth would be a boon to South Africa’s public finances) but the need to avoid squeezing consumers too hard has certainly placed constraints on fiscal tightening. So how much more is needed?

To assess this, we use the following identity.(1) Define real GDP growth g(y), the real interest rate on government debt r, the deficit as a percentage of GDP d, and the debt-GDP ratio b. Then:


Clearly, prior to the budget, the public finances were in a perilous situation. In Figure 1 the “pre-budget” projection assumes that the deficit remains at 3.01% of GDP (latest print), with growth at 1.51% (a five-year average) and the real interest rate at 2.22%. The real interest rate estimate is based on the assumption that the nominal cost of government borrowing is equal to the 10 year average of 10 year government bond yields, adjusted for a 6% rate of inflation (the upper end of the SARB’s range).

So how has the budget changed things? To answer this, we input the projections for the deficit and growth from Gordhan’s speech. These are assumed to then remain constant at the final value outside the forecast period. (This is labelled “post-budget” on the graph). We can see that there is a marginal improvement in the debt profile of South Africa, but that much more remains to be done.

Finally, we examine a scenario in which the government is successful in establishing a steady path. A crucial insight is that, referring to our identity, if the real interest rate is higher than the growth rate, then South Africa needs to run a surplus in order to prevent an explosion of the debt-GDP ratio over time. We simulate a scenario in which Gordhan’s growth assumptions hold but he is successful in fully closing the deficit. This does indeed lead to a much more stable path of the Debt-GDP ratio. Note that this is still increasing, however, because real rates outpace growth. South Africa in essence can barely sustain a deficit without dangerously explosive debt dynamics.

Eliminating the deficit is a tall order, however. Can South Africa achieve a full closing of the deficit without endangering growth (and hence potentially worsening its debt dynamics)? Naturally, this is an unknown risk, and one Gordhan has, perhaps understandably, declined to take in this budget. However, for now sustainable public finances have not been met. The finance minister’s position is not one to envy.

Source: SARB, South African Treasury, IMF WEO, Macrobond, Record. The dotted black line shows the historical evolution of the debt-GDP ratio from 2007. Projections are as described in the text.(1) This is derived as follows: define b=B/(yp) where B is nominal outstanding debt, y is real GDP and p is the price level. Then where D is the nominal deficit, i the nominal interest rate paid on government debt, and Pi inflation:


The first is an assumption about how the nominal debt stock evolves; the second is derived from differentiation. It follows that:

This is rearranged to make the identity where r is the nominal rate adjusted for inflation


A framework for differentiating among Emerging Market currencies

  • How to rank the relative attractiveness of Emerging Market currencies? In this blog post we bring together various metrics that should help investors decide on the perennial question, whether or not to hedge Emerging Market currency risk.

Where currencies in growth economies able to deliver a structural appreciation for those who own them while at the same time as posing a currency risk, a key question is how to best to go about deciding whether to hedge that currency risk, if at all? Emerging Market currencies, through their cyclicality as well as long term trends, have always posed this perennial dilemma for investors.

The below table outlines a framework for considering hedging decisions in high growth / convergence economies where there is likely to be a punitive combination of a long term expected return from holding unhedged currency positions and relatively high currency hedging costs. Because of this, any hedging decision must be evaluated from multiple angles.

We begin by laying out and categorising the key criteria that we believe should be considered in any such currency hedging decision. These are:

  • The long-term appreciation prospects for the currency, here defined as a combination of the real interest rate differential and the level of undervaluation of that currency (vs US Dollar).
  • The short-term market sentiment for the currency, as measured by a (FX spot) momentum indicator.
  • The portfolio benefits in terms of a lower volatility return stream resulting from hedging, in this case, EM equity exposures (relative to the unhedged return stream)
  • The all in cost of hedging, which includes the economic (carry) cost as well as the trading (execution) cost.

Depending on which of these factors has a greater relevance for the investor, different rankings will emerge.

In the below table we take a transparent approach by combining these four decision criteria in an equally weighted fashion to arrive at an overall hedging attractiveness rank as of December 2016.

The overall framework does suggest that, for example, under these metrics, it is less punitive to hedge Korean Won exposures than Peruvian Nuevo Sol exposures, from the perspective of a US investor. There is no single, or indeed final answer to this question, but the practice of differentiating among EM currencies is one that is gaining more credence as these markets mature and become more differentiated themselves.


Real interest rates differentials are nominal rate differentials adjusted by the most recent YoY CPI numbers.

Undervaluation versus USD uses Record the Fair Value Model, which is based on econometric regressions using long term datasets.

The momentum signal uses three moving average pairs (10d-240d, 20d-120d, 10d-60d). Where all three moving averages agree on the direction of momentum, the currency is assigned to “weakening” or “strengthening”. Otherwise, it is considered to have “no momentum”.

Normalised volatility reduction from hedging is the difference in annualised volatility between the MSCI local return and the MSCI return (hedged to dollars), normalised by dividing by the unhedged volatility.

Annualised cost of hedging comprises the nominal interest rate differential, and indicative the bid/offer spreads (August 2016). This is except for MYR, which uses the Dec-2014 estimate, increased by the average percentage increase in spread across other EM pairs over the period.

All signals except momentum are normalised as follows: we transform each signal by adding the absolute of the smallest value in the universe to each. This ensures that all signal values are weakly positive. They are then normalised by dividing by largest value in the universe. For momentum, the normalisation is by assigning the value 0 to a moving average which suggests the EM currency is weakening against the dollar and 1 otherwise. This is then summed across the three moving averages and divided by three for each EM currency. The overall score for a currency is then this result divided by 2.

The overall rank is the rank of the currency by the average across these signals.

Source: IMF, Bloomberg, Macrobond, Reuters W/M.