Ruble crisis: the impact of sanctions and oil price drop on the economy of Russia

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Выпускная квалификационная работа

Ruble Crisis: The Impact of Sanctions and Oil Price Drop on the Economy of Russia

Автор:

М.А. Ступин

Москва, 2020 г



The end of 2014 until the beginning of 2016 was a grim period for Russia. A rapid decline in the oil prices became a blow for the commodity-dependent economy of Russia. On the other hand, a large-scale political crisis lead to a list of sanctions imposed on Russia, inflicting damage both to the economy and reputation. All of these events caused a massive financial crisis and an abrupt fall of ruble and this study aims to evaluate the effects of both oil price shock and sanctions.

Russia is one of the world's largest crude oil exporters. It produced more than 10.75 mln barrels per day exported up to 5 mln barrels per day in 2018 and the export volumes have been increasing since the early 1990s. Also oil exports accounted for a large share, which is estimated to be up to one third, of Russian GDP. These facts hint that Russian economy might heavily rely on oil exports and oil price, in particular, the ruble exchange rate might consistently depend on the oil price dynamics.

The fact that a major share of Russian GDP is attributed to commodities exports (primarily, oil and gas) hints that Russia might be subject to the Dutch disease. Mironov and Petronevich (2015) found empirical evidence that the real effective exchange rate is affected by export revenues and thus by oil prices. In January 2014 the price of Brent crude oil was approximately 107 dollars per barrel, while one dollar cost approximately 34 roubles. In January 2016 the dollar exchange rate peaked at approximately 78 roubles, while the Brent oil price per barrel reached its minimal value of approximately 32 dollars per barrel. Similar dynamics suggests that these variables might be strongly correlated.

On the other hand, in 2014 Russia got involved in a political crisis, which lead to several countries imposing sanctions, including economic and political acts. The list of countries that introduced sanctions against Russia includes the world's most economically advanced ones, many of which are Russia's major trade partners and oil exporters: the European Union, Japan, the United States, Canada. The sanctions were directed against Russia's largest banks, oil companies, Russian individuals and specific measures were undertaken against the Crimea region. Obviously, these measures had an impact on the economy of Russia.

Moreover, Russia reacted symmetrically and introduced counter-sanctions later in 2014. In March, 2014 the initial counter-measures were introduced and consisted mainly of political acts including entry bans and sanctions against several American and Canadian citizens. Later economic counter-sanctions were imposed, including embargoes on several countries and ban on imports from them. Of course, these measures impacted on both Russia and the countries affected by counter-sanctions and it might have impacted on the exchange rates.

The Central Bank of Russia initially did not intervene, but when the ruble dropped drastically on December 16th, 2014, the Central Bank raised its annual key interest rate from 10.5% to 17% and bailed out a Russian bank the following week. Also the Central Bank had to inject a lot of foreign currency to prevent ruble from further decline, resulting in a decrease of foreign exchange reserves of Russia by approximately 25% over the course of 9 months. Since 2015 and up until recently the Central Bank has been lowering the annual key interest rate gradually from 17% to 6% (as of February 10th, 2020) and restoring the foreign exchange reserves.

Literature review

Impact of commodity prices on the real exchange rates of the exporting countries has been widely studied in the economics literature. One of the most cited articles researching the topic was written by Chen and Rogoff (2003). The authors study Australia, New Zealand and Canada as these countries depended heavily on exporting primary commodities at the time. Using OLS and GMM-IV analysis, the researchers found out that real commodity prices significantly affect the real currency rates for Australia and New Zealand. However, the results turned out to be not robust to de-trending for Canada. Later Clements and Fry (2008) also studied Australia, Canada and New Zealand and suggested that the causal relationship between the real exchange rates and the commodity prices might be more complex. Chen, Rogoff and Rossi (2008) using multivariate analysis found that the exchange rates of export-dependent countries' currencies predict global commodity prices well both in- and out-of-sample, the authors also suggested that the effect of exchange rates on commodity prices might exceed the prices' effect on exchange rates. However, this paper was later overturned by Bork, Kaltwasser and Sercu (2014). Bork, Kaltwasser and Sercu using data at a higher frequency (that is, daily and monthly data instead of quarterly) did not find any evidence of predictive power of exchange rates on commodity prices both in-sample and out-of-sample. Cashin et al. (2004) studied a much wider range of commodity-dependent countries and allowed for structural breaks. The authors found robust significant results which support the hypothesis of long-run co-movement of the real exchange rates and the real commodity prices. MacDonald and Ricci (2004) estimated a long-run path for the real exchange rate of South African Rand and found significant impact of real commodity prices on it.

