The real exchange rate of an oil exporting country: the case of Russia
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The real exchange rate of an oil exporting country:
the case of Russia
Natalia Suseeva
QEM-IDEA
Thesis supervisor: Francesca Rondina
IAE
July 2010
Abstract
In this thesis we examined whether the real exchange rate of Russia and the real
price of oil move together over time. Using International Monetary Fund and
OECD data, we constructed monthly and quarterly the real bilateral exchange
rates of Russia against US dollar and Euro and productivity differentials over
1995-2010. It is possible to find a positive long-run relationship between the
real oil prices and the real bilateral exchange rate against Euro. Moreover, this
relationship becomes more positive after the change of the monetary policy has
been implemented.
1 Introduction
Commodity-exporting countries largely depend on their primary commodity exports.
Fluctuations in the primary commodity price induce changes in trade and current ac-
count surpluses. Russia, as an oil-exporting country, is highly affected by the changes
of oil prices. The major drop in oil prices in 1998 contributed to the Russian crisis
and dramatic depreciation of the national currency. Over the period of 1999 to 2008
Russian ruble appreciated almost on 80% along with the increasing oil prices.
In this thesis we take the real oil price as a proxy of the terms of trade and examine
whether oil price fluctuations affect the real effective and bilateral exchange rates of2 Natalia Suseeva Russia. Since there occurred a change of the exchange rate policy with respect to Euro, we try to understand whether it had an effect on the relation between the real oil prices and the bilateral exchange rate. Our empirical investigation is based on the theoretical framework developed by Cashin et al. (2004). To describe the theoretical link between the real exchange rate and real commodity prices, they considered a small open economy that produces two different types of goods: a nontradable good and exportable good. In this simple model, an increase in the international price of the primary commodity will increase wages in the commodity sector. As wages are equal across sectors, the increase in wages will raise the relative price of the nontraded good and, therefore, appreciate the real ex- change rate. This analysis is in line with the literature that stresses the role of terms of trade in the determination of exchange rate, which includes work by De Gregorio and Wolf (1994) and Obsfeld and Rogoff (1996). There is a large empirical literature on the relationship between the terms of trade and the real exchange rate for the case of commodity-exporting countries. The lit- erature has identified the terms of trade as one of the potential determinants of the real exchange rate, which may explain long and persistent deviations from a simple Purchasing Power Parity (PPP) equilibrium. Chen and Rogoff (2003) found that for Australia and New Zealand the US dollar price of their non-energy commodity exports has a strong and stable influence on the real exchange rates. In favor of the presence of cointegration between the real exchange rates and the real oil prices, Chen and Chen (2006) performed a rigorous analysis. They examined the long-run relationship between real oil prices and real exchange rates for the G7 coun- tries. They showed that real oil prices may have been the dominant source of real exchange rate movements and found evidence of a cointegrating relationship between real oil prices and real exchange rates. Most of the literature is concentrated on understanding the sources of real exchange rate fluctuations in developed countries and evidence on the behavior of developing country real exchange rates has been scarce. Cashin et al (2006) examined the be- havior of real exchange rates of commodity-exporting countries and the real prices of their commodity exports on the set of 58 developing countries. They found an evidence of a long-run relationship between national real exchange rate and real com- modity prices for about one-third of the commodity-exporting countries. For the case of oil-exporting countries, Habib and Manolova-Kalamova (2007) inves- tigated an impact of the real oil price on the real exchange rates. They also used the
