Does Misaligned Currency Affect Economic Growth? – Evidence from Croatia
u1459284
1. Contagion or Not? Russia impacts all.
Esha Jiwanmall
September 7, 2015
Abstract
Contagion has been defined as a significant increase in market linkages after a shock to
an individual country. This paper tests for the existence of contagion following the Russian
currency crisis (2014 - present) in the CIS countries. In order to do this, I have used the
adjusted correlation test suggested by Forbes and Rigobon (2002) along with real world news.
However, I contest their findings by showing that contagion does in fact exist in some countries
even after correction for market volatility bias. I further hypothesize that even a decrease in
the co-movement of markets can be interpreted as contagion (reverse contagion). Possible
mechanism of crisis transmission through remittances is modelled to explain the presence of
contagion in Armenia and Tajikistan.
Keywords: Contagion, Currency crisis, Russia, Exchange rate
1
3. 1 Introduction
While Russia tries to work its way out of the ongoing currency crisis, the countries surrounding
it are trying to protect themselves from facing one. It is not purely based on the geographical
situation of these countries that makes them vulnerable to the Russian crisis, but also established
trade, historical and institutional linkages. These linkages are particularly scrutinized during
periods of crisis, but this does not mean that they do not exist during periods of stability as well.
However, during a crisis, relationships between countries tend to be amplified or could even
be broken. This magnification or diminution of relationships between countries during periods of
crisis as compared to periods of stability is known as contagion (Dungey et al., 2010). This paper
not only looks at contagion in its traditional form of an increase in relationships during times of
crisis, but also at reverse contagion, wherein relationships between markets have reduced.
For the purpose of this paper, the countries that I will analyse for the presence of contagion are
the Commonwealth of Independent States (CIS) namely Belarus, Armenia, Kazakhstan, Kyrgyzs-
tan, Tajikistan, Azerbaijan, Uzbekistan and Georgia, a former member state. It is highly probable
that if there occurs a boom or crisis in one of these countries, then the others in the cohort will
be affected too. Hence, this transmission of shocks from the Russian market to the CIS markets
is the contagion that I will be analysing.
The paper is organised as follows. Section 2 will briefly look into the source of contagion,
that is, the currency crisis in Russia. The next section will elucidate what the different paths
of transmission of contagion are. Section 4 will briefly look into previous empirical literature and
Section 5 explains the methodology that I have used. Section 6 analyses if contagion actually exists
in the CIS countries, and if so, then what caused it. Section 7 brings together the various countries
to obtain a complete picture of the contagion affected states and Section 8 briefly concludes.
Appendix A provides a list of the significant events that took place in Russia from 2014 to 2015.
2 What caused the crisis in Russia?
Figure 1: Relationship between oil prices and exchange rate
3
4. If we take a look at the plot of the USD/RUB exchange rate against oil prices (Figure 1), it is
easy to notice that there is a clear, negative relationship between the two. This is quite logical as
the energy sector constitutes 63.8% of Russia’s exports and 48% of the federal budget reserve is
financed by the mineral extraction taxes and export customs duties on oil and natural gas. Since
the country is so heavily dependent on one commodity for revenue, when the price of it falls, the
currency slumps in the foreign exchange market and thus, devalues.
I investigate this relationship more rigorously by using a linear regression analysis. A monthly
average exchange rate (USD/RUB) from January 2011 to June 2015 is used as the dependent
variable. Oil prices (dollar per barrel), a dummy variable for the crisis and their interaction are
used as the three independent variables. Estimation results are presented in Table 1. The negative
coefficient for oil prices shows that a one dollar per barrel decrease on average causes a 0.133
increase in the exchange rate number during non-crisis periods 1
.
Table 1: Estimation results
Variable Coefficient (Std. Err.)
Oil −0.133∗∗
(0.054)
Crisis 39.099∗
(6.277)
Oil X Crisis −0.334∗
(0.059)
Intercept 45.626∗
(5.970)
* p < .001, ** p < .05
Not surprisingly, there is a strong negative effect of oil prices on the exchange rate, which
demonstrates heavy dependency of the Russian economy on the current state of the oil market.
However, significant interaction between oil prices and the crisis variable also tells us that this effect
strengthened after the Western powers of the world started alienating Russia for its intervention
in Ukraine, by imposing sanctions. It looks to be that at this point, investors started worrying
that the government would base their decisions on political tactic or Vladimir Putin’s ego instead
of sound economic reasons.
2.1 One way option model
So why should oil prices have a greater effect on the economy during times of political unrest
than when not? One of the possible explanations for this could be because of investor speculation.
When oil prices fall, and there is also political instability in the country, investors start speculating
against the Government, even if there is no reason to believe that the exchange rate will fall. This
speculation enables the fall in oil prices to have a greater impact on the economy, as it is already
weak. This negative conjecture is usually caused by a lack of information on the part of the
investors about how much of the reserves the Government will use in order to defend the exchange
rate (Krugman, 1979). If we assume that these transactions changing domestic for foreign currency
are costless, then it is maximising utility for investors to speculate against the Government.
We will define the one-way option model developed by Krugman (1979) to see how protecting a
fixed exchange rate, eventually leads to a crisis caused by speculation. Lets assume that a country
X has a reserve, from which the first part R1 is public information, known to be used to defend
the currency, and the second part R2 might be used with very low probability. When R1 starts
1
An increase in the exchange rate number leads to a fall in the value of the exchange rate or a depreciation, and
a decrease in the exchange rate number causes an increase in the value or an appreciation.
