The Analysis of Oil Price and Ruble Exchange Rate

Abstract

The exploitation and export of crude oil is the foundation of the economy of Russia. Such a dependence is the reason for the emergence of a set of destructive economic phenomena known as "Dutch Disease". The most destructive effect of the Dutch Disease is the dependence between the exchange rate level and the crude oil price. The aim of the article is to describe the impact of oil price on the Rubel – US dollar exchange rate. The analysis was based of time series data of Brent oil prices in USD per barrel and USD/RUB exchange rate. The data frequency was weekly, covering the period from July 2015 to December 2017. The time series length was 651 observations. The period subjected to the analysis was divided into three sub-periods: 01.07.2005 - 30.06.2008, 01.02.2009 - 30.06.2014 and 01.01.2015 - 15.12. 2017. The analysis used econometric model (VAR) built in accordance with the Enle-Granger methodology. The analysis has shown that the USD/RUB exchange rate was affected by the changes in oil price in all the sub-periods defined above. The analysis has shown that the influence of oil price on the USD/RUB exchange rate was stronger after the 2008 world financial crisis. This confirms the hypothesis that the Russian Economy shows symptoms of the Dutch Disease.

Full text

Y. Bilan, S. Gedek, G. Mentel
ISSN 1648-4460
Empirical Investigation on Economic Roles of Price
TRANSFORMATIONS IN BUSINESS & ECONOMICS, Vol. 17, No 3 (45), 2018
195
Bilan, Y., Gedek, S., Mentel, G. (2018), “The Analysis of Oil Price and
Ruble Exchange Rate”, Transformations in Business & Economics, Vol.
17, No 3 (45), pp.195-205.
THE ANALYSIS OF OIL PRICE AND RUBLE EXCHANGE
RATE
1Yuriy Bilan
Department of Finance, Banking and
Accountancy
The Faculty of Management
Rzeszow University of Technology
al. Powstańców Warszawy 10
35-959 Rzeszow
Poland
Tel.: +48 506 354 648
E-mail: yuriy_bilan@yahoo.co.uk
2Stanislaw Gedek
Department of Economics
The Faculty of Management
Rzeszow University of
Technology
al. Powstańców Warszawy 10
35-959 Rzeszow
Poland
Tel.: +48 504 016 454
E-mail: gedeks@prz.edu.pl
3Grzegorz Mentel
Department of Quantitative
Methods
The Faculty of Management
Rzeszow University of Technology
al. Powstańców Warszawy 10
35-959 Rzeszow
Poland
Tel.: +48 608 591 330
E-mail: gmentel@prz.edu.pl
1Yuriy Bilan, PhD, Associate Professor, Department of
Finance, Banking and Accountancy, Rzeszow University of
Technology. He is the President of the Centre of Sociological
Research. He is the Deputy Editor-in-Chief of the Economics
and Sociology Journal and the Editor-in-Chief of Journal of
International Studies. His research interests are labour market,
entrepreneurship, energy and society.
2Stanislaw Gedek, PhD, Associate professor and the Head of
the Department of Economics at the Rzeszow University of
Technology. The author of 60 scientific papers and 4
monographs. The main area of his scientific activities is the use
of quantitative methods in the analysis of economic processes
and management.
3Grzegorz Mentel, PhD, has been working at Rzeszow
University of Technology, Faculty of Management at the
Department of Quantitative Methods since 2000. He has been
working as a lecturer since 2007. In the years 2010-2012, he
was a branch manager of Bank Pocztowy S.A. in Rzeszow.
The author of publications in the field of finance and capital
markets, including monographs about the risk of financial
instruments. Specialist in risk management of securities,
fundamental analysis, technical analysis, multivariate analysis
and forecasting.
---------TRANSFORMATIONS IN --------
BUSINESS & ECONOMICS
© Vilnius University, 2002-2018
© Brno University of Technology, 2002-2018
© University of Latvia, 2002-2018