The real exchange rate of an oil exporting country: the case of Russia 3 theoretical framework developed by Cashin et al. (2004), which in turn is built on De Gregorio and Wolf (1994) and Obsfeld and Rogoff (1996). They created a measure of the real effective exchange rates for Norway, Saudi Arabia and Russia, and tested whether real oil prices and productivity differentials against 15 OECD countries influ- ence exchange rates. They found a positive long-run relationship between the real oil price and the real exchange rate in the case of Russia, but found no impact in the case of Norway and Saudi Arabia. However, the results Habib and Manolova-Kalamova (2007) obtained for Russia are preliminary and should be taken with some caution, because the sample size they used is relatively small (46 observation). There are a few studies on the real exchange rate of Russia. Spatofora and Stavrev (2003) estimate the equilibrium real exchange rate of Russia and confirmed a link between equilibrium real exchange rate and oil prices, in spite of the attempts to offset such a links through monetary policy and foreign reserve accumulation. Sosunov and Zamulin (2006) in the environment of a calibrated general equilibrium model investigated whether the real appreciation of the Russian ruble in 1998-2005 is due to the increase in the oil revenues. They showed that the real appreciation of ruble is fully consistent with the growth of the oil export revenues that took place during this period. However, the increase of the oil price alone cannot explain the ap- preciation, but a combination of a series of temporary but persistent oil price shocks with permanent increases in the volume of exports produces appreciation in a model economy of similar magnitude observed in reality. In this thesis we constructed the real bilateral exchange rates with respect to Euro and US dollar and the measure of productivity differentials against 30 Russia’s main trading partners and against 14 Euro area countries, thus controlling also for the Balassa-Samuelson effect and including productivity differentials as a potential ex- planatory variable of the real exchange rate. Second, in order to increase the number of observations, we constructed and added to the analysis monthly data on the real effective and bilateral exchange rates and the real price of oil. Third, to examine the effect of change of the monetary policy we split the sample of the real bilateral exchange rate against Euro and analyzed two separate regressions. Finally we per- formed separate analysis of the real exchange rates as stationary processes, by doing the OLS regressions, and as non-statironary processes, by estimating the Vector error correction model.
4 Natalia Suseeva 2 Data analysis 2.1 Background Russia is the largest oil exporter in the world, producing an average of 9.93 million barrels of oil per day in 2009 for a total of 494.2 million tons. Russia’s economy is heavily dependent on oil and natural gas exports. In 2009, oil exports generated around 50% of Russias export revenues, and accounted for 30 percent of all foreign direct investment (FDI) in the country. After the August 1998 crisis Russia’s economy started to recover quickly. Over the period of 1999 to 2008 Russia’s trade surplus continued to strengthen due to an oil price-led export boom, which contributed to the growth by easing liquidity constraints in the economy. The flexibility of the exchange rate in Russia was limited. In the mid-1995 the Cen- tral Bank of Russia (CBR) has introduced the corridor system in order to stabilize the ruble. The corridor system was designed to limit the rubles fluctuations within a narrow range by limiting its upper and lower limits. Between 1995 and 1998, the de jure exchange rate arrangement of the ruble was a floating exchange rate with rule-based intervention, according to the IMF. After the currency and financial crisis in the summer of 1998, the IMF de jure classified Russia as a floating exchange rate regime. Since 1999 the CBR has had annual targets for the speed of disinflation, but tradi- tionally also set an explicit ceiling for real appreciation of the rouble. In terms of monetary policy instruments, intervention in the foreign exchange market has been the CBR’s main tool for achieving those objectives. Therefore, Russia’s monetary and exchange rate policy framework has often been referred to as a de facto nominal exchange rate peg (OECD, 2006). On February 4 2005 the CBR announced that it has started to stabilize the daily volatilities of the Russian ruble against a dollar-euro currency basket. While the an- nounced weight of the euro was 10% (90% dollar) by then, the CBR increased this weight to 40% within next ten months.
The real exchange rate of an oil exporting country: the case of Russia 5
2.2 Data description
The data sample ranges from 1995 to 2010 for the real effective exchange rate and the
bilateral real rate against US dollar, for total of 59 observations of quarterly data and
181 for monthly. For the bilateral real exchange rate against Euro, the data sample is
from 1999 to 2010 with 43 observations of quarterly data and 132 of monthly. Data
of the real effective exchange rate based on relative CPI, consumer price index and
nominal bilateral exchange rates are from the International Financial Statistics (IFS)
of the IMF.