4
5. declining, at one point investors will start speculating and will use up the balance of R1, leading
to a crisis, as they do not know if R2 will be used. They will benefit from holding foreign over
domestic currency for the waiting period, to see if the Government will commit to using the rest of
its reserves. If R2 is then put into play, consumers will buy back domestic currency and there will
be a recovery of confidence. However, if they choose not to, investors will panic and there will be
volatility in the markets. In the case of Russia, on the 10th of November 2014, Russia abolished its
dual currency basket rouble value system (fixed exchange rate), for a floating exchange rate and
the economy entered a full-blown crisis (Central Bank of Russia, 2014). This is because investors
now had public information that the Government would not use its R2 to defend the exchange
rate, and so increasingly exchanged roubles for dollars or euros.
Figure 2: Dollar purchase and sale transactions with individuals by authorized banks (mil $)
By looking at the volume of operations involving domestic for foreign transactions, we can
perceive the amount of speculation in the period. During normal times, customers and banks
still exchange currency, but Figure 2 shows an obvious jump in the amount of dollars bought by
individuals from the bank during the months of October to December 2014, showing speculative
tendencies. Thus, during periods of crisis, speculative pressures can cause negatives factors, like
falling oil prices to have greater impacts on the exchange rate.
3 Transmission mechanism of contagion
In general, contagion has been defined as the transmission of shocks from one country to another.
But in order to understand how these shocks transfer themselves from point A to point B, we
need to look at the transmission mechanism of contagion. I find that the explanation given by
5
6. Allen and Gale (1998) to be the most credible and suitable for the purpose of this paper. They
have stated three possible kinds of contagion, namely Real Contagion, Financial Contagion, and
Informational Contagion.
Real contagion depends on heavy trade relationships between countries, and occurs because
countries in the trade market follow their competitors to stay profitable. This means that if one
country devalues its currency because of its own problems, then the other countries will be forced
to follow suit. In the case of Russia, it happens to be the major exporter or importer for most of
the neighbouring countries, and so when the rouble devalued, the others found that their export
prices were now less attractive and imports increased. This would naturally cause an imbalance
of trade, moving the currency to another higher equilibrium (depreciation).
The second form of contagion is a financial one, which is typically known as a ’run on the
banks’ phenomena, but can be extended beyond banks to a region, country or currency. When a
bank in region A faces liquidity problems it normally turns to banks in region B for help in the
form of loans. But when there is a global shortage of liquidity, then the bank in region A cannot
turn to the bank in region B for help and is forced to liquidate its assets, and incurs losses. This
turns into a problem for the bank in region B if it happens to have claims in the bank in region
A, which also triggers a loss, due to the fall in value of assets in A.
Correspondingly with currencies, when country B has large amounts of currency from country
A, which has now devalued, it faces a loss. This loss can be big enough to cause contagion if a
country holds a large share of the devalued currency, and if the scale of devaluation is large enough.
Thus, the initial problem faced by one country can cause a run not only on its own currency, but
also on others, if the density of financial links is large enough. This form of contagion has become
a possibility owing to the interdependence of financial systems.
The third form of contagion, known as Information Contagion or the ’wake-up call’ phe-
nomenon, occurs when an underlying problem in one country comes to the forefront when another
country faces a crisis, thereby providing a signal or a wake-up call. This usually takes place when
there is some similarity between two economies be it financial or even cultural. If two countries
share similar policies or fiscal conditions like debt or unemployment, investors may assume that
these two countries are similar and hence will face the same problems.
It could occur due to cultural similarities, as in the example of the Mexican and Argentinian
peso crisis. Even though they shared low trade linkages, and Mexico faced slow growth problems
and Argentina faced unemployment, their mutual Latin culture made them similar in this weak
sense. Investors in Argentina had imperfect knowledge of the actual cause of the crisis in Mexico
and so went after their own peso assuming that they were next to face devaluation. ”Once investors
have seen one country with that cultural background abandon its peg under pressure, they may
revise downward their estimate of the willingness of other such countries to defend their parities”
(Krugman, 1999).
The correlation between two economies need not be perfect for investors to be irrational and
speculate against their currency. This is also known as a self-fulfilling prophecy, for when there is a
run on one currency and investors in another country hypothesize that they share some correlation
with the country of that currency, they will make a run on their own. Thus, simultaneous financial
crises can also occur because international investors believe that a particular currency or stock
market will be the next victim of a simultaneous speculative attack.
6
7. 4 Previous empirical literature
When one reads the Bloomberg or International Business Times, it is interpreted that the CIS coun-
tries have been significantly affected by the ongoing crisis in Russia, to varying degrees (Michael,
2014). This effect has been loosely termed contagion in almost every magazine and newspaper
article. However, without due analysis of data it cannot be said with certainty that contagion
exists.
One of the earlier papers that analysed why a number of stock markets crashed in October 1987
even though they had different economic circumstances, was written by King and Wadhwani (1990).
They believed that when agents use price changes in other markets as a basis for information on
markets in general, it can lead to contagion. As specified in Section 3, when an error occurs in one
market, it can provide a wake-up call to other markets. However, their paper was limited owing
to the fact that they only admitted to correlation coefficients being affected by volatility, but did
not find a correction for it.
In 2002, Kristin J. Forbes and Roberto Rigobon, in their paper, ’No Contagion, Only Inter-
dependence: Measuring Stock Market Comovements’ defined contagion as a ”significant increase
in cross-market linkages after a shock to one country or group of countries”. This means that
when the level of co-movement between two stock markets increases during periods of crises, as
compared to periods of stability, this increased dependency is called contagion. Simple correla-
tion coefficients were used as a measure of co-movement between countries. Nonetheless, Forbes
and Rigobon (2002) also noticed that correlation coefficients are biased upwards due to increased
market volatility during periods of crisis. So they introduced a simple correction of the correlation
coefficient (see Section 5) that thereby eliminates this bias. They found in their case studies that
there was practically no trace of contagion after doing so.