The aforementioned literature only considered the supply side of the non-fuel commodity markets. However, in the fuel commodity markets the commodity price for the importers might be just as vital for the exchange rates as for the exporters. Amano and van Norden (1998) consider Germany, Japan and the USA - major crude oil importers over the 1973-1993 period. The authors found a significant one-way causal relationship - real domestic oil prices impact on the real exchange rates. The researchers explain the dependency by suggesting that oil prices reflect the terms of trade, due to high negative correlation between the lagged oil prices and the ratio between the unit value of exports and the unit value of imports (used as a measure of the terms of trade). The majority of the studies made on the relationship between the real exchange rate and commodity prices commonly consider low-frequency data and aim to detect a long-run relationship. In contrast to them Kohlscheen, Avalos and Schrimpf (2016) analyze high-frequency (daily) data to find a statistically significant short-term dependency.

Korhonen and Juurikkala (2009) consider the relationship between the real exchange rates and commodity prices specifically in the oil market and the OPEC countries in the period from 1975 to 2005. The researchers discovered that for oil exporters there is a significant positive effect of oil price increase on the country's currency real exchange rate (currency appreciation). Habib and Kalamova (2007) consider specifically three main oil-exporting countries: Norway, Saudi Arabia and Russia. The authors find a significant long-run relationship between the real oil price and the real exchange rate for Russia. However, for both Saudi Arabia and Norway the relationship is insignificant. The authors suggest that is because oil revenues are “sterilized” in these economies.

The literature on the impact of sanctions on the exchange rate is scarce due to the ad hoc nature of introduction of sanctions, which are usually targeted at restricting rights and activities of certain organizations and people. Nonetheless, Wang et al. (2019) released a study, attempting to evaluate the impact of the US and the EU sanctions on 23 targeted countries from 1996 to 2015, including Russia. The authors conclude that the EU sanctions and sanctions intensity have a positive impact on the exchange rate volatility, while the effect of the US sanctions is insignificant. Several articles considered the impact of the economic sanctions on GDP. Neuenkirch and Neumeier (2015) studied 160 countries over the period 1976-2012, 67 of which experienced economic sanctions. The authors discover that imposition of the UN sanctions decreases per capita GDP growth by at least 2pp. The most harmful sanction, according to the article, is the total embargo by all of the UN members. The US sanctions in comparison to the UN sanctions were found to be much less harmful to the sanctioned country and the damage inflicted to it depends on the geographical closeness to the US.

A few articles studied the effects of the negative oil price shock and the sanctions in the case of Russia in 2014-2016 specifically. Gurvich and Prilepsky (2015), for example, in their work consider the effects of sanctions and oil price drop on Russian GDP. The authors estimated the GDP decrease by 2017 due to sanctions to be approximately -2.4 pp, but this is still 3.3 times lower than projected losses due to oil prices reduction. Kholodilin and Netљunajev (2019) studied the sanctions of the European Union on Russia. The authors using VAR analysis concluded that the effect of sanctions on the growth rate of Russian GDP was weak. Furthermore, the authors found the effect of sanctions on the exchange rate also negligible, as well as the effect of sanctions on the economies of the euro area. Bondarenko (2017) also does not find any significant effect of the sanctions on the ruble controlling for the oil price changes.

Data description

In contrast to many of the existing studies, where monthly and quarterly data was considered, the focus in this work will be on high frequency data, that is, daily and hourly data. High-frequency data allows identifying (at least to some extent) the direct impact of one variable on another.

The primary source for the currency nominal exchange rates, Brent oil prices and stock market indices is Finam. Finam provides the intraday data with frequency from 1 minute to 1 hour. In this study hourly data is chosen because higher frequency data might lack a significant amount of observations. Furthermore, higher frequency data is subject to more errors in variables. Hourly data should be good enough for high-frequency identification, while the consumer price indices are not calculated at such frequency, thus nominal exchange rates are used instead of real exchange rates. The data covers the timespan from December 4th , 2013 (the earliest date at which both variables are available) up until January 31st, 2020. The hours on which one or both variables are unavailable are excluded, that is, the data covers the trading days from 11 AM to 6 PM.