Figure 1 below represents the dynamics of the real effective exchange rate (REER),
the real bilateral exchange rate against US dollar (RER USD) and Euro (RER Euro)
and different measures of the real oil prices (UK Brent, Urals and the IFS crude
petroleum average).
2.2 REER
2
1.8
RER_Euro
1.6
1.4
RER_USD
1.2
1
0.8
0.6
0.4
IFS
0.2
Urals
0
-0.2Jan.95 Jan.97 Jan.99 Jan.01 Jan.03 Jan.05 Jan.07 Jan.09
-0.4
-0.6 UK Brent
-0.8
-1
Figure 1: The real exchange rates and the real oil price
Based on the methodology introduced in Habib and Manolova-Kalamova (2007), we
constructed the trade-weighted relative productivity differential (PROD)1 by calcu-
1
The formula used to calculate productivity differentials is: P ROD = Πj6=i ( PP roductivity
roductivityi Wij
j
) ,
P roductivityi and P roductivityj denote the productivities of Russia and the trading partner. Wij6 Natalia Suseeva
lating a trade-weighted geometric average of productivity against trading partners
productivity. We built three different productivity differentials, depending for which
real exchange rate we used it. For the regression of real effective exchange rate, the
productivity differential (PROD) is against 30 Russias main trading partners. For
the bilateral real exchange rate against Euro, PROD Euro is against 14 Euro area
countries. And for the bilateral real exchange rate against US dollars, PROD US is
against USA. Productivity is defined as the seasonally adjusted real gross domestic
product (GDP) relative to the number of people employed. Data of GDP and em-
ployment are from the OECD Economic Outlook Database. Since the data on the
GDP of Russia is quarterly or yearly, we were able to construct only the quarterly
productivity differentials.
For a nominal price of oil we used the IFS petroleum crude oil price index, the Urals
and UK Brent. We constructed both monthly and quarterly data of the real oil price
(ROP)2 . The real price of oil is calculated as the nominal price of oil relative to the
world price of manufactured exports which is often used as a proxy of the import
prices of commodity exporters (Deaton and Miller 1996, Cashin et al 2004 and Habib
and Manolova-Kalamova 2007).
2.3 Unit root analysis
In this subsection we explore the time series data constructed. We examine each
individual series by using the Augmented DickeyFuller (ADF) test as well as the
PhillipsPerron test and DF-GLS test for a unit root. The results are presented in the
Table1.
indicates the relative weight of the bilateral trade between Russia, i, and the foreign country, j, on
the total trade of the home country
2
We report the results only for the ROP constructed using the IFS crude petroleum index, since
the results for Urals and UK Brent are very similar. Thus the results are robust for using different
nominal oil pricesThe real exchange rate of an oil exporting country: the case of Russia 7
Table 1.Unit root tests
ADF Phillips-Perron DF-GLS
H0 : x ∼ I(1) H0 : x ∼ I(1) H0 : x ∼ I(1)
Z(pho) Z(t)
REER 1.622 6.252 1.874 3.027*
RERU SD 2.502 11.210* 2.661* 3.410**
REREuro 2.395 3.790 2.475 2.647
P ROD 0.798 1.204 0.558 1.501
P RODU SD 0.335 0.703 0.456 1.411
P RODEuro 1.089 1.328 0.562 1.150
ROP 1.095 2.699 1.179 3.229**
Note: Asterisks *, ** indicate 10% and 5% significance levels.
Clearly, according to the ADF test, all real exchange rates are integrated of order
one I(1). But the result for Phillips-Perron test is rejecting the null of non-stationarity
This results are consistent with Habib and Manolova-Kalamova (2007), they per-
formed ADF, KPSS and Lanne unit root test and also received a mixed results. We
follow Chen and Rogoff (2003) and Habib and Manolova-Kalamova (2007) and con-
sider several alternative data-generating processes, both I(0) and I(1), as robustness
check for our result.
The results of unit root tests of the real oil price and productivity differential show
that this variables are nonstationary. Therefore we use the first difference of this vari-
ables in the regressions of stationary variables and levels with other nonstationary
variables.
