Americas > South America > Argentina > A vulnerability index for Latin American currencies

Argentina: A vulnerability index for Latin American currencies

2015/10/03

 

  • Latin America currencies have weakened substantially against the US dollar during 2015.
  • While for some nations domestic (political and economic) factors have played their part, it has largely been external forces driving the selling.
  • The prospect of a Fed hike, fallen commodity prices and weaker world request in general weigh on Latin America currency valuations.
  • Latin Am currencies are most vulnerable to external forces from an interregional comparison, with the Brazilian real standing out.

Currency vulnerability has always been a hot topic of conversation at the same time as looking at economies or financial markets, one that economies have faced over a lot of generations and that remains highly pertinent today. Currencies are at the mercy of both domestic and international developments in a zero sum game, with world winners and losers. This has been a particularly significant subject of late given the sizeable moves seen across the foreign exchange (FX) market as currency wars intensify and nations attempt to competitively devalue their own currencies at the expense of others. So why has this become such an significant theme recently? In a world struggling to grow with a backdrop of low inflation, individual nations need each boost they can get. This is particularly authentic for those who have by presently exhausted stimulus from a monetary policy rate perspective.[1] That said, although a weaker currency is generally viewed as desirable in the current environment, it is as well authentic that no central bank wants to see disorderly moves. The additional vulnerable a currency is, the additional likely we are to see extreme moves. FX vulnerability has been a particularly hot topic of late as markets assess the likely impact of rising US rates on other currencies, particularly those in the emerging market world. Indeed, this topic has continued to dominate headlines since former Fed chairman Bernanke induced the ‘Taper Tantrum’ in 2013.

For Latin America, this is a particularly poignant topic given recent price action. If we look at Latin American currency performance so far this year we see double-digit depreciation against the US dollar (USD) in almost each country. For example, on a spot return basis (not inclunding the returns completed from the interest rate differential between the two currencies in an FX cross), we see that the Brazilian real (BRL) and Colombian peso (COP) are over 25% weaker while the Chilean (CLP) and Mexican peso (MXN) are nearly 14% weaker. While these moves bode well for exports and for those nations facing low inflationary pressures, the speed of the moves has been uncomfortable for central banks. Indeed, FX volatility has risen markedly of late and some central banks have been employing stability mechanisms aimed at engineering a additional orderly depreciation. For example, the Central Bank of Mexico (Banxico) auctions USD 200mn if USD/MXN moves additional than 1% away from the previous day’s fix. One thing we should note, however, is that although central banks can try and dampen volatility, they are generally not able to turn a depreciation trend into an appreciation. China may be one exception to that policy given the extreme size of international reserves it holds (USD 3.65trn).

What has triggered the Latin America (and broader emerging markets (EM)) sell-off? For some nations it has been domestic factors, such as BRL, where political woes and a deteriorating macro backdrop have weighed, but largely it has been external forces driving the selling. There is the pending Fed rate hike which is assumed to weigh on EM FX as interest rate differentials to the US narrow, making outflows a risk. Falling commodity prices have weighed heavily, with CLP suffering on the back of falling copper prices and COP on the back of cheaper oil. Of course, falling commodity prices are as well a symptom of slowing world increase and weakness in Chinese activity is expected to weigh on EM currencies across the board.

In this chapter we take a additional detailed look at FX vulnerability, using two different methodologies to track this across Latin American currencies.

A vulnerability index for Latin American currencies

Our initial methodology involves the creation of a simple but clear vulnerability heat map. In this exercise we only look at tradeable currencies within Latin America, giving us a total of
nine currencies to be included in the heat map. We selected seven key variables (in brackets is the short-hand coding used in the tables below) which are all widely touted as affecting currency vulnerability, these being:

  1. Market beta (‘Correlation’), which is an extra way of saying how closely linked a currency is to broad-based, general market moves. We proxy this by looking at the correlation of each currency to the VIX index (a tradeable index which reflects a market estimate of next volatility of the S&P US equity index – this is known colloquially as the ‘fear’ or ‘risk’ index).
  2. The current-account balance to GDP ratio (‘CA/GDP’).
  3. The budget balance to GDP ratio (‘BB/GDP’), which we use as a proxy for fiscal strength.
  4. Consumer price inflation (‘CPI’), which we use as a measure of the store of price and central bank credibility.
  5. International reserves as a share of GDP (‘IR/GDP’), which we use to show the all of ‘firepower’ a central bank has to provide support for its currency.
  6. External deficit to GDP (‘ED/GDP’), the additional external deficit a country has the higher its servicing cost at the same time as the domestic currency depreciates.
  7. The public-deficit-to-GDP ratio (‘PD/GBP’).

Table 1: Currency vulnerability heat map 2014

Table 1: Currency vulnerability heat map 2014

The values for each country can be found in table 1 below. By the end of last year it turns out the Brazilian real (BRL) was the majority exposed currency in our sample despite its relatively strong standing in IR/GDP and ED/GDP. Moreover, the Mexican peso (MXN) took the second place despite relatively strong macro policies as gleaned from its current-account position and inflation performance. Intuitively, this is what we would expect from experience given the way we see these currencies trade. Readers may well presently be asking, why is MXN high beta? MXN has a particularly high beta nature (i.e. highly correlated to broad-based market moves) because it is the majority liquid and only fully deliverable and convertible currency that is traded 24 hours a day in Latin America. This means that at the same time as there is regional or world stress, a lot of market players use MXN as a proxy for the region as a whole.

In order to check the robustness of our methodology, we looked at the average of historical heat maps for the nine nations from 2001 to 2014, giving us 126 observations. To that end, we normalised each variable by subtracting the average of the country sample and dividing by its standard deviation, thus creating z-scores for each currency. Some of the seven variables have inverse implications for vulnerability, by which we mean that a high CA/GDP and a high IR/GDP make a currency less vulnerable while high values for all the other variables mean a additional vulnerable currency. Therefore, we simply multiply CA/GDP and IR/GDP by -1 before calculating the z-scores.