Later on it was pointed out by Martin and Tang (2006), that there was a bias towards showing
no contagion in Forbes and Rigobon (2002), as they defined the non-crisis period as the total sample
period (non-crisis + crisis period). This definition of non-crisis period introduces a covariance
between the correlation coefficients from the overlapping sample periods. Dungey et al. (2010)
suggest an alternative test that distinguishes between stable and crisis periods.
Instead of studying the correlation between stock market movements, I will perform the same
analysis on the currency markets, as I want to test if the currency crisis in Russia has spread to the
CIS countries. I will calculate correlation coefficients between exchange rates during non-crisis and
crisis periods. But I will define my crisis period following the definition in Dungey et al. (2010).
Forbes and Rigobon (2002) correction will be used to control for biases, and a t-test will be carried
out to check significance of change in correlation.
5 Data and methodology
I have conducted my analysis on the following seven countries: Belarus, Georgia, Kazakhstan,
Armenia, Kyrgyzstan, Tajikistan and Azerbaijan. All necessary data on exchange rates has been
obtained from the respective Central Bank websites 2
. Uzbekistan has been eliminated from the
group of CIS countries owing to the fact that their data on exchange rates was published only
weekly, causing difficulties in terms of analysis.
On the 21st of March 2014, Crimea was formally annexed by Russia. This was regarded as an
interference in Ukraine’s sovereignty as a country by the Western powers of the world. Starting
2
Marginal differences in data due to distinct bank holidays in different countries.
7
8. with its suspension from the G8, Russia found itself imposed by sanctions from various different
countries including the United States, EU, Australia, and Canada. It retaliated by placing counter
sanctions in the form of a ban on food imports on the 6th of August 2014 3
. The rouble fluctuated
between the 34-36 USD/RUB band during the year, but it was only on the 18th of August 2014
that the rouble did not climb back down and escalated to see a 37.29 USD/RUB exchange rate
on the 2nd of September 2014 (Figure 3). Thus, I have defined the crisis period as 18/08/2014
to 13/02/2015 4
and chosen a relatively stable period for the exchange rate from 11/01/2012 to
17/08/2014 as the pre-crisis period 5
.
Figure 3: USD/RUB Exchange Rate
In my paper, Russia will be country X, and the affected countries will be country Y. Volatility
is typically higher during crisis periods and lower during the pre-crisis period. Due to the con-
ditionality of the correlation coefficients on market volatility, we get higher values in general for
the correlation coefficients in the crisis period. To adjust for this upwards bias, I will be using the
adjusted correlation coefficient (Forbes and Rigobon, 2002) as follows:
va =
ρa
1 + (1 − ρ2
a)(
σ2
a,X −σ2
b,X
σ2
b,X
)
where:
ρa - the correlation between the currency markets in X and Y during the crisis period;
σ2
a,X - the variance of exchange rate in country X (crisis period)
σ2
b,X - the variance of exchange rate in country X (pre-crisis period)
The correlation between the currency markets in X and Y during the pre-crisis period is denoted
as ρb. va is thus a decreasing function of change in variance. For example, if variance was equal
3
A total overview of major events in Russia will be found in Appendix A.
4
If we extend the crisis period up to June 2015, the results would be misleading as there was a recovery of the
rouble from Feb-May.
5
Terms pre-crisis period, stable period and non-crisis period have been used interchangeably.
8
9. before and during the crisis period (σ2
a,1 = σ2
b,1), then the denominator would equal to 1, and the
adjusted correlation would in fact be equal to the correlation coefficient during the crisis period.
Once adjusted, in order to check for contagion, I test
H0 : va = ρb
versus
H1 : va = ρb
If the null hypothesis is true, then there does not exist contagion. If the alternative hypothesis
is true, then there exists contagion, in the context of the definition in this paper.
We then perform a t-test to check for the significance of difference between pre-crisis period
and adjusted crisis period correlations. T-statistics is calculated as follows:
ˆva − ˆρb
1
Ta
+ 1
Tb
where:
Ta - sample size of high volatility period
Tb - sample size of low volatility period
5.1 Limitations
This test for contagion, however, has certain limitations:
1. If stable and crisis periods are chosen differently, results could vary. This might lead to a
period selection bias.
2. This analysis assumes that there is an absence of endogeneity and omitted variables (Forbes
and Rigobon, 2002).
I have not considered the effect of these limitations in my study. In addition, the suggested
correction as we will notice later, may not show the best results for countries that have initial high
correlation.
6 Analysis
On preliminary analysis of correlation (Table 2) between the exchange rate of rouble and other
currencies, five of the countries show traces of contagion through increased correlation during the
crisis period, namely Georgia, Armenia, Kyrgyzstan, Tajikistan and Azerbaijan. Belarus shows
relatively steady correlation through both the stable and crisis periods, but we will still analyse it
further. Kazakhstan does not fit the pre-condition for contagion, as it shows reduced correlation
even before adjustment and so is dropped from further analysis 6
.
After adjusting for heteroscedasticity using the procedure described in Section 5, only three of
the five countries still show an increased correlation, being Armenia, Tajikistan and Azerbaijan
while for Georgia the increased correlation disappears, leaving only an interdependence of currency
6
I will explain in Section 6.3 why an analysis of Kazakhstan would be misleading.