We apply the Johansen test for cointegration and investigate the results from the
trace statistics. Table 2 presents the results.
Table 2. Cointegration test
Johansen test
H0 : r = 0 H0 : r = 1 H0 : r = 2
REER, P ROD, ROP 27.797 7.268 0.748
RERU SD , P RODU SD , ROP 30.272** 13.873 0.600
REREuro , P RODEuro , ROP 41.059** 11.814 0.100
Note: Asterisks ** indicates 5% significance levels.
In the case of the real effective exchange rate, the real oil prices and productivity
differential, variables seem to be not cointegrated. This result differs from Habib and8 Natalia Suseeva Manolova-Kalamova (2007), the reason for that might be because of different time periods and because they used their own measure of the real effective exchange rate, which is different from the IFS one. On the other hand, for the bilateral real exchange rates against Euro and USD, the trace test suggests a cointegrating relationship between oil prices, the real exchange rates and productivity differentials. Evidence of cointegration implies that the real oil price can adequately capture all the permanent innovations in the real bilateral exchange rates. 3 Empirical analysis Based on the analysis performed in the previous section, we perform the estimation of two different specifications. In the first specification, assuming that the real exchange rates are stationary, we check if the change in real oil prices and productivity differ- ential can explain the level of the real bilateral and effective exchange rates. In the second specification, when the real exchange rate is assumed to be non-stationary, we estimate a vector error correction model and search for a long-run relationship between real oil prices, productivity differentials and the real exchange rates. For the regressions we used monthly and quarterly samples of data. We include time dummies in August 1998 (the third quarter of 1998) and in January 2009 (first quar- ter of 2009). The first dummy is to control for the effect of the Russias liquidity crisis. The second is to eliminate the effect of the change of monetary policy, when the Cen- tral Bank or Russia readjusted the upper and lower limits of the range of fluctuations in the value of the bi-currency basket and ruble depreciated dramatically. 3.1 The real exchange rate as a stationary process Under the assumption that the real exchange rates are stationary, we test for a simple linear relationship between the level of the real bilateral and effective exchange rates and the first differences of oil prices and productivity differentials. The equations
The real exchange rate of an oil exporting country: the case of Russia 9
that we estimate are the following:
X
REERt = const. + βREERt−1 + γi ∆ROPt−i + t (1)
i
X
RER U SDt = const. + βRER U SDt−1 + γi ∆ROPt−i + t (2)
i
X
RER Eurot = const. + βRER Eurot−1 + γi ∆ROPt−i + t (3)
i
We test for contemporaneous and lagged impact of the change in real oil prices on the
real exchange rates. In Table 3 and 4 we report the result of the regressions (1)-(3).
The first table is using quarterly data and the second is using monthly.
Table 3. OLS regression results, quarterly data
REER RER USD RER Euro
Real exchange ratet−1 0.926*** 0.949*** 0.943***
[0.06] [0.04] [0.04]
∆t (Real oil price) 0.064 0.033 0.153***
[0.016] [0.08] [0.05]
∆t−1 (ROP ) -0.124** -0.367*** -0.158**
[0.05] [0.09] [0.06]
∆t−2 (ROP ) 0.063 0.154 0.148**
[0.05] [0.09] [0.06]
∆t−3 (ROP ) 0.023 -0.084 -0.082
[0.05] [0.09] [0.06]
∆t−4 (ROP ) 0.137** 0.119 0.155**
[0.06] [0.10] [0.06]
const. 0.019 -0.074 -0.085
[0.06] [0.07] [0.06]
Adj. R-sq 0.96 0.91 0.98
Number of observations 59 59 43
Sample period 1995Q1-2009Q3 1995Q1-2009Q3 1999Q1-2009Q3
Note: Asterisks (*,**,***) indicate 10%, 5% and 1% significance levels. Standard
errors are reported under the coefficients in square brackets.