Another source for the currency nominal exchange rates and Brent oil prices at daily frequency is investing.com. The data is covers the period of time from January 1st , 2013 to January 31st, 2020.

Following Bondarenko (2017), I take the Google Trends data to identify the impact of the sanctions on the ruble exchange rate. However, this data has a few drawbacks. Firstly, the data is calculated by a Google algorithm, which is unknown and which might have changed over time. Secondly, the daily data is only available in semi-annual packages, and the data over a larger timeframe is monthly only. The daily values of the index provided are relative to the period they are included in. To deal with this I do the same procedure as Bondarenko (2017), that is, I take daily values subsequently for each half a year since December 4th, 2013 up until January 31st, 2020, then I multiply each day's value by the corresponding month's value and divide it by 100. Thirdly, the daily data is extremely volatile and might inaccurately reflect the actual impact of sanctions on the economy. Finally, the data depends on the query itself and does not allow to isolate the counter-sanctions from sanctions.

Model description

Firstly, I estimate a simple model to estimate the direct impact of oil price dynamics on the exchange rate using hourly data.

m

Where RUB/USDt is the RUB/USD nominal exchange rate hourly, Brentt is the Brent oil price hourly. I also test for several specifications of the model, including several lags of changes in the Brent oil price.

On November 10th, 2014 the Central Bank of Russia introduced a fully floating exchange rate of ruble and announced it would only intervene to suppress speculations. On December 16th, 2014, when the speculations were at the peak, the Central Bank intervened and raised the key interest rate from 10.5% to 17%. To see whether these actions had a significant impact I estimate the following model:

m

Where Itfloat is equal to 1 if t is later than 11 AM November 10th, 2014, and Itint is equal to 1 if t is later than 1 PM December 16th, 2014. These are the dates on which the Russian Central Bank made the announcements, suggesting that the dependency of the nominal exchange rate on the Brent oil price and sanctions might have changed on these dates. Therefore, in this model I allow for structural breaks at the announcement dates. According to the results of the previous regression, appropriate amount of lags which significantly impact the nominal exchange rate are also included in this model.

To estimate the impact of sanctions on the nominal exchange rate, the following model is introduced:

m

Here the data is on the daily frequency, sanctions is the Google Trends data.

Following the same logic as for the first group of models, I estimate a similar model, but allowing for structural breaks at the dates of introduction of the fully floating rate and the intervention of the Russian Central Bank. Moreover, since the announcement date I allow for asymmetric impact of the oil price on the RUB/USD nominal exchange rate, because the Central Bank's actions suggest that there might be an asymmetric effect of oil price changes.

m

Here again several specifications with different amounts of lags included are tested.

To estimate daily ruble elasticities on Brent oil price for each year I run separate regressions for each year 2012-2020 similar to the initial model but with daily data, allowing for an asymmetric impact of the oil price of the RUB/USD nominal exchange rate. Then I repeat the exercise for years 2014-2019 (years for which the data on the whole year is available) using hourly frequency, again, allowing for an asymmetric impact.

Expected results and possible arguments for causality

Normally we should expect the oil price to be a significant determinant of the ruble nominal exchange rate since Russia is a major crude oil and oil products exporter. Russian oil companies sell crude oil and oil products abroad, receiving revenues in dollars. Since their costs are in rubles, they create demand for ruble causing ruble appreciation. Since the budget of Russia is heavily dependent on taxes on the oil export revenues, oil price drop negatively impacts on attractiveness of the Russian economy as a whole. Therefore, we should expect increases in the Brent oil price to positively impact on the RUB/USD exchange rate and ruble appreciation. Moreover, the elasticity of the RUB/USD nominal exchange rate on the Brent oil price might be correlated with the degree of dependency of Russian economy on oil revenues.

On the other hand, the economic sanctions imposed on Russia were designed to damage the Russian economy, therefore, a negative impact of sanctions on the RUB/USD nominal exchange rate is expected. The economic sanctions include trade restrictions, so Russian exporters might have suffered from them and the demand for ruble shortened. The sanctions might have hurt Russia's image on the international scene, damaging its potential for future investments. The sanctions might have also boosted speculations on the financial market.

Considering the endogeneity concerns, there are no reasons to believe sanctions imposed on Russia anyhow depend on the nominal exchange rate. Thus it will be fair to assume that sanctions are exogenous. As for the Brent oil price, there might be some endogeneity concerns since Russia has a decent share in the world oil market. However, the existing literature that studied the impact of the commodity exporters' currency rates on the market commodity prices either have not found any significant impact or has been overturned. Moreover, Russia's market power on the world oil market is not that high.