9
10. Belarus Georgia Kazakhstan Armenia Kyrgyzstan Tajikistan Azerbaijan
Stable 0.86 0.78 0.82 0.54 0.88 0.67 -0.63
Crisis 0.86 0.95 0.70 0.94 0.92 0.97 0.46
Total 0.94 0.87 0.75 0.93 0.93 0.80 0.67
Adjusted 0.55 0.78 – 0.76 0.69 0.86 0.21
T-statistics -3.09 0.00 – 2.03 -1.92 1.71 7.39
Theoretical value 1.65 1.65 – 1.65 1.65 1.65 1.65
Significant difference 1 0 – 1 1 1 1
Contagion RC NC – C RC C C
C = Contagion
RC = Reverse Contagion
NC = No Contagion
Table 2: Results of Contagion Analysis
markets. For Kyrgyzstan as well as Belarus there is a decreased correlation after adjustment.
This result also shows that there does exist heteroscedasticity due to dependency of correlation
coefficients on market volatility, that causes an upwards bias which is eliminated after adjustment.
Lastly, to ensure that the increased or decreased correlation is in fact consequential, we perform
a t-test. If the difference between stable and crisis correlation coefficients is statistically significant,
I conclude that they end in contagion or reverse contagion.
6.1 Armenia and Tajikistan
Figure 4: Remittances from Russia and Total
Armenia and Tajikistan were two of the worst affected countries from the CIS group during the
Russian financial crisis (2014-present). I hypothesize that the main reason for devaluation in these
10
11. countries was the level of importance that remittances play in their economies, and that Russia
is the largest source of these remittances. In Armenia, remittances are equivalent to 18% of the
GDP, and 75% of remittances flow in from Russia (Figure 4). Whereas, 52% of Tajikistan’s GDP
is procured from remittances, almost the whole of which is from Russia (Hille, 2015). Close to half
of the working males in Tajikistan migrate to Russia for better job opportunities.
This hypothesis is backed by the 2006 IMF Armenia Report on the size and impact of remit-
tances on macroeconomic variables in Armenia, which shows that there exists a negative correlation
between the volume of remittances that flow into Armenia and its exchange rate (USD/AMD)
(International Monetary Fund, 2006). They noted that a one percent increase of GDP in to-
tal remittances (personal and other remittances) accounts for a one percent appreciation in the
USD/AMD exchange rate. Therefore, this same relationship between remittances and exchange
rate should hold true even when remittances decrease which we see graphically from Figure 5 for
both the countries. However, we have to take into account the seasonal variations in productions
for countries like Tajikistan and Armenia, as it slows in the first season and then grows to reach
its peak in the third season (summer) before falling back down.
(a) Armenia (b) Tajikistan
Figure 5: Relationship between seasonally adjusted remittances and exchange rate
The high level of dependency that Armenia has established on Russia will only see an increase
due to its entry into the Eurasian Economic Union (EEU). This involvement will reduce the
relationship Armenia has with the European Union (EU), and leave them with Russia to trade with,
who is currently facing a depressing economic situation (European Council on Foreign Relations,
2015). But also being a member of the EEU will reduce the requirements for migrants to work in
Russia like work permits, documents and so forth, leading to a larger number of migrants choosing
to work in Russia. Tajikistan’s only option is to look to China, who has taken over more than half
of Tajikistan’s debt and is investing heavily in its infrastructure and oil sector. But recently the
Tajiks have voiced out their concern that the high level of dependency that their country currently
faces towards Russia will soon be replaced by a dependency on China (AFP JIJI, 2015).
6.1.1 How remittances affect the exchange rate
As seen for both the previous countries, remittances account for a big chunk of the total GDP (Fig-
ure 4). They are also a larger growing source of foreign exchange than foreign direct investments
(FDI) (The World Bank, 2014b). Thus, a decrease or increase in remittances has a big impact on
the foreign exchange rate and the trade power a country holds (Fuentesy and Herrera, 2008).
11
12. Remittances had been consistently rising in Armenia and Tajikistan over the previous years
but fell in 2014 due to the crisis in Russia. Since migrants earn in roubles, which now has lower
purchasing power with respect to the dollar they send home less in absolute dollar terms thereby
reducing the purchasing power of their domestic currency. For example, assume that the migrant
workers had been previously earning 12,000 roubles this would have been worth (12000/33) 363
dollars remitted to Armenia or Tajikistan on the 21st of January 2014. But by the end of December
it would only fetch (12000/56) 214 dollars. Thus, the amount of dollars being sent back home had
nearly halved as the rouble depreciated against the dollar.
These reduced remittances as I have claimed earlier could arguably be the main cause for the
Armenian Dram and the Tajik Somoni to devalue. The following is a simple model which explains
how changes in remittances can affect exchange rates.
An average family in Armenia and Tajikistan earns money, consumes, saves, pays taxes and
receives remittances from relatives working abroad. Thus, their wealth function (Wt) can be
calculated as the following :
Wt = Yt (1 − τ) + REMt + St−1 (1 + r) (1)
where:
Yt - earnings of the period
τ - rate of income tax
REMt - remittances received during the period
St−1 - savings of the previous period
r - interest rate
The wealth is then distributed between consumption and savings in the current period:
Wt = Ct + St (2)
Armenia and Tajikistan are characterized as countries that spend majority of their wealth on
consumption. So if wealth falls, consumption will be affected more than savings.
Consumption is characterized as a choice between traded (a) and non-traded (b) goods. Traded
goods are those that are exported and imported, whereas non-traded goods are those that are only
locally consumed.