In the REER regression the results show that the real exchange rate tends to depre-
ciate over the short run, since the coefficient for the first lag of the change in the
real oil price is negative and significantly different from zero, but then it appreciates10 Natalia Suseeva
after one year, as the coefficient for the forth lag has almost the same value, positive
and significantly different from zero. These results are consistent with Habib and
Manolova-Kalamova (2007).
Results for the RER USD regression are similar to those of the REER regression only
for the short run. In the effect of the change in the real oil prices in the long run is
not significantly different from zero and seems to be more negative than in the REER
regression.
In the RER Euro regression we find that three lags out of four of the change in real oil
prices are significant and enter with both positive and negative signs, with a greater
impact of positive effects in the long run.
Table 4. OLS regression results, monthly data
REER RER USD RER Euro
coef. st.err. coef. st.err. coef. st.err.
Real exchange ratet−1 0.985*** [0.02] 0.977*** [0.02] 0.980*** [0.01]
∆t (Real oil price) -0.015 [0.04] -0.074* [0.04] 0.019 [0.03]
∆t−1 (ROP ) 0.026 [0.04] 0.044 [0.04] 0.060* [0.03]
∆t−2 (ROP ) 0.037 [0.04] 0.041 [0.04] 0.041 [0.03]
∆t−3 (ROP ) 0.050 [0.04] 0.006 [0.04] -0.036 [0.03]
∆t−4 (ROP ) -0.023 [0.04] -0.022 [0.04] -0.004 [0.03]
∆t−5 (ROP ) -0.013 [0.04] -0.000 [0.04] 0.034 [0.03]
∆t−6 (ROP ) 0.025 [0.04] 0.017 [0.04] -0.026 [0.03]
∆t−7 (ROP ) 0.015 [0.04] 0.016 [0.04] 0.026 [0.03]
∆t−8 (ROP ) 0.041 [0.04] 0.044 [0.04] 0.063** [0.03]
∆t−9 (ROP ) 0.023 [0.04] 0.032 [0.04] -0.021 [0.03]
∆t−10 (ROP ) 0.025 [0.04] 0.001 [0.05] -0.002 [0.03]
∆t−11 (ROP ) -0.039 [0.04] -0.035 [0.05] 0.012 [0.03]
∆t−12 (ROP ) 0.035 [0.04] 0.030 [0.04] 0.018 [0.03]
const. 0.030 [0.02] -0.037 [0.02] -0.030 [0.02]
Adj. R-sq 0.96 0.96 0.98
Number of observations 181 181 132
Sample period 01.1995-01.2010 01.1995-01.2010 01.1999-01.2010
Note: Asterisks (*,**,***) indicate 10%, 5% and 1% significance levels. Standard
errors are reported in square brackets.The real exchange rate of an oil exporting country: the case of Russia 11 In the REER regression of monthly data estimated coefficients are smaller and less significant. In order to compare the result with quarterly data we sum up the coef- ficients and can see that the results are similar. To see the joint significance of the estimated coefficients, we perform the F-test statistics and obtain the same result as for the quarterly data: the coefficients of the last four and first four lags of the change in oil prices are jointly significant. For RER USD and RER Euro the estimated coefficients are smaller, but if sum them up by quarters, we can see values and signs similar to the quarterly regression esti- mated coefficients. Moreover, in the RER USD regression the contemporaneous effect of the change in the real oil prices is significant, what is consistent with the results of quarterly data regression. In the RER Euro regression the coefficients for the first and eights lag of the change in the real oil price are the same sign and in the same period range as the coefficients of the quarterly data. For all regressions it seems that the change in real oil prices has short-run negative effect and than for the REER and RER Euro it has a positive effect in one year. These results are consistent with the Habib and Manolova-Kalamova (2007) OLS re- gression results for the REER. A possible explanation why the effect of the change in real oil prices changes the sign is the following. When the changes in the real oil prices increase, this might be a bad news for the Russia’s trading partners, which are mainly oil importers, and a good news for Russian monetary authorities, since Russia is oil-exporter. The bad news could lead to the increase of the CPI of the Russia’s trading partners and therefore the real exchange rate or Russia will depreciate. The good news could have a positive impact on the nominal exchange rate of Russia, lead- ing to the appreciation of the national currency. But could also lead to the increase of the domestic prices. So depending which of this effects is prevailing, in the sort run, the change in real oil prices can have a positive or a negative effect on the real exchange rate of Russia. In order to capture the effect of the monetary policy change, we split the sample of the RER Euro in two: one before the CBR started to target the dollar-euro currency basket (previously it targeted US dollar only) and one after. The results of these regressions are presented in Table 5. From the estimated coefficients we can clearly see that the effect of the change of the real oil prices changes and become more positive. The change in policy that happened in February 2005 makes the positive relationship between the change in the real price of oil and the RER Euro even stronger, because it implies that the monetary author-
12 Natalia Suseeva
ities in Russia respond even more than before to the appreciation pressures on the
nominal exchange rate between ruble and euro.