Results and discussion

The Table 1 presents the OLS regression results of changes of the logarithm of the RUB/USD exchange rate on different lags in changes of the logarithm of the Brent oil price.

Table 1. Determinants of the nominal RUB/USD exchange rate.

	(1)	(2)	(3)
VARIABLES	OLS	OLS	OLS
Дln(Brentt)	0.25***	0.25***	0.25***
	(0.02)	(0.02)	(0.02)
Дln(Brentt-1)		0.01**	0.01**
		(0.01)	(0.01)
Дln(Brentt-2)			0.01
			(0.01)
Observations	11,599	11,598	11,597
R-squared	0.22	0.22	0.22
Time Fixed Effects	Included	Included	Included

Newey-West robust standard errors in parentheses, dependent variable - Дln(RUB/USDt), hourly data used

The results show that the same hour change in the Brent oil price is a significant (at 5% level) determinant of the RUB/USD nominal exchange rate. A 1% increase in the Brent oil price leads to approximately 0.25% increase of the RUB/USD nominal exchange rate. The first lag of the change in the Brent oil price is also significant at 5% level, while the second lag and all the further lags are insignificant. Table A1 reports the regression results for specifications with up to 6 lags of the Brent oil price changes. With addition of the further lags the coefficients on the previous lags almost do not change. Time fixed effects for each year, each trading day of the year, each month, each trading hour and each day of the week are included in the regression. Heteroskedasticity and autocorrelation robust standard errors are used in the regression.

The Table 2 presents the results of the OLS regressions of the changes of the nominal RUB/USD exchange rate on the lagged Brent oil price, controlling for structural breaks at the dates of the aforementioned Central Bank announcements.

financial price ruble dollar



Table 2. Determinants of the nominal RUB/USD exchange rate allowing for structural breaks.

	(1)
VARIABLES	OLS
Дln(Brentt)	0.08
	(0.05)
IfloatДln(Brentt)	0.50***
	(0.08)
IfloatIintДln(Brentt)	-0.33***
	(0.06)
Дln(Brentt-1)	0.04**
	(0.02)
IfloatДln(Brentt-1)	0.16
	(0.10)
IfloatIintДln(Brentt-1)	-0.19*
	(0.10)
Observations	11,598
R-squared	0.24
Time Fixed Effects	Included

Newey-West robust standard errors in parentheses, dependent variable - Дln(RUB/USDt),

Addition of the second and further lags almost does not change the results of the regression, thus the specification with one lag is chosen. Table A2 reports the results of the regressions with two lags included. Before the introduction of the fully floating exchange rate the coefficient on the hourly ruble elasticity on oil price was significant, but low in the absolute value. During the period between 11 AM November 10th, 2014 and 1 PM December 16th, 2014 the elasticity drastically increased in the absolute value. After 1 PM December 16th, 2014 the elasticity moved in the opposite direction. Also note that all the lags have significantly different coefficients during the specified time periods. Time fixed effects for each year, each trading day of the year, each month, each trading hour and each day of the week are included in the regression. Heteroskedasticity and autocorrelation robust standard errors are used in the regression.

Table 3. Determinants of changes of the RUB/USD nominal exchange rate.

	(1)
VARIABLES	OLS
Дln(Brentt)	0.25***
	(0.02)
sanctionst	-0.33
	(0.37)
Дln(Brentt-1)	0.01
	(0.01)
sanctionst-1	-0.35
	(0.43)
Дln(Brentt-2)	0.02
	(0.01)
sanctionst-2	0.62*
	(0.36)
Observations	2,050
R-squared	0.38
Time Fixed Effects	Included

Newey-West robust standard errors in parentheses, dependent variable - Дln(RUB/USDt),