Ct = a + b (3)
When remittances fall the wealth falls considerably, thereby reducing the purchasing power of
consumers. This lower purchasing power results in a fall in demand forcing domestic suppliers to
reduce their prices of non-traded goods (b) to remain competitive (Figure 6). Assuming that they
do not have the ability to change the prices of traded goods, as these prices are determined by
world markets.
Now that local prices have fallen and low demand subdues economic activity, the Government
tries to stimulate the economy by reducing the interest rates. This makes money easily available
to consumers and enables them to increase their purchasing power. However, lower interest rates
reduces the demand for the Armenian dram or the Tajik somoni for investment purposes and
increases demand for US dollars to get higher returns on investment. Thus, the currency depreciates
relative to others, in this case, the Armenian dram depreciated by 14.25% and the Tajik Somoni
by 7.78%, both against the dollar 7
. Thus, a decrease in remittances causes a depreciation in the
7
Any change in the relative value of currency is calculated for the period 18/08/2014 - 13/02/2015.
12
13. Figure 6: Effect of lower remittances on demand
exchange rate. What I conclude from this is that financial contagion can also occur when there
are reduced earnings in one country, and when a large number of migrants exist there, it affects
the earnings of their home country too.
6.1.2 Empirical evidence of the impact of remittances on exchange rate
In order to empirically verify that a fall in remittances did depreciate the exchange rates in Armenia
and Tajikistan, I performed the following linear regression analysis:
ExRatet = α + βExRatet−1 + γRemSharet−1 (4)
where:
ExRatet - average exchange rate in the current quarter
ExRatet−1 - average exchange rate in the previous quarter
RemSharet−1 - share of remittances from Russia in country’s quarterly GDP in the previous
quarter
If the share of remittances in the last period helps to predict the exchange rate of the next
quarter, it means that remittances are likely to have an effect on the exchange rate. Estimation
results are presented in Table 3. What we find, is that for both countries effect of remittances on
the exchange rate is negative and statistically significant.
It means that (a) a decrease in 1% of share of remittances in GDP in Armenia on average leads
to an increase in the exchange rate number by 6.834 units ceteris paribus, and (b) a decrease in
1% of share of remittances in GDP in Tajikistan on average leads to an increase in the exchange
rate number by 0.00469 units ceteris paribus.
13
14. Table 3: Effect of remittances on exchange rate
(1) (2)
Exchange rate (Armenia) Exchange rate (Tajikistan)
Exchange rate (t − 1) 1.197∗∗
1.850∗∗∗
(4.39) (29.05)
% of remittances in GDP (t − 1) -683.4∗∗∗
-0.469∗∗
(-7.06) (-3.27)
Intercept 33.69 -3.820∗∗∗
(0.28) (-10.50)
N 11 13
t statistics in parentheses
∗
p < 0.05, ∗∗
p < 0.01, ∗∗∗
p < 0.001
6.2 Azerbaijan
When the currencies of Russia, Ukraine and Turkey all depreciated, Azerbaijan had to respond by
devaluing its currency to keep its exports competitive. A devaluation would also help to reduce
imports from these countries as Azerbaijani consumers would otherwise find it cheaper to import
rather than consume local goods. Azerbaijan could be a victim of not only real contagion but also
informational contagion as the Government might have been coerced into devaluing their currency
due to the pressure of neighbouring countries devaluing theirs.
However, I hypothesize that the Government’s decision to devalue in Azerbaijan was not due to
trade relationships, but a dependence on a third party, namely oil prices. 43.3% of GDP in Azer-
baijan comes directly from export revenues and 93.4% of these exports consist of crude petroleum
and petroleum products (The World Bank, 2014a). Besides export revenues, tax revenues from
the State Oil Fund of Azerbaijan (SOFAZ) and foreign oil companies also contribute to the federal
budget. Thus, in total oil and petroleum constitute 65% of the federal budget reserve (Mirzeyev,
2015). So we can conclude that their exists a heavy dependency of Azerbaijan on prices in the
energy sector.
Even though exports in 2014 from Azerbaijan did not fall, lower prices from each barrel caused
a fall in their revenue. The Government was hit hard by this fall in income and decided to devalue
the exchange rate to save money. By doing so, lower oil prices would still lead to greater revenue:
oil prices × exchange rate:
55 × 0.78 = 42.9
55 × 1.05 = 57.75
before and after devaluation, respectively. The second main reason to devalue was to reduce
losses incurred by SOFAZ. The fund gets its revenue in US dollars but pays its expenses in Azer-
baijani manat. Thus, devaluation will lead to lower expenses in terms of dollars and thus an overall
dollar savings.
If the Government’s main reason to devalue the manat was the low oil prices, then it would
be misleading to check for the presence of contagion in Azerbaijan by solely looking at the data.
There would be no direct or indirect form of relationship between Russia and Azerbaijan, other than
14
15. their shared dependency on oil prices. Hence, based on whether we believe that the Government’s
decision to devalue was trade competency or low oil prices, we can state that there exists contagion
or not.
6.3 Kazakhstan
When the FED started using Quantitative Easing as a monetary policy instrument in order to
stimulate activity in the economy, it increased the supply of money in the market. This increase
in supply is negatively proportional to interest rates as the amount of money available is so high
that banks or lenders cannot charge very high borrowing rates. These lower interest rates act as a
deterrent for investors to put their funds in developed economies and thus channels them towards
emerging markets (EM) which will give investors greater returns. But when the idea that the FED
would start cutting back their purchases circulated in December 2013, there was volatility in the
markets and general panic (Mishra et al., 2014). This cutting back or tapering led to a reversal of
flow of capital from emerging markets back to developed economies, resulting in a huge sell off in
emerging markets. When a country loses a large proportion of foreign capital at such rapid pace,
it naturally leads to a fall in the value of their currency relative to others, or depreciation.