Table 5. RER Euro regression results
RER Euro RER Euro
(first period) (second period)
coef. st.err. coef. st.err.
Real exchange ratet−1 0.914*** [0,03] 0.909*** [0,06]
∆t (Real oil price) 0.009 [0,04] -0.085 [0,05]
∆t−1 (ROP ) -0.015 [0,04] 0,104* [0,05]
∆t−2 (ROP ) -0.062 [0,04] 0,121** [0,05]
∆t−3 (ROP ) -0,105** [0,04] 0.007 [0,05]
∆t−4 (ROP ) -0.060 [0,04] 0.017 [0,05]
∆t−5 (ROP ) 0.016 [0,04] 0.027 [0,05]
∆t−6 (ROP ) -0.045 [0,04] -0.060 [0,05]
∆t−7 (ROP ) 0.020 [0,04] 0.048 [0,05]
∆t−8 (ROP ) 0,095** [0,04] 0.027 [0,05]
∆t−9 (ROP ) -0.037 [0,04] 0.004 [0,05]
∆t−10 (ROP ) 0.012 [0,04] 0.002 [0,05]
∆t−11 (ROP ) -0.011 [0,04] 0.059 [0,05]
∆t−12 (ROP ) 0.037 [0,04] -0.008 [0,05]
const. -0.137 [0,05] -0.135 [0,09]
Dummies 09.1998-12.1998 09.1998-12.1998
Adj. R-sq 0.95 0.82
Number of observations 73 59
Sample period 01.1995-02.2005 03.2005-01.2010
Note: Asterisks (*,**,***) indicate 10%, 5% and 1% significance levels.
Standard errors are reported in square brackets.
3.2 The real exchange rate as non-stationary process
In this section we trying to find the long-run relationship between the real exchange
rates, the real oil prices and the productivity differentials. In the section 2.3 we tested
for the number of cointegrating relations and found evidence of cointegration. WeThe real exchange rate of an oil exporting country: the case of Russia 13
use the Vector Error Correction model in the following form:
p
X
∆yt = Πyt−1 + ΨDt + Φi ∆yt−i + t (4)
i=1
where the vector of endogenous variables yt = |REERt , P RODt , ROPt | for the real ef-
fective exchange rate regression, yt = |RER U SDt , P ROD U St , ROPt | for the real bi-
lateral exchange rate against US dollar and yt = |RER Eurot , P ROD Eurot , ROPt |
for the real bilateral exchange rate against Euro. Dt is a vector of exogenous variables
(constant terms and time dummies) and t is the error term. Φi is a matrix of the
short-term dynamics, Ψ is a matrix of coefficients of deterministic terms. Π = αβ
where α is the vector of speeds of adjustment and β is the transpose of the cointe-
grating vectors.
In order to find the number of lags to use, we used the VAR representation of the
VEC model:
p−1
X
yt = (Ik + Π + Φ1 )yt−1 + ΨDt + (Φi − Φi−1 )yt−i − Φp−1 yt−p + t (5)
i=2
We choose the lag order of 3 for all three regressions according to the Akaike and
Schwarz’s Bayesian information criteria.