A specification with two lags is chosen since the coefficients almost do not change with addition of new lags. Table A3 presents all the regression results for all the specifications. Time fixed effects for each year, each trading day of the year, each month, and each day of the week are included in the regression. Heteroskedasticity and autocorrelation robust standard errors are used in the regression. Firstly, we see that the resulting daily nominal exchange rate elasticity on the Brent oil price is close to the results found for the hourly elasticity, that is, if the oil price increases by 1%, the RUB/USD nominal exchange rate decreases by 0.25%. Addition of lags almost does not change the coefficient on the change of logarithm of the oil price. However, in this model with daily data the first lag of the changes in the Brent oil price is no longer significant. Moreover, addition of further lags affects the coefficients on lags of the changes of the Brent oil price. Another interesting result is that the impact of the same day sanctions is positive and significant at 10% level. However, introduction of further lags of sanctions eliminates this significance. In the specification that includes lags up to three days back no lags of sanctions are individually significant. To conclude whether the sanctions significantly impact on the RUB/USD nominal exchange rate, I conduct an F-test on the seventh model with the null-hypothesis that coefficients on all the lags of sanctions (sanctionst, sanctionst-1, sanctionst-2) are all equal to zero. The F-test returned the p-value of 0.2488, which means that the hypothesis that sanctions had no impact on the RUB/USD nominal exchange rate is not rejected.

Table 4. Determinants of changes of the RUB/USD nominal exchange rates allowing for structural breaks.

	(1)
VARIABLES	OLS
Дln(Brentt)	0.20***
	(0.03)
IfloatДln(Brentt)	0.29**
	(0.14)
I(ДBrentt>0)IfloatДln(Brentt)	0.00
	(0.05)
IfloatIintДln(Brentt)	-0.24*
	(0.14)
sanctionst	-0.13
	(0.35)
Ifloatsanctionst	-8.22*
	(4.21)
IfloatIintsanctionst	8.15*
	(4.44)
Дln(Brentt-1)	0.04
	(0.02)
IfloatДln(Brentt-1)	0.29
	(0.18)
I(ДBrentt>0)IfloatДln(Brentt-1)	-0.04
	(0.03)
IfloatIintДln(Brentt-1)	-0.31*
	(0.18)
sanctionst-1	0.07
	(0.35)
Ifloatsanctionst-1	8.18**
	(3.83)
IfloatIintsanctionst-1	-9.50**
	(3.93)
Observations	2,051
R-squared	0.40
Time Fixed Effects	Included

Newey-West robust standard errors in parentheses, dependent variable - Дln(RUB/USDt), daily data used

Table 4 presents the regressions results of the sanctions and changes in the Brent oil price on the changes of the RUB/USD nominal exchange rate in presence of structural breaks. Time fixed effects for each year, each month, each trading day of the year, and each day of the week are included in the regression. Heteroskedasticity and autocorrelation robust standard errors are used in the regression. The regression shows that the daily elasticity of the RUB/USD nominal exchange rate on the oil price is significantly different in each specified period. Moreover, the first and the second lags also have significantly different coefficients before introduction of the fully floating rate, after introduction and after intervention of the Russian Central Bank. All the coefficients on the lagged and non-lagged changes of the Brent oil price have similar dynamics: at first they were low, but after introduction of the fully floating rate the coefficients increased in the absolute value and after the intervention they returned back almost to the original values. The same holds for the lags of sanctions. The coefficients on the sanctions were insignificant, followed by a significant decrease and then by a significant increase, which are equal in the absolute value. Therefore, the sanctions significantly impacted on the RUB/USD nominal exchange rate during the brief period of time when depreciation of the ruble was the most intense.

Table 5. Daily RUB/USD nominal exchange rate elasticities on the oil price for each year 2012-2019.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	2012	2013	2014	2015	2016	2017	2018	2019
VARIABLES	OLS	OLS	OLS	OLS	OLS	OLS	OLS	OLS
Дln(Brentt)	0.31***	0.23***	0.29**	0.34***	0.31***	0.25***	0.16***	0.11***
	(0.05)	(0.07)	(0.13)	(0.07)	(0.04)	(0.04)	(0.04)	(0.03)
I(ДBrentt>0)Дln(Brentt)	-0.08	-0.16	0.10	-0.02	0.00	-0.18**	-0.05	-0.06
	(0.09)	(0.11)	(0.32)	(0.10)	(0.08)	(0.09)	(0.11)	(0.05)
Observations	257	257	258	257	250	249	248	249
R-squared	0.47	0.27	0.26	0.52	0.59	0.29	0.27	0.33
Time Fixed Effects	Included	Included	Included	Included	Included	Included	Included	Included

Newey-West robust standard errors in parentheses, dependent variable - Дln(RUB/USDt),