Kazakhstan was one of the emerging markets that faced this sell off too, which is reasoned to
be the cause for the tenge to be devalued. However, instead of the gradual depreciation in the
exchange rate like in most other EMs, the tenge was suddenly devalued in February 2014 8
. This
was because the Central Bank of Kazakhstan initially chose to defend the currency with its foreign
reserves so as to avoid depreciation. However, after a point the strength of the tenge was causing
its export sector to be less competitive and the imports were increasing beyond budget which was
hurting the economy. Hence, Mr. Nursultan Nazarbayev, President of Kazakhstan, decided to
devalue the tenge by 16% on 11/02/2014.
Figure 7: KZT/USD
He believed that it was necessary to devalue the tenge in order to (a) protect the currency
8
Devaluation is a controlled decision made by the Government to reduce the value of the exchange rate. Depre-
ciation is a fall in the value of the exchange rate due to market forces of demand and supply.
15
16. from further, sudden devaluation (especially against the sliding ruble), (b) to curb speculative
tendencies, and (c) to keep their goods competitive in the world market (Farchy and Strauss,
2014). However, exacerbated circumstances allowed the tenge to devalue suddenly for the second
time on 21/08/2015 as seen from Figure 7. This happened due to the following three reasons:
1. When a country depends on one commodity as its primary source of revenue, any movement
in the price of the commodity will cause an equal change in the funds available in the economy.
So when Kazakhstan gets 30% of its GDP from the oil industry and 64.1% of its exports
are also sourced from oil, it shows an unhealthy dependence on the energy sector (Trading
Economics, 2015). Since oil prices have stayed slumped at 45-60 dollars per barrel (Nasdaq,
2015), the country has had to cut back on expenditures to stay within the budget.
2. In 2013, Kazakhstan sent 8.4% of its exports to Russia but the falling rouble value made for
lower demand which resulted in a fall in exports in Kazakhstan. Also higher competition
from now cheaper Russian goods on the Kazakh market meant consumers in Kazakhstan
preferred to import goods from Russia over consuming locally produced goods (Snytkova,
2015). As Kazakhstan is now a member of the Eurasian Customs Union along with Russia, it
is prone to follow any monetary decisions taken by other members, such as Russia’s decision
to switch to a free floating exchange rate in 2014.
3. Then there was also the slowdown of economic growth in China, Kazakhstan’s main export
partner who devalued their yuan against the dollar (The World Bank, 2014c). The Kazakh
Government then followed suit by announcing a free floating exchange rate which allowed
the currency to depreciate by 26.5%.
In context of the preceding analysis on contagion (see Section 6), I can say that any correla-
tion between Kazakhstan and Russia during the stable or crisis period would be misinformed as
the devaluation was a deliberate move made on the part of the Government due to the US FED
tapering and not by free market forces of demand and supply. It would thus be misleading to
look into the adjusted correlation between these two countries. However, after the second devalu-
ation (21/08/2015), correlation for these countries can be henceforth calculated as (a) Kazakhstan
switched to a floating exchange rate and (b) the reason for devaluation as explained above was
also due to their relationship with Russia.
6.4 Belarus and Kyrgyzstan - Reverse contagion
Even though Kyrgyzstan could be conjectured to have contagion and Belarus showed no increased
correlation during crisis periods, they both depict a marked fall in correlation after adjustment.
As per Dungey et al. (2010), any magnification or reduction of relationships from stable to crisis
periods can be called contagion. So would this break of relationships during crisis be known as
reverse contagion? It could also be that when such high levels of relationship exist between coun-
tries during stable periods there is no room for a magnification, but only a reduction. Contagion
is defined only as a change in the relationship between the markets of countries but this does not
account for the size of the effect that a country faces due to contagion. So even though Belarus
and Kyrgyzstan are currently facing significant financial and economic problems, their relationship
with Russia does not see an increase.
In order to further analyse if Belarus and Kyrgyzstan are currently susceptible to a currency
crisis, I carried out a financial health inspection, with the help of the FSI (Financial Soundness
16
17. Indicators). These indicators are ratios given by the IMF which depict different aspects of the
health of the banking and financial system of a country. For the purpose of this paper, I have
taken into account three of the following ratios:
1. Assets which are volatile due to change in prices are known as risky assets, such as equities,
commodities, and loans. Banks should have sufficient capital to back these risky assets in
order to cover any losses. This necessity is represented by the Capital to Risk-Weighted
Asset Ratio (CRAR), wherein higher loans with higher weights will require higher capital.
As per the Basel III international regulatory banking standard, this ratio must stand at a
minimum of 8%.
Figure 8: Capital to Risk-Weighted Asset Ratio
As we can see for both Belarus and Kyrgyzstan (Figure 8), this ratio is at 13.3% and 16.2%
respectively well above the 8% mark. If either of the countries are to face a worsening
financial situation they should be able to cope with the level of capital available.
2. The second indicator that is useful to look into is the Ease of Business Index which ranks
countries based on different components like ease of starting a business, trading across bor-
ders, procuring construction permits, etc. Ranked from 1 to 189 as per World Bank Data, 1
being the most favourable position. This index not only shows the kind of financial environ-
ment existent in the country but also the level of political corruption or red-tapism present.
The lower the corruption, the higher the ease of doing business. Belarus ranks at 57 and
Kyrgyzstan at 102 down from 99. In comparison to countries like the United States ranked
currently at 7, neither countries show very stimulated economies. But compared to Russia
at 62, Belarus is relatively superior.