Y. Bilan, S. Gedek, G. Mentel
ISSN 1648-4460
Empirical Investigation on Economic Roles of Price
TRANSFORMATIONS IN BUSINESS & ECONOMICS, Vol. 17, No 3 (45), 2018
196
Received: April, 2017
1st Revision: May, 2017
2nd Revision: January, 2018
Accepted: April, 2018
ABSTRACT
. The exploitation and export of crude oil is the
foundation of the economy of Russia. Such a dependence is the
reason for the emergence of a set of destructive economic
phenomena known as "Dutch Disease". The most destructive effect
of the Dutch Disease is the dependence between the exchange rate
level and the crude oil price. The aim of the article is to describe the
impact of oil price on the Rubel – US dollar exchange rate. The
analysis was based of time series data of Brent oil prices in USD
per barrel and USD/RUB exchange rate. The data frequency was
weekly, covering the period from July 2015 to December 2017. The
time series length was 651 observations. The period subjected to the
analysis was divided into three sub-periods: 01.07.2005 -
30.06.2008, 01.02.2009 - 30.06.2014 and 01.01.2015 - 15.12. 2017.
The analysis used econometric model (VAR) built in accordance
with the Enle-Granger methodology. The analysis has shown that
the USD/RUB exchange rate was affected by the changes in oil
price in all the sub-periods defined above. The analysis has shown
that the influence of oil price on the USD/RUB exchange rate was
stronger after the 2008 world financial crisis. This confirms the
hypothesis that the Russian Economy shows symptoms of the Dutch
Disease.
KEYWORDS
: Russian economy, crude oil, VAR model,
exchange rates.
JEL classification
: C58; F31; G15.
Introduction
It is difficult to overestimate the importance of oil and gas sector for the Russian
economy. Russia is a major player in the world energy markets. It has second largest proven
natural gas reserves (almost as large as Iran, the leader of that ranking) nearly one fifth of
world proven natural gas reserves and it is among the top six in proven oil reserves. Russia is
the largest exporter of natural gas, the second largest crude oil exporter and the second largest
exporter of petroleum products
1
. According to the balance of payments of the Russian
Federation
2
this sector constituted more than 50% of Russia’s export in the year 2016. Energy
export has been a major driver of Russia’s economic growth in the last years, as world oil
prices have been very high and Russian oil production has risen.
Oil has a special place in Russia’s international trade. Over 70% of its production is
exported (only one third of that as processed petroleum products) and oil export constitutes
over 50% of Russia’s export revenue
3
. Oil can be called “Russia’s best ally”
4
. However, this
is a double-edged sword for Russia as it makes the Russian economy dependent on oil exports
and vulnerable to fluctuations in oil prices.
1
See BP Statistical Review of World Energy, June 2017.
2
See: Balance of Payments, International Investment Position, and External Debt of the Russian Federation, Bank of Russia, Moscow 2016.
[https://www.cbr.ru/eng/statistics/credit_statistics/bop/White_book.pdf].
3
Only about 30% of Russian natural gas production is exported, while the rest is used on domestic market. However, natural gas is more
widely used in Russia’s foreign policy, as it has monopolistic position on some European markets.
4
The second would be natural gas. This is a paraphrase of the saying of Russian tsar Alexander III that Russia has only two allies – its Army
and its Navy (Konończuk, 2012).