Table 5 presents daily elasticities of the RUB/USD nominal exchange on the Brent oil price for each year allowing for asymmetric effects of Brent oil price increases and decreases. Firstly, the Brent oil price significantly impacts the RUB/USD nominal exchange rate regardless of the year. This is plausible because Russia is heavily dependent on the oil exports. However, the results show that the elasticity is not stable and changes from year to year. This suggests that the elasticity depends on other macroeconomic variables which are not considered in this research. It is notable that the elasticity is the highest in the absolute value during the peak crisis years - 2014-2016. After the crisis years the elasticity remains lower than prior to the crisis. The dynamics of the coefficient on the product of a dummy that indicates an increase in the Brent oil price and the change in the logarithm of the Brent oil price. From the regressions we see that no significant asymmetric effect was found in any years except 2017. In 2017 an increase in the Brent oil price lead to a significantly lower increase in the RUB/USD nominal exchange rate than a decrease in the RUB/USD nominal exchange rate caused by a similar decrease in the Brent oil price. What is also important is the dynamics of the R-squared. R-squared signals by what extent the RUB/USD nominal exchange rate is explained by the oil price and other factors. It does not seem to follow a certain trend; however, R-squared reaches its highest values in years 2015-2016, again, when Russia was under the most intense economic pressure of sanctions.

Table 6. Hourly RUB/USD nominal exchange rate elasticities on the oil price for each year 2014-2019.

	(1)	(2)	(3)	(4)	(5)	(6)
	2014	2015	2016	2017	2018	2019
VARIABLES	OLS	OLS	OLS	OLS	OLS	OLS
Дln(Brentt)	0.42***	0.32***	0.25***	0.17***	0.10***	0.08***
	(0.10)	(0.04)	(0.03)	(0.03)	(0.02)	(0.02)
I(ДBrentt>0)Дln(Brentt)	-0.05	-0.08	0.04	-0.06	0.05	-0.03
	(0.30)	(0.06)	(0.05)	(0.04)	(0.06)	(0.03)
Observations	1,887	1,895	1,887	1,895	1,871	1,887
R-squared	0.11	0.24	0.40	0.13	0.08	0.09
Time Fixed Effects	Included	Included	Included	Included	Included	Included

Newey-West robust standard errors in parentheses, dependent variable - Дln(RUB/USDt),

Table 6 presents hourly elasticities of the RUB/USD nominal exchange on the Brent oil price for each year. In this exercise the main results for the daily elasticities hold, that is, for each year the Brent oil price is a significant in-sample determinant of the RUB/USD nominal exchange rate, the elasticity is the highest in the absolute value during the crisis years 2014-2016, and the null hypothesis that there is no asymmetric impact of increases and decreases in the Brent oil price is not rejected for all the years. And the R-squared follows the same pattern as for the daily elasticity and is at its peak during years 2015-2016.

One of the possible explanations to the changing elasticity of the nominal RUB/USD exchange rate on the Brent oil price is that the degree of oil dependency of Russia on oil revenues also has been changing throughout the years. Graph B4 presents the dynamics of the hourly and daily RUB/USD nominal exchange rate elasticities together with the share of oil revenues in the total exports of Russia. From the graph we can see that both hourly and daily elasticities follow a similar pattern - they have been monotonously decreasing since 2014. However, the share of oil revenues in the total exports of Russia changed only slightly throughout the years and it does not seem like the nominal exchange rate elasticity is anyhow correlated with the share of oil revenues in the total exports of Russia. So this explanation does not seem plausible.

As a robustness check, I test the hypothesis that the sanctions were significantly impactful during a short period between the structural breaks. For this I use the data on the Russian stock market indices - RTS and the Moscow Stock Exchange index. Using the data for the same period as for the nominal exchange rate models, that is, January 1st 2012 - January 31st 2020, I estimate a similar model that allows for the structural breaks at the dates of the aforementioned Central Bank announcements and includes all the time fixed effects: for each year, each month, each day of the month and each day of the week. Table 7 presents the results of the regressions.

Table 7. Estimation of the impact of sanctions on the stock exchange indices.