Table 6 reports the long-run parameters of the estimated system:
Table 6. VEC regression results
REER RER USD RER Euro
Real Oil price 0.134 -0.255 0,420**
[0,02] [0,04] [0,10]
P roductivity dif f erential 1,397 2.650*** -0.003
[0,17] [0,19] [0,21]
Constant 2.754** 0.050 -1,396***
[0,10] [0,08] [0,26]
Error correction (alpha) -0,159** -0,223*** -0,589***
[0,07] [0,07] [0,14]
Half-life of deviations 4Qs 3Qs 1Q
No. Obs 59 59 43
Sample period 1995Q1-2009Q3 1995Q1-2009Q3 1999Q1-2009Q3
Note: Asterisks (*,**,***) indicate 10%, 5% and 1% significance levels. Standard
errors are reported under the coefficients in square brackets.14 Natalia Suseeva The results show that there exist positive long run relationship between the the real bilateral exchange rate against Euro and the real oil prices, since the estimated coefficient is positive, relatively large and significantly different from zero. For the real effective exchange rate and the real bilateral exchange rate against US dollar, we did not find any significant long-run effect of the real oil prices. In order to reinforce the results of the VEC regression, we performed also the OLS regression of the quarterly data with the changes of productivity differentials. The results are robust to the inclusion of the changes in productivity differentials, the table A.1 is reported in the Appendix. The coefficient for productivity differential in the RER USD regression is highly positive and significant. Therefore we can say that, in Russia the Balassa- Samuelson effect is taking place, when the productivity growth in the tradable sector, relative to the USA productivity, has a positive effect on the real exchange rate against US dollars. 3.3 Discussion of the results In both specifications, when the real exchange rate is assumed to be stationary and non-stationary, we find a positive long-run relationship between the real bilateral ex- change rate of Russia against Euro and the real oil prices. The results for the real effective exchange rate are consistent with the ones obtained by Habib and Manolova- Kalamova (2007). Meanwhile for the real exchange rate of Russia against US dollar the real oil prices seem to have more negative effect. A possible interpretation of this result is the following. If Russia sells a large portion of its oil to European countries, the nominal exchange rate against Euro will be subject to appreciation pressures every time the price of oil increases. The monetary authorities in Russia will intervene to avoid this apprecia- tion, this will eventually change the relative prices between the Euro area and Russia and will appreciate the real bilateral exchange rate against Euro. Since Russia does not trade oil that much with the US (main Russian oil exports are to Europe), the nominal exchange rate with the US dollar will not be subject to the same appreciation pressures when the price of oil increases. This can help to explain why we don’t find a positive relationship between the real price of oil and the real exchange rate of Russia against US dollar. The change in policy that happened in February 2005 makes the positive relationship between the real price of oil and the real bilateral exchange rate of Russia against Euro even stronger, because it implies that the monetary authorities in Russia will respond even more than before to the appreciation pressures on the
The real exchange rate of an oil exporting country: the case of Russia 15
nominal exchange rate between Russia and Euro.
-0.7
Jan.95 Jan.97 Jan.99 Jan.01 Jan.03 Jan.05 Jan.07 Jan.09
-0.8
-0.9
-1
-1.1
-1.2
-1.3
-1.4
NER_Euro
-1.5
-1.6
-1.7 NER_USD
-1.8
Figure 2: The nominal exchange rates against US dollar and Euro
From the Figure 2 we see that the nominal bilateral exchange rates of Russia against
US dollar and Euro significantly depreciated in September 1998 during the Russian
crisis and in January 2009 in the period of the world financial crisis, but in the period
of 1999-2008 the nominal exchange rates did not appreciate, when the prices of oil
were increasing. Meanwhile in that period the inflation in Russia was quite high,
around 15%. So in the period of 1999-2008 the roubles real appreciation in response
to the large positive terms-of-trade shock materialized mainly through a positive in-
flation differential vis-a-vis the average of Russia’s trading partners instead of nominal
appreciation.16 Natalia Suseeva 4 Conclusion In this thesis we examined the evidence whether the real effective and bilateral ex- change rates of Russia react to fluctuations of the real oil price. We used monthly and quarterly samples of data over the period from 1995 and 2010 and obtained similar results for both samples. We constructed the productivity differentials against 30 Russia’s main trading partners for the regression of the real effective exchange rate, against 14 Euro area countries for the regression of the real bilateral exchange rate against Euro. Therefore we controlled the possible effect of the productivity differ- entials. We find positive long-run relationship between the real bilateral exchange rate against Euro and the real oil prices. This finding holds for both cases when the real exchange rate is assumed to be stationary and non-stationary. We also investi- gated the effect of the monetary policy change and can conclude that the effect of the change in real oil prices became more positive after russian monetary authorities included euro in the targeting bi-currency basket.