Y. Bilan, S. Gedek, G. Mentel
ISSN 1648-4460
Empirical Investigation on Economic Roles of Price
TRANSFORMATIONS IN BUSINESS & ECONOMICS, Vol. 17, No 3 (45), 2018
197
1. Literature Review
In the theory of economics the term “Dutch Disease” exists which refers to the
problems in countries’ economy caused by the massive exploitation of natural resources.
Originally the problems were denoted by the decline in the manufacturing sector and
agriculture as the exploitation of natural resources increased
5
. This problem is well described
and has an extensive literature on it
6
. Four main channels of transmission from extensive
exploitation of natural resources to slow economic growth have been suggested in the
literature: the appreciation of local currency
7
and the increased exchange rate volatility,
growing rent-seeking behaviour, the reduction of private and public incentives to accumulate
human capital, and the decrease of private and public incentives to save and invest (see, for
example, Gylfason, Zoege, 2001).
Overvalued currency makes export less profitable and foreign commodities cheaper on
the domestic market, which decreases the country’s competitiveness. Booms and busts on the
raw materials market result in fluctuations in export earnings that trigger the volatility of
exchange rate and further increase uncertainty that harms foreign trade and foreign
investment.
Growing rent-seeking behaviour is determined by huge natural resource rent that
increases government redistribution activity. Further, it sparks competition between social or
ethnic groups, which leads, in the best case, to the state of affairs that public subsidies and
social transfers are larger than incomes generated by natural resources. This kind of
competition may even cause civil wars in the worst case, which have already happened in
Africa.
Reduction of private and public incentives to accumulate human capital is due to the
fact that societies are oversupplied in cash coming from the exploitation of natural resources
and may be tempted to underrate the long run value of education. Empirical evidence shows
that across countries school enrolment at all levels is inversely correlated to earnings from
natural resource exploitation (Gylfason et al., 1999). However, there are some exceptions
from this rule (Botswana for instance) where the rent stream from natural resource
exploitation enables citizens to give a higher priority to education.
The decrease of private and public incentives to save and invest takes place because
the demand for capital falls when the share of output to the owners of natural resources rises.
This leads to the decrease of real interest rates and thereby obstructs economic growth
(Gylfason, Zoega, 2001). Also, unproductive investments may seem easy to governments or
individuals who have large amount of cash due to earnings from natural resource exploitation.
The key symptom of the “Russian Disease” is a strong and positive correlation of oil
prices and Gross Domestic Product (GDP) growth. There also exists the positive correlation
of oil prices and the manufacturing growth. This makes the Russian economy different from
the economies suffering from the “classic” Dutch Disease and suggests high potentials for the
Russian economy. However, the fact that the GDP growth is highly oil-dependent brings the
danger of high instability in the economy. That is reinforced by the circumstance that the
increase of Russian export earnings fully depends on the price of oil, as the amount of
5
The name “Dutch Disease” was first used by the magazine “The Economist” to explain the shrinkage of manufacturing sector in the
Netherlands after the discovery of a large natural gas deposits in late 1950s and its intense extraction in 1960s and early 1970s. The theory
explaining mechanism of the Dutch Disease was formulated by Corden and Neary (1982).
6
For the review see, for example: Ismail (2010) and Kojo (2015).
7
As it has been shown (Lartey et al., 2008) the appreciation effect operates even more strongly under fixed exchange rate regime then under
float exchange rate regime.

Y. Bilan, S. Gedek, G. Mentel
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TRANSFORMATIONS IN BUSINESS & ECONOMICS, Vol. 17, No 3 (45), 2018
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production and export tends to stabilize or even decrease (Covi, 2014; Oomes, Ponamorenko,
2015; Tabata, 2012; Vatansever, 2010; Tyll et al., 2018).
The appreciation of currency is the most destructive effect of the Dutch Disease
(Lartey et al., 2008; Magud, Sosa, 2010). It affects economy very deeply, as it generates
factor reallocation, reduces manufacturing output and exports, and increases imports
8
. These
are the long run effects. There is some evidence that this phenomenon disturbs the Russian
economy (Dülger et al., 2013; Mamedov et al., 2016; Chuprov, 2016). On the other hand, the
decrease of oil price (which can result in the depreciation of Ruble) will hurt the Russian
economy as it will reduce the export income as well as it will affect the economy in the short
run. The purpose of the paper is to study the relation of Ruble exchange rate – oil price, as this
relation seems to be crucial for the Russian economy. The stronger the relation is, the tighter
is the gap.
2. Data and Methodology
The data set is comprised of the time series of Brent oil prices expressed in USD per
barrel, and the US dollar - Russian ruble exchange rate (USD/RUB). The data frequency is
weekly, covering the period from 01.07.2005 to 15.12.2017, so the time series length is 651
items. The source of the data is Reuters statistics published on the website stooq.com. Figure
1 shows the graph of the indices of Brent oil prices and the Russian ruble - US dollar
exchange rate over time.
Source: created by the authors.
Figure 1. Brent Oil Price (USD/barrel) and RUB/USD Exchange Rates (indices 01.07.2005 = 100)
A simple look at Figure 1 shows a similar variation pattern of Brent oil price and the
RUB/USD exchange rate. That allows to formulate the hypothesis that the appreciation of the
Russian ruble against the US dollar is correlated with the increase of oil price. Nevertheless,
any hypothesis based on graphs has to be verified by a statistical or econometric model.
The period subjected to the analysis was divided into three sub-periods to indicate any
difference in relation between the Brent oil price and RUB/USD exchange rate:
I – 01.07.2005 - 30.06.2008 (157 observations);
8
Some associate the Dutch Disease only with the effects of the appreciation of the currency. The other channels of transmission from
extensive exploitation of natural resources to slow economic growth listed above are assumed to be a part of the broader issue of the
“Resource Curse” (Stevens et al., 2015).