	(1)	(2)	(3)	(4)
	OLS	OLS	OLS	OLS
VARIABLES	Дln(RTS)t	Дln(MosEx)t	Дln(RTS)t	Дln(MosEx)t
Sanctionst	-0.17	0.19	-0.15	0.05
	(0.63)	(0.46)	(0.81)	(0.59)
IfloatSanctionst			-16.44**	-5.80*
			(7.47)	(3.15)
IintIfloatSanctionst			16.26**	6.22*
			(7.75)	(3.23)
Observations	2,033	2,033	2,033	2,033
R-squared	0.02	0.03	0.03	0.03
Time Fixed Effects	Included	Included	Included	Included

Robust standard errors in parentheses, daily data used

From the regression results we see that the general results hold: the sanctions significantly negatively impacted on the stock indices values during a short period between the announcement dates. The effect is significant at 5% level for the RTS index and at 10% level for the Moscow Stock Exchange index. This suggests that since the effect of sanctions is significant for both the exchange rate and the indices values, the sanctions inflicted reputational damage and boosted speculations on the stock exchanges.



Conclusion

The same period changes of the Brent oil price significantly impacts on changes of the RUB/USD nominal exchange rate. Besides that, the first and the second lag of changes of the Brent oil price also significantly influences on the RUB/USD nominal exchange rate (at 5% and 10% levels respectively). This study finds that sanctions significantly impacted on the RUB/USD nominal exchange rate at least during the period of time between November 10, 2014 and December 16, 2014, when depreciation of the ruble was the sharpest. However, outside this period of time, I have not found any significant of sanctions on the RUB/USD nominal exchange rate. This result contradicts the previous research on the topic, in which no effect of sanctions had been found. This study also finds that there are structural breaks at the dates when the Russian Central Bank introduced the fully floating exchange rate of ruble and when the Russian Central Bank intervened and raised the key interest rate. The daily elasticity of RUB/USD nominal exchange rate on the Brent oil price through the whole considered period is estimated to be equal to approximately -0.25 and is robust to addition of further lags of changes of Brent oil price. This result is obtained both on hourly and daily data. However, further analysis showed that the elasticity was not constant through the whole period. Hourly and daily elasticities of the RUB/USD nominal exchange rate on the Brent oil price have similar dynamics throughout years 2012-2019, that is, the elasticity is the highest in the absolute values during the crisis years 2014-2016, and after the crisis the elasticity of the RUB/USD nominal exchange rate remains lower than it had been prior to the crisis.



References

1. Amano, Robert A., and Simon Norden. 1998. “Exchange Rates and Oil Prices.” Review of International Economics 6(4): 683-694.

2. Bondarenko, Dmitry. 2017. “Exchange Rates and Resource Dependence: A Study of the 2014 Oil Price Slump. The case of Russia.” Master thesis, New Economic School.

3. Bork, Lasse, Pablo R. Kaltwasser, and Piet Sercu. 2014. “Do Exchange Rates Really Help Forecasting Commodity Prices?” SSRN.

4. Cashin, Paul, Luis Cespedes, and Ratna Sahay. 2004. “Commodity currencies and the real exchange rate.” Journal of Development Economics 75: 239-268.

5. Chen, Yu-chin, and Kenneth Rogoff. 2003. “Commodity Currencies.” Journal of International Economics 60(1): 133-160.

6. Chen, Yu-chin, Kenneth Rogoff, and Barbara Rossi. 2008. “Can exchange rates forecast commodity prices?” Quarterly Journal of Economics 125: 1145-1194.

7. Clements, Kenneth W., and Renee Fry. 2008. “Commodity currencies and currency commodities.” Resources Policy 33(2): 55-73.

8. Gurvich, Evsey, and Ilya Prilepsky. 2015. “The impact of financial sanctions on the Russian economy.” Russian Journal of Economics 1: 359-385.

9. Habib, Maurizio M., and Margarita M. Kalamova. 2007. “Are there oil currencies? The real exchange rate of exporting countries.” European Central Bank Working Paper 839.

10. Juurikkala, Tuuli, and Iikka Korhonen. 2009. “Equilibrium exchange rates in oil-exporting countries.” Journal of Economics and Finance 33: 71-79.

11. Kholodilin, Konstantin, and Aleksei Netљunajev. 2019. “Crimea and punishment: the impact of sanctions on Russian economy and economies of the euro area.” Baltic Journal of Economics 19(1): 39-51.

12. Kohlscheen, Emanuel, Fernando Avalos, and Andreas Schrimpf. 2016. “When the Walk is Not Random: Commodity Prices and Exchange Rates.” BIS Working paper 551.

13. MacDonald, Ronald, and Luca A. Ricci. 2004. “Estimation of the equilibrium real exchange rate for South Africa” South African Journal of Economics 72(2): 282-304.