3. If a bank owns a large number of non-working loans, it could possibly face serious debt
problems due to the high level of risk attached to these loans. In case of liquidity shortage,
banks will not be able to cash in on the non-working loans leaving behind bad debt. Thus,
a higher ratio of non-working bank loans to total gross loans is undesirable for any bank.
17
18. Figure 9: Non-working bank loans to total gross loans
The amount of non-working loans owned by Kyrgyzstan was at a very high level in 2010
(Figure 9). However, they seemed to have overcome their bad debt and have fallen to the
same point as Belarus (4.4%) in 2014.
From the above analysis, we can say that neither countries are in imminent danger of facing a
currency crisis. Russia however is facing one and hence I cerebrate that this difference of current
financial health caused reduced adjusted correlation. However, to reiterate my previous point,
even though the analysis shows that relationships between countries decreased during crisis, they
still have been impacted negatively by the situation in Russia.
6.4.1 Belarus
During periods of volatility in a country it is natural for countries surrounding it to try and move
away to avoid any form of contamination. The same as would take place for the medical term
of contagion, wherein people move away from the source of infection. Alexander Lukashenko,
President of Belarus, has started a passive-aggressive relationship with Russia, wherein he neither
condemned Vladimir Putin for his actions towards Ukraine nor did he support Russia. Instead
Belarus has tried to keep a safe distance from Russia while trying to forge closer relationships with
the Western countries.
Belarus has proven to be the most interesting country in my analysis as the data shows reverse
contagion, but one glance at the news in Belarus would tell you that Belarus was the most affected
by the Russian crisis (Kudrytski, 2015). After Russia, Belarus had the most marked depreciation
of its currency, even though the analysis shows a reduced correlation. The same way that we have
contagion transmission through trade linkages called as real contagion, I argue that there could
be a reverse real contagion. Even though the Belarusian rouble depreciated against the dollar, it
appreciated against the rouble. Russia is its main trading partner, and an appreciation meant that
its goods were no longer competitive against the Russian goods. Belarus lost 740 mil $ in export
revenue due to a contracted demand in Russia, followed by bans on sanctioned goods. This real
reduction in trade relationships between the two countries is what I believe to be the main cause
for this reverse contagion.
The other reason for the Belarusian rouble not to devalue as fast as the Russian rouble causing
decreased correlation is the placement of capital controls by Belarus. Belarus followed a two-step
18
19. devaluation, wherein their exchange rate was devalued by 9% in December 2014 and 7% in January
2015. Between these periods is when a form of informational contagion took place, as Belarusians
demanded hard currency (dollars) and changed their deposits to dollars due to the panic they
witnessed in Russia. This led to a huge fall in foreign reserves for the country and in order to limit
this, the Government imposed a 30% tax on exchanging Belarusian roubles for foreign currencies
(Kruk, 2015). Unlike Russia where a massive flight of capital took place, Belarus imposed capital
controls in the form of currency exchange fees and curbed market panic. However, I excogitate
that if the Russian rouble is not stabilised at a healthy rate in the near future, the Belarusian
rouble will be further affected due to their still strong relationship with Russia.
6.4.2 Kyrgyzstan
As explained in Section 6.1.1, a fall in remittances helps to depreciate the exchange rate of a
country. The selfsame way in Kyrgyzstan, remittances from Russia have fallen 33% (compared
to the same period of the previous year) causing a depreciation of 14.63% in its exchange rate.
Remittances form an equivalent of 27.8% of Kyrgyzstan’s GDP, wherein migrant workers choose
between Russia (92%) and Kazakhstan (8%) to find employment and higher income than their
native country (Eurasian Development Bank, 2015). Thus, it is quite obvious how important
remittances from Russia are to the economy of Kyrgyzstan.
The first reason for a drop in remittances is purely because of lower wages in Russia due to
the depreciated rouble. But the second important reason is a tightening of legal requirements for
migrants who choose to work in Russia as per 2015. A reduction in number of migrants would
mean lower remittances to their home country. However, now that Kyrgyzstan is a member of the
Eurasian Economic Union, it means that migrants will not require work permits to acquire jobs in
Russia and will have better work conditions. Thus, most migrants are supportive of the accession
of Kyrgyzstan into the EEU which will henceforth increase the number of migrants in Russia even
further (Lelik, 2015).
In order to further strengthen ties with Kyrgyzstan, Russia forgave $500m in debt in exchange
for a lease extension for a Russian military base in Kyrgyzstan, and pledged another $500m for its
integration into the EEU. Furthermore, Gazprom and Rosneft took over Kyrgyzstan’s oil sector
by heavily investing in it alongside assuming the energy sectors debt (Ott, 2014). Thus, what we
see is a very high connection between these two countries, regardless of the economic situation at
hand. However, this connection did not increase during the crisis period but instead decreased.
What I deduce from this situation in Kyrgyzstan is that Russia was hit much harder and even
more directly by the problems at hand causing the rouble to devalue faster than the Kyrgyz Som,
thus making for a decreased correlation. However, Kyrgyzstan can still play catch up to Russia if
it does not protect itself through the recoveries and falls of the rouble with Russia trying to align
Kyrgyzstan with itself in every possible way.
6.5 Georgia
As noted before, any speculation of crisis in Georgia owing to the current political tension, sanctions
and oil prices in Russia has turned out to be just inter-market dependence after adjustment.
According to Babych et al. (2015), Georgia is not facing a crisis, simply a depreciation of the
Lari. This was a boon to Georgia by making its exports competitive on the world market and
encouraging local production.