Y. Bilan, S. Gedek, G. Mentel
ISSN 1648-4460
Empirical Investigation on Economic Roles of Price
TRANSFORMATIONS IN BUSINESS & ECONOMICS, Vol. 17, No 3 (45), 2018
199
II – 01.02.2009 - 30.06.2014 (282 observations);
III – 01.01.2015 - 15.12. 2017 (155 observations).
The purpose of this division was the need for testing the hypothesis that the relation
between the Brent oil price and the RUB/USD exchange rate vary over time.
The econometric analysis of relation between two time series is exposed to the danger
of so-called spurious correlation, which is sometimes called “nonsense correlation”. Spurious
correlation (or spurious regression) means obtaining results that suggest the presence of
significant relationships among time series variables when in fact no such relationship is
present in the data-generating process (population) under study. Such a danger is present
when a pair of variables, for example, xt and yt, are both non-stationary, i.e., show trends
and/or non-constant variance. It is the case of both time series analysed here. To avoid the
danger of the spurious correlation by Engle and Granger (1987), methodology has to be
applied
9
. The details of that methodology can be found, for example, in the concise Lütkepohl
and Krätzig handbook (2004) or in a comprehensive book of Lütkepohl (2007).
The Engle-Granger methodology of time-series analysis consists of 5 steps:
1. Stationarity testing
2. Cointegration analysis when non-stationarity is identified in the first step.
3. Estimation of econometric model (the results of cointegration analysis determine the
type of model used to describe the relationship of time series).
4. Causality analysis
10
based on the results of the econometric model estimation,
obtained in the previous step.
5. The analysis of impulse response functions (IRF), obtained on the base of the results
of Step 3 and Step 4.
These common elements of the Engle-Granger methodology can be enhanced by the
analysis of the forecast error variance decomposition. This analysis points to the potential
sources of variation of each variable, depending on the time horizon.
The ADF and KPSS tests
11
were used for stationarity testing (see Maddala, Lahiri,
2009, pp.555-563). The Johansen methodology was applied for cointegration analysis (see
Lütkepohl, 2005, pp.327-343). The Wald variant of the F-test was used in the causality
analysis
12
. This test is originally used to find whether the inclusion of a variable or a set of
variables in the model significantly reduces the model variance of residual, which in fact
answers the same question as the classic Granger causality test. The use of the F-test is much
simpler. Most of statistical packages supporting the regression analysis (e.g. the GRETL
package) routinely provide the F-test statistic values and the type I error probability. The
analysis of impulse response functions was carried out according to the methodology
described by Lütkepohl (2005, pp.51-63). GRETL Statistical package was applied for
estimation of models in step 3 and for calculation of IRF values as well as the variance of the
errors of prediction decomposition.
9
In 2003, Granger and Engle were awarded the Nobel Memorial Prize in Economic Sciences in recognition that they developed the
methodology of non-stationary time series analysis data that had fundamentally changed the way in which economists analyse financial and
macroeconomic data. The Engle-Granger methodology was further improved by Johansen (1988).
10
The concept of “Granger causality” is used here. Granger causality is a term for a notion of causality in time-series analysis. The idea of
Granger causality is such: a variable X Granger-causes Y if Y can be better predicted using the histories of both X and Y than using only the
history of Y.
11
It is generally alleged that stationarity tests have low power. Confirmatory analysis using alternative tests (with the opposing null
hypothesis) is a common remedy. The ADF and KPSS tests are usually used in this type of analysis (see Maddala, Lahiri, 2009, p.560).
12
The details of the F test applied in the regression analysis can be found in any econometrics handbook (see, for example, Maddala, Lahiri,
2009, section 4.10).