The real exchange rate of an oil exporting country: the case of Russia 17
Appendix
Table A.1 OLS regression results with productivity differential, quarterly data
REER RER USD RER Euro
Real exchange ratet−1 0.968*** 0.990*** 0.976***
[0.05] [0.05] [0.05]
∆t (Real oil price) 0.053 0.132 0.142**
[0.07] [0.08] [0.06]
∆t−1 (ROP ) 0.011 0.029 -0.050
[0.08] [0.08] [0.08]
∆t−2 (ROP ) -0.053 -0.261*** 0.135**
[0.08] [0.09] [0.06]
∆t−3 (ROP ) 0.037 0.067 -0.066
[0.08] [0.09] [0.06]
∆t−4 (ROP ) 0.140 -0.024 0.207***
[0.09] [0.09] [0.07]
∆(P roductivity dif f erential) 0.439 0.242 -0.098
[0.33] [0.08] [0.33]
∆t−1 (P ROD) 0.344 0.191 -0.589*
[0.32] [0.52] [0.33]
∆t−2 (P ROD) 1.110*** 0.323 0.350
[0.32] [0.55] [0.35]
∆t−3 (P ROD) -0.387 -0.457 -0.345
[0.36] [0.58] [0.34]
∆t−4 (P ROD) -0.209 0.041 -0.346
[0.40] [0.57] [0.31]
const. 0.061 -0.007 -0.033
[0.10] [0.08] [0.07]
Adj. R-sq 0.92 0.90 0.94
Number of observations 59 59 43
Sample period 1995Q1-2009Q3 1995Q1-2009Q3 1999Q1-2009Q3
Note: Asterisks (*,**,***) indicate 10%, 5% and 1% significance levels. Standard
errors are reported under the coefficients in square brackets.18 Natalia Suseeva References Backus, D.K. and M.J. Crucini (2000). Oil prices and terms of trade. Journal of International Economics 50, 185-213. Baxter, M. and M.A. Kouparitsas (2000). What can account for fluctuations in the terms of trade. Federal Reserve Bank of Chicago, Working Paper 2000-25. Bayoumi, T.; J. Lee and S. Jayanthi (2005). New Rates from New Weights. IMF Working Paper Series. WP/05/99. Cashin, P.; L.F. Cspedes and R. Sahay (2004). Commodity currencies and the real exchange rate. Journal of Development Economics 75, 239-268. Chen, S.-S. and H.-C. Chen (2007). Oil prices and real exchange rates. Energy Eco- nomics, 29, 390-404. Chen, Y.-c. and K. Rogoff (2003). Commodity Currencies. Journal of International Economics 60, 133-160. Chaudhuri, K. and B.C. Daniel (1998). Long-run Equilibrium Real Exchange Rates and Oil Prices. Economics Letters 58, 231-238. De Gregorio, J. and H. Wolf (1994). Terms of trade, productivity and the real ex- change rate. NBER Working Paper Series, No. 4807. Gregory, A.W. and B.E. Hansen (1996). Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics 70. 99-126 Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press, Princeton, NJ. Johansen, S. (1991). Estimating and hypothesis testing of cointegration vectors in Gaussian Vector Autoregressive Models. Econometrica 59, 1551-1580. Johansen, S. (2000). A Bartlett correction factor for tests on the cointegrating rela-
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