14. Mironov, Valeriy, and Anna Petronevich. 2015. “Discovering the signs of Dutch disease in Russia.” Resources Policy 46(2): 97-112.

15. Neuenkirch, Matthias, and Florian Neumeier. 2015. “The impact of the UN and US economic sanctions on GDP growth.” European Journal of Political Economy 40A: 110-125.

16. Wang, Yiwei, Ke Wang, and Chun-Ping Chang. 2019. “The impacts of economic sanctions on exchange rate volatility.” Economic Modelling 82: 58-65.



Appendix

Table A1. Determinants of the nominal RUB/USD exchange rate.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
VARIABLES	OLS	OLS	OLS	OLS	OLS	OLS	OLS
Дln(Brentt)	0.25***	0.25***	0.25***	0.25***	0.25***	0.25***	0.25***
	(0.02)	(0.02)	(0.02)	(0.02)	(0.02)	(0.02)	(0.02)
Дln(Brentt-1)		0.01**	0.01**	0.01**	0.01**	0.01**	0.01**
		(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
Дln(Brentt-2)			0.01	0.01	0.01	0.01	0.01
			(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
Дln(Brentt-3)				-0.00	-0.00	-0.00	-0.00
				(0.01)	(0.01)	(0.01)	(0.01)
Дln(Brentt-4)					0.01	0.01	0.01
					(0.01)	(0.01)	(0.01)
Дln(Brentt-5)						-0.00	0.00
						(0.00)	(0.00)
Дln(Brentt-6)							0.01
							(0.01)
Observations	11,599	11,598	11,597	11,596	11,595	11,594	11,593
R-squared	0.22	0.22	0.22	0.22	0.22	0.22	0.22
Time Fixed Effects	Included	Included	Included	Included	Included	Included	Included

Newey-West robust standard errors in parentheses, dependent variable - Дln(RUB/USDt), hourly data used

Table A2. Determinants of the nominal RUB/USD exchange rate allowing for structural breaks.

	(1)	(2)	(3)
VARIABLES	OLS	OLS	OLS
Дln(Brentt)	0.07	0.08	0.08
	(0.05)	(0.05)	(0.05)
IfloatДln(Brentt)	0.49***	0.50***	0.50***
	(0.08)	(0.08)	(0.07)
IfloatIintДln(Brentt)	-0.32***	-0.33***	-0.34***
	(0.07)	(0.06)	(0.06)
IfloatДln(Brentt-1)		0.04**	0.04**
		(0.02)	(0.02)
IfloatДln(Brentt-1)		0.16	0.16
		(0.10)	(0.10)
IfloatIintДln(Brentt-1)		-0.19*	-0.20*
		(0.10)	(0.10)
Дln(Brentt-2)			0.00
			(0.02)
IfloatДln(Brentt-2)			0.11**
			(0.05)
IfloatIintДln(Brentt-2)			-0.11**
			(0.05)
Observations	11,599	11,598	11,597
R-squared	0.23	0.24	0.24
Time Fixed Effects	Included	Included	Included

Newey-West robust standard errors in parentheses, dependent variable - Дln(RUB/USDt), hourly data used

Table A3. Determinants of changes of the RUB/USD nominal exchange rate.

	(1)	(2)	(3)	(4)
VARIABLES	OLS	OLS	OLS	OLS
Дln(Brentt)	0.25***	0.25***	0.25***	0.25***
	(0.01)	(0.02)	(0.02)	(0.02)
sanctionst	-0.24	-0.20	-0.33	-0.39
	(0.31)	(0.38)	(0.37)	(0.40)
Дln(Brentt-1)		0.00	0.01	0.01
		(0.01)	(0.01)	(0.01)
sanctionst-1		-0.07	-0.35	-0.37
		(0.38)	(0.43)	(0.43)
Дln(Brentt-2)			0.02	0.02
			(0.01)	(0.01)
sanctionst-2			0.62*	0.55
			(0.36)	(0.42)
Дln(Brentt-3)				-0.02
				(0.01)
sanctionst-3				0.21
				(0.39)
Observations	2,052	2,051	2,050	2,049
R-squared	0.38	0.38	0.38	0.38
Time Fixed Effects	Included	Included	Included	Included

Newey-West robust standard errors in parentheses, dependent variable - Дln(RUB/USDt), daily data used

Table A4. Determinants of changes of the RUB/USD nominal exchange rates allowing for structural breaks.

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