Y. Bilan, S. Gedek, G. Mentel
ISSN 1648-4460
Empirical Investigation on Economic Roles of Price
TRANSFORMATIONS IN BUSINESS & ECONOMICS, Vol. 17, No 3 (45), 2018
200
3. Results
In Table 1, the ADF and KPSS test results are presented. Both tests, the ADF test and
the KPSS test, show non-stationarity of analysed time series, which strongly confirms their
non stationarity.
Table 1. The results of time series stationarity tests
Period
Variable
ADF test
KPSS test
Variable levels
First differences
Test statistics
Critical
value
α = 0.05
Test
statistics
p
Test
statistics
p
Variable
levels
First
differences
I
Brent oil
0.6138
0.9902
-7.8485
0.0000
5.172
0.363
0.464
USD/RUB
0.1567
0.9699
-9.2467
0.0000
7.620
0.097
II
Brent oil
-2.1127
0.5360
-11.6938
0.0000
3.474
0.233
0.463
USD/RUB
-1.7495
0.4053
-10.6926
0.0000
1.295
0.043
III
Brent oil
-1.9073
0.3292
-7.7782
0.0000
1.462
0.214
0.464
USD/RUB
-1.5337
0.5140
-10.4619
0.0000
1.567
0.117
Source: own calculations.
Table 2 contains results of Johansen cointegration tests. These results show that the
analysed time series were conjointly stationary in all other sub-periods. That allowed to
estimate the VAR model for all sub-periods.
Table 2. The results of the Johansen cointegration tests
Period
Matrix rank
Eigenvalue
λtrace
p
λmax
p
I
0
0.078021
16.921
0.0784
12.754
0.1969
1
0.026194
4.167
0.0412
4.167
0.0412
II
0
0.077072
30.565
0.0004
22.618
0.0059
1
0.027789
7.948
0.0048
7.948
0.0048
III
0
0.099547
20.126
0.0266
16.253
0.0648
1
0.024678
3.873
0.0491
3.873
0.0491
Source: own calculations.
The basic form of the VAR model (see, for example: Enders, 2010, Chap. 5.5;
Lütkepohl, 2007, pp.357-386) is as follows:
, (1)
where:
xt = [x1t, ..., xmt]T is a vector of observation on the current values of the variables,
dt = [d0t, ..., dkt]T is a vector k+1 of deterministic components of equation (intercept, time
variable, binary variables, etc.),
A0 – is a matrix of parameters in the dt, vector variables,
Ai – is a matrix of parameters in the lagged variables of vector xt, where the number of lags is
equal to r,
et = [e1t, ..., emt]T contains vectors of the model equation residuals.
In the analysis applied in that paper, the xt vector consists of two variables: Brent oil
price and USD/RUB exchange rate. The dt vector contains intercept and time variables. The
basic characteristics of the estimated USD/RUB equation of the model (1) are presented in
Table 3.