Figure 1: Vulnerability Rank vs Devaluation Rank


By construction, therefore, positive figures in the table reflect that the country is in a worse position against the average. Our variable selection limits the heat map to a yearly frequency. The results for the normalised heat map for 2014 are presented in the annex, table 1.

We again compared the vulnerability rankings and price action for each currency during the same year. Our findings show that – although additional factors impinge on price action in any given time– there is a positive relationship between our vulnerability rankings and currency performance, with additional vulnerable currencies tending to depreciate additional (figure 1).

We have found a positive correlation between the vulnerability rank and the devaluation rank. Intuitively this makes sense, but it is interesting to see these results given that FX markets are seemingly driven by an almost unlimited number of potential factors.

In order to see how Latin America in general fits into the world picture with respect to vulnerability, we as well created a World Vulnerability Framework using a similar methodology as our Latin American heat map (the same except we excluded the correlation to the VIX index, mainly because we are additional interested in structural variables). It is significant to note that adding new countries/regions to the sample will change all z-scores. As the table below shows, Europe Middle East and Africa (EMEA) is broadly on a par with Asia, while Latin America is seen as additional vulnerable. This is far from perfect in way of comparison as there are factors outside of the chosen parameters that make a currency additional or less vulnerable in the current environment. For example, given the slowdown in China, Latin America currencies are likely additional vulnerable than EMEA currencies as trade ties between China and the Latin America region are far better than between China and Eastern Europe. It is as well the case that the denomination of external deficit is significant. In EMEA external deficit tends to be additional likely denominated in EUR, while in Latin America it tends to be USD denominated, making the Latin America region additional vulnerable to USD appreciation. But as a rough guide we acknowledge with the results that Asia is likely the least vulnerable region, followed by EMEA and again Latin America.

Table 2: Interregional comparison of currency vulnerability 2014

Table 2: Interregional comparison of currency vulnerability 2014

Pressure gauge

In order to look at Latin America currencies from an extra perspective, we created an index to gauge pressure on a sample of currencies. We define the sample using monthly averages of USD/Latin America exchange rates for the same currencies we used in the Vulnerability Index. This second methodology is based on the idea that there are some key (additional) high-frequency data in financial markets that drive in general shifts in FX as well and some of these are particularly significant for Latin America currencies.

We have chosen five series that help picture investor sentiment towards emerging markets (EM) or developed markets (DM):

The DXY USD index (a measure of USD strength relative to 57.6% EUR, 13.6% JPY, 11.9% GBP, 9.1% CAD, 4.2% SEK and 3.6% CHF) – if the USD is appreciating against these currencies again it is likely to be appreciating against EM currencies as well.
VIX index – higher volatility often results in EM FX depreciation as investors seek safe havens, while carry trades become less attractive.
Yields on 10-year US Treasury bonds – EM currencies often depreciate at the same time as US rates rise as the interest rate differential becomes less favourable.
The Commodity Research Bureau (CRB) raw materials index – at the same time as commodities prices are falling (so we use the negative of CRB RM, ‘-CRB RM’) EM currencies are expected approaching under pressure as a lot of EM nations are commodity exporters.
MSCI EM stock market index – if EM stock markets are falling we would expect EM currencies to find themselves under pressure (again, we use the negative MSCI EM, ‘-MSCI EM’).

We ran a Principal Component Analysis (PCA) framework for the period 2009 to 2014, using normalised versions of each series next applying a smoothing technique.[2] As a result of the PCA, we created an index we call the Latin America FX Pressure Gauge (LFPG). The logic behind this series is that at the same time as the LFPG is running above its trend (i.e. LFPG >0), FX Latin America currency is under pressure and is expected to be running above its trend. Again, we applied the same smoothing technique to each currency in order to ensure stationarity in the analysis. We again ran a regression between the normalised smoothened currency against our LFPG series, using the same timeframe (2009-2014).

Our major results are shown in table 3 below.

Table 3. Pressure gauge results for currency vulnerability

Table 3. Pressure gauge results for currency vulnerability-globserver

A look at the table above reveals some very clear results. During the 2009 to 2014 period, BRL reacted additional aggressively to shifts in median investor sentiment with a beta equal to 0.56. CLP shows a similar picture with a beta of 0.53. On the other hand, the GTQ shows an insignificant beta (note that a lack of liquidity in GTQ makes conclusions less robust). Argentina has a small beta, but constraints on default situation seem to weigh on investors’ appetite for Argentina for a period much longer than from 2009 to 2014.

Conclusions

In summary, Latin America currencies have suffered heavy losses of late but given the world environment, inclunding some specific domestic woes, this theme is unlikely to change in the near term. Of course, periods of correction can be expected but these are likely to offer better levels for re-instating Latin America shorts rather than signalling a reversal of fortunes. Given this view, we look to the findings in our Vulnerability Heat Map and LFPG to help us differentiate between individual Latin America currencies and, as such, we remain bearish on BRL.

Footnotes

Table 1: Normalized currency vulnerability heat map 2014

Table 1: Normalized currency vulnerability heat map 2014

[1] As an example, we believe one of the primary reasons for the ECB announcing its QE plan in the eurozone was to try and weaken EUR in order to import higher inflation and in part to boost exports.

[2] We ran a Hodrick-Prescott filter to each series using different lambda values. Initial, we chose a small price for lambda (10) in order to clean random shocks from each series. Next, we ran the filter with a high lambda (14,400) in order to extract the trend of the series. We again calculated to proportional deviation between the filtered series and its trend.
Annex

@rabobank.com/economics

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