19
20. Georgia holds a long lasting relationship with Ukraine (previously with Russia) in more than
one way including trade, politic and social relations. But yet, 7.70% of its imports come from
Russia and 6.54% of its exports go to Russia, with whom they share cultural and geographical ties
since Soviet times.
The above paragraph shows two reasons as to why the Georgian Lari could share such a
high correlation with the Russian rouble in general. Since Georgia has indirect trade relations
with Russia through Ukraine, currency correlations could be transmitted through this third party
connection. Or it could simply be the small, yet direct trade relations with Russia that cause an
increase in one currency relative to another due to changes in export and import values.
However, this depreciation most likely has nothing to do with the crisis in Russia and everything
to do with the tapering in America. As explained for Kazakhstan (see Section 6.3), the outflow of
foreign capital from Georgia was caused by a hike in interest rates in the US. This outflow led to a
depreciation in its currency relative to the US dollar. Ever since the Russo-Georgia War in 2008,
Georgia has consciously made an effort to distance itself from Russia in terms of trade, politics
and so forth. This reduced necessity for Russia and its products has helped Georgia steer clear
from a crisis.
The other reason why Georgia is currently not facing a crisis is owing to its managed float
exchange rate, whereby the National Bank of Georgia allows market forces of demand and supply
to influence the exchange rate but within boundaries. This boundary was drawn to protect the
Lari from a sudden depreciation at which point the bank intervened by selling its foreign reserves.
But the bank should restrain itself from further interventions and let the exchange rate move
independently to another level before trying to control it. This will keep investor speculation at
bay and help avoid a currency crisis in the future (Biermann and Tsutskiridze, 2015).
7 General discussion
What I notice from putting together the statistics and the news in countries is that even though
analysis may indicate a positive or negative contagion, most of the CIS countries have been nega-
tively affected by the crisis in Russia. Some common factors that these countries share are strong
trade linkages with Russia and each other, heavy dependency of revenue on the energy sector prices
and high percentage of migrants working in Russia.
Almost all of the countries used in my analysis have shown some form of change in their
relationship with Russia, albeit in different directions. Some countries show increased correlation
of markets during periods of crisis, some reduced and some have stayed constant. Yet the exchange
rates of all these countries have devalued over the same time frame at varying moments. So why
is there a varying sense of direction?
I plotted the adjusted correlation as a function of stable period correlation in Figure 10. There
is a clear demarcation in the points helping to split the countries into the three following groups 9
:
1. Section A shows an increased relationship between countries like Armenia and Tajikistan
with Russia. At this point, adjusted correlation is an increasing function of the non-crisis
period correlation, and this results in straightforward contagion.
2. Georgia is on the borderline between Sections A and B on which all points show equal
correlation during both stable and crisis periods. This depicts absolutely no change in the
9
I also plotted fitted values from the quadratic regression to demonstrate the change in direction of the relation-
ship between correlation coefficients.
20
21. Figure 10: Relationship between correlations in non-crisis and crisis period
relationship between Russia and Georgia, regardless of the time frame.
3. After this point, adjusted correlation is a decreasing function of the stable period correlation,
which I have called reverse contagion. Thus, Belarus and Kyrgyzstan show that at such initial
high levels of correlation, there is a fall in correlation during crisis period.
Hence, I hypothesize that the reason for varying directions in correlation movements stems
from the difference in initial correlation between countries. The lower the initial correlation, the
greater the chance of contagion.
Ukraine is another country that has been severely affected by the Russian crisis but due to
the fact that they are involved in the conflict with Russia and are burdened by sanctions, their
case is rather different. Russia and Ukraine show high correlation now and during stable periods
(ρ = 0.72) but due to the political involvements, I shall treat it as an outlier and exclude it from
this paper.
21
22. 8 Conclusion
Following a fixed exchange rate policy has proven to be futile in the case of Russia, and other coun-
tries should thus switch to either an inflation targeting regime or an interest rate policy. Allowing
exchange rates to be moved by market forces of demand and supply with government interventions
as and when necessary reduces investor speculation and allows the Government to make decisions
regarding interest rates or inflation without limitations imposed by a pegged exchange rate. Geor-
gia has faced the smallest depreciation of its currency and has not entered a crisis as it follows a
managed float regime and has tried to cut its ties with Russia.
Unless and until, the CIS countries choose to withdraw from pre-Soviet times and strengthen
relationships with other countries in the world, they will be isolated with Russia. Ascension into
the EEU has come at a terrible time which further influences countries in this region to follow the
actions of Russia. Furthermore, the Western and European nations need to provide support to
enable these countries to be independent and decide which countries they want to risk setting up
relationships with. They should not be bound by geographical, political or even cultural ties.
Appendix A
Date Event
2014
26/02/14 Annexation of Crimea by Russia
17/03/14 First round of sanctions by the United States on Russia
20/03/14 Santions imposed by Russia on important persons from U.S. and Canada
21/03/14 Formal annexation of Crimea
24/03/14 Russia suspended from G8
10/04/14 Russia removed from Parliamentary Assembly of the Council of Europe
28/04/14 Second round of sanctions imposed on Russia, including personnel in close relation to Vladimir Putin
17/07/14 U.S. imposes bans on Rosneft, Novatek, Gazprombank and Vneshekonombank
31/07/14 European Union sanctions imposed on Russian financial sector
06/08/14 Russia places ban on import of food products from Europe, US, Canada, and Australia
11/09/14 U.S. extends sanctions to Sberbank (largest bank in Russia), Rostech (arms producer)
2015
01/01/15 Formation of Eurasian Customs Union
12/02/15 Ceasefire called in Ukraine
15/06/15 Sanctions extended for further six months
Table A1: Significant events in Russia
22
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