Y. Bilan, S. Gedek, G. Mentel
ISSN 1648-4460
Empirical Investigation on Economic Roles of Price
TRANSFORMATIONS IN BUSINESS & ECONOMICS, Vol. 17, No 3 (45), 2018
201
Table 3. Basic characteristics of USD/RUB equations
Parameter
Period
I
II
III
Number of lags
4
5
5
Adjusted coefficient of determination (R2)
0.9964
0.9707
0.9646
Trend
significant
significant
significant
Coefficient of residual autocorrelation
0.0097
-0.0404
0.0665
Type I error probability of portmanteau test (p-value)
0.6403
0.6823
0.1183
Type I error probability of F test (p-value)
0.0162
0.0089
0.0052
Coefficient of mutual error correlation
-0.299
-0.466
-0.609
Source: own calculations.
The results of the estimation shown in Table 3 indicate that the USD/RUB equations
have been formulated correctly for each sub-period. The autocorrelation coefficient of the
residuals of each of the equations is very low, and the p-value of the portmanteau test
13
is
high. Table 3 contains results of the causality tests. Low type I error probability of F test
(p-values lower than 0.05) indicate that the USD/RUB exchange rate is Granger-caused by
crude oil prices.
The coefficients of mutual error correlation are significantly different from zero for
each sub-period. This indicates the existence of links between the equations of crude oil prices
and USD/RUB exchange rates and gives the possibility to specify the IRF function. This will
allow to identify the direction of the impulse coming from the crude oil price to the
USD/RUB exchange rate, the strength of that impulse and its distribution over time.
-2,0
-1,6
-1,2
-0,8
-0,4
0,0
010 20 30 40 50
I
II
III
Source: created by the authors.
Figure 2. Impulse Response Functions of USD/RUB to Brent Oil Price Changes in Each sub period
The graph depicted in Figure 1 shows the IRF functions of USD/RUB to Brent oil
price changes in all the defined sub-periods. The time horizon (expressed in weeks) is stated
on the horizontal axis of the graph depicted in Figure 1. The vertical axis of that graph shows
the RUB/USD reaction caused by the change of oil price equal to 1 USD per barrel.
The analysis of the graph displayed in Figure 2 shows that in all the sub-periods the
increase of Brent oil price was associated with the decrease of USD/RUB exchange rate. This
13
The portmanteau test is used to test the examination of the model correctness. It verifies the overall hypothesis of the presence of residual
autocorrelation in the VAR model. A high probability of type I error (p-value higher than 0.05) indicates the correct structure of the model
(Ljung, Box, 1978).

Y. Bilan, S. Gedek, G. Mentel
ISSN 1648-4460
Empirical Investigation on Economic Roles of Price
TRANSFORMATIONS IN BUSINESS & ECONOMICS, Vol. 17, No 3 (45), 2018
202
means that the growth of Brent price made the Russian ruble stronger. The graph depicted in
Figure 2 also allows to note that this reaction was not only immediate, reaching its maximum
in the 5th week, and declined very slowly. However, the pattern of the relationship between
USD/RUB and crude oil price was different in each sub-period. The reaction in the sub-period
III was much stronger in comparison with the reactions in sub-periods I and II. The reaction of
USD/RUB exchange rate diminished much faster in sub-period III than in two other sub
periods.
The differences in the strength of USD/RUB exchange rate reaction to the change in
crude oil prices in the various periods can be compared by relating the IRF functions to the
impulse response from own side. The comparison is depicted in Figure 3. It shows that in the
two first periods the crude oil price had small effect on the USD/RUB exchange rate as
compared to the effect caused by changes of USD/RUB exchange rate. The opposite impact
was observed in sub-period III. The effect of crude oil price on the USD/RUB exchange rate
was significantly stronger than the effect from own side. It confirms the main conclusion of
the previous analysis.
Sub-period I
Sub-period II
Sub-period III
Source: created by the authors.
Figure 3. Impulse Response Functions of USD/RUB to Brent Oil Price Changes in Sub-periods and to the
Impulse from Own Side (USD/RUB)
Although impulse response functions provide information on the direction, size and
speed of the pass-through of the impulse coming from both the crude oil price and the own
side to the USD/RUB exchange rate, they give no information on the importance of those two
impulses for the variance of that exchange rate. Additional insights into the impact of both
impulses (crude oil and USD/RUB) on the analysed exchange rate and those obtained from
the impulse response functions may be received from variance decompositions. The variance
decompositions of the forecast error specify the percentage contribution of those two impulses
to the variance of the forecast errors of USD/RUB exchange rate. Figure 4 shows this

Y. Bilan, S. Gedek, G. Mentel
ISSN 1648-4460
Empirical Investigation on Economic Roles of Price
TRANSFORMATIONS IN BUSINESS & ECONOMICS, Vol. 17, No 3 (45), 2018
203
decomposition. The graphs displayed there are designed in such a way that on the Y-axis of
the graph the percentage contribution of each impulse to the variance of the forecast errors of
USD/RUB exchange rate are shown and the X-axis gives the time horizon of the forecast.
This graph describes the share of both variables (USD/RUB exchange rate and Brent crude oil
price) in the explanation of USD/RUB exchange rate.
Sub-period I
Sub-period II
Sub-period III
Source: created by the authors.
Figure 4. Variance Decomposition of USD/RUB Exchange Rate
Figure 4 reveals that crude oil price explains a significant proportion of the forecast
error variance of USD/RUB exchange rate in all three sub-periods. That proportion sharply
increased as the forecast horizon was longer. Sub-period III was different from the other sub
periods only because the share of crude oil price was significantly larger. In the long horizon,
the fraction of crude oil price in the variance of USD/RUB exchange rate forecast was close to
80%. The results of this analysis are consistent with the conclusions that have been
formulated as a result of the analysis of the IRF function.
Conclusions
The IRF analysis as well as the analysis of the variance decompositions of the forecast
error lead to the general conclusions:
1. Ruble exchange rate was under influence of oil price.
2. The rise of oil price resulted in Russian ruble appreciation and the fall of oil price
resulted in ruble depreciation.

Y. Bilan, S. Gedek, G. Mentel
ISSN 1648-4460
Empirical Investigation on Economic Roles of Price
TRANSFORMATIONS IN BUSINESS & ECONOMICS, Vol. 17, No 3 (45), 2018
204
3. The pattern of RUB/USD exchange rate and oil price correlation was different in each
sub-period.
The analysis has also shown that the relation was stronger after the 2008 world
financial crisis. This confirms the hypothesis that the Russian Economy shows symptoms of
the Dutch Disease. It also shows that the gap defined in the first section of that paper has been
becoming tighter in the last years.
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Y. Bilan, S. Gedek, G. Mentel
ISSN 1648-4460
Empirical Investigation on Economic Roles of Price
TRANSFORMATIONS IN BUSINESS & ECONOMICS, Vol. 17, No 3 (45), 2018
205
NAFTOS KAINŲ IR RUBLIO KURSO TYRIMAS
Yuriy Bilan, Stanisław Gędek, Grzegorz Mentel
SANTRAUKA
Žaliavinės naftos eksploatavimas ir eksportas yra Rusijos ekonomikos pagrindas. Toks priklausomumas
lemia žalingų ekonominių reiškinių, vadinamų „olandiškąja liga“, atsiradimą. Kenksmingiausias „olandiškosios
ligos“ poveikis yra valiutos kurso ir žaliavinės naftos kainos priklausomybė. Straipsnio tikslas – apibrėžti naftos
kainos įtaką rublio ir JAV dolerio kursui. Tyrimas pagrįstas „Brent“ naftos kainų laiko eilutėmis JAV doleriais
už barelį ir USD / RUB kursą. Duomenys buvo gaunami kas savaitę nuo 2015 m. liepos mėn. iki 2017 m.
gruodžio mėn. Laiko eilučių trukmė buvo 651 stebėjimas. Tyrimas buvo padalintas į tris laikotarpius: 2005-07-
01 – 2008-06-30, 2009-02-01 – 2014-06-30 ir 2015-01-01 – 2017-12-15. Tyrimui pasitelktas vektorinės
autoregresijos (VAR) modelis, sukurtas pagal Engle-Granger metodiką. Tyrimo rezultatai atskleidė, kad naftos
kainos pokytis visuose prieš tai apibrėžtuose laikotarpiuose turėjo įtakos USD / RUB kursui. Paaiškėjo, kad
naftos kainos įtaka USD / RUB kursui buvo didesnė po 2008 m. Pasaulio finansų krizės. Tai patvirtina teoriją,
kad Rusijos ekonomikai būdingi „olandiškosios ligos“ simptomai.
REIKŠMINIAI ŽODŽIAI: Rusijos ekonomika, žaliavinė nafta, vektorinės autoregresijos (VAR) modelis,
valiutos kursas.