Corresponding author: Irina Tarasenko ( irina.v.tar@googlemail.com ) © 2021 Non-profit partnership “Voprosy Ekonomiki”.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits to copy and distribute the article for non-commercial purposes, provided that the article is not altered or modified and the original author and source are credited.
Citation:
Tarasenko I (2021) The impact of exchange rate volatility on trade: The evidence from Russia. Russian Journal of Economics 7(3): 213-232. https://doi.org/10.32609/j.ruje.7.57933
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This paper analyzes the effects of exchange rate volatility on exports and imports of a range of goods between Russia and its 70 trading partners from 2004 until 2018. The goods in question fall into eight product categories, as follows: (i) agricultural raw materials; (ii) chemicals; (iii) food; (iv) fuels; (v) manufactured goods; (vi) ores and metals; (vii) textiles; and (viii) machinery and transport equipment. Exchange rate volatility is measured using the standard deviation of the first difference in the logarithmic daily nominal exchange rate. The paper concludes that exchange rate volatility had a negative impact on exports of agricultural raw materials, manufactured goods, and machinery and transport equipment. In contrast, it was found to have a positive and significant impact on trade in fuels and imports of chemicals and textiles.
exchange rate volatility, gravity model, instrumental variable, exports, imports
International trade has dramatically increased in the last 80 years thanks to reductions in shipping and communication costs, globally negotiated reductions in tariffs in the context of multiple rounds of GATT and WTO trade negotiations, the widespread outsourcing of production activities, and a greater awareness of foreign cultures and products. Yet many impediments to trade still remain. In this vein, the link between exchange rate volatility — as a potential barrier to trade — and international trade flows remains a recurrent issue.
The general argument is that exchange rate volatility performs as an impediment to international trade in light of subsequent risks and transaction costs, which act as disincentives to trade. Conversely, for countries with a high level of financial development, the negative impact of exchange rate volatility should be less pronounced as these countries have access to financial instruments sufficient for them to hedge against any volatility shocks, including through the use of forward and option contracts (Dell’Araccia,1999;
This paper builds on earlier research in this area and aims to contribute to the understanding of the relationship between exchange rate volatility and trade and to answer the question of the impact of exchange rate volatility on trade between Russia and its trading partners, between 2004 and 2018, in the following product categories: (i) agricultural raw materials; (ii) chemicals; (iii) food; (iv) fuels; (v) manufactured goods; (vi) ores and metals; (vii) textiles; and (viii) machinery and transport equipment. In contrast to other studies, this paper focuses on the relationship between Russia and its 70 trading partners. The originality of this paper consists in disaggregating trade and evaluating the impact of exchange rate volatility on exports and imports of each of the eight product categories listed above. In addition, an instrumental variable approach has been used given the challenge of a potential reverse causality.
The remaining sections of the paper are organized as follows: section 2 provides a brief review of the literature; section 3 discusses Russia’s trade environment during the period under consideration; section 4 discusses the selected data and empirical strategy and reports the estimated results; section 5 concludes the findings.
Much has been written on the relationship between exchange rate volatility and international trade; indeed, studies on this topic date back to the collapse of the Bretton Woods System in 1971 (
Regarding the measurement of exchange rate volatility, a large number of studies have employed the standard deviation of the first difference of the logarithmic exchange rate, which has the property of being equal to zero if exchange rate follows a constant trend (Dell’Araccia, 1999;
Several models have been used to study the impact of exchange rate volatility on trade flows; however, the most commonly used is the gravity model. This model is largely used because of its strong theoretical foundation (Dell’Araccia, 1999;
Finally, results on the impact of exchange rate volatility on trade do not appear to be unanimous in the literature. The traditional argument for the effect of uncertainty on trade suggests that higher exchange rate volatility acts as a disincentive to trade, diminishing the volume of trade and undermining future profits from international trade transactions (
Other explanations as to why exchange rate volatility exerts less of an impact on trade flows relate to the development of relevant financial instruments on the market (
Following the collapse of the Soviet Union in 1991, the Russian Federation began to transform its economy, moving from a centrally-planned to a market-oriented economy. Having annual growth at an average of 7% over the years 2004 to 2008 was largely due to increasing oil and gas revenues, and mining investment; this economic growth then fell abruptly, down to –7.8%, in the wake of the 2008 financial crisis. In the following years, as a result of higher oil prices and stronger global demand and investment, the Russian economy turned around and expanded by an average of 3%. Nevertheless, another slowdown occurred in 2015, with a decline in consumption and investment in light of the crude oil price slump, market volatility, and policy uncertainty, which together resulted in a negative trend of –2.3%. During the 2016–2018 period, economic growth returned to a modest upward trend of 1.4% on average, driven mostly by mineral resource extraction and non-tradable sectors.
Accounting for an average of 50% of GDP during this period, international trade played an essential role in the Russian Federation’s economy. Indeed, although Russia’s economy was at this time declining in general terms, throughout the 2004–2018 period, Russia enjoyed a current account surplus in its trade in goods. Russia’s terms of trade fluctuated significantly during this period. After modest improvements from 2004 to 2008, its terms of trade declined significantly in 2009. Consequently, following a short rise in 2010–2012, the economy situation began to deteriorate from 2012 onwards. A correlation may be drawn between these developments and the fluctuations in oil prices and the levels of certain other exports, as well as increases in import prices as a consequence of a ban on food imports (
Regarding the Russian Federation’s exchange rates policies, one of the major modifications during this time was a shift from a managed floating to a free floating exchange rate regime.
In November 2014, when market pressures again intensified, the CBR floated the rouble in order to facilitate a more rapid adjustment to external shocks and to curb reserve losses (WTO, 2016). Subsequently, the current regime sets no targets either for the exchange rate level or its fluctuations; it is fully determined by supply and demand on foreign exchange markets. Under normal conditions, the CBR does not intervene to influence the rouble exchange rate. However, developments in foreign exchange markets were monitored and foreign exchange operations conducted to maintain financial stability (CBR, 2017a).
During the 2004–2018 period, the composition of total exports and imports in goods had not significantly changed. With regard to exports, fuel represented 61% of total exports in 2018 (53% in 2004), followed by manufactured goods, at 17%; ores and metals, at 6%; machinery and transport equipment and chemicals, at 5% each in 2018; the respective numbers for 2004 were 23%, 10%, 5%, and 4%. Exports of agricultural raw materials decreased from 4% in 2004 to 2% in 2018 (Fig.
The Russian Federation’s main export markets were the following: the Netherlands (8%); Germany (7%); Italy (7%); Belarus (6%); and Ukraine (6%) in 2004; and China (12%); the Netherlands (10%); Germany (8%); Belarus (5%); and Turkey (5%) in 2018. Russia’s main sources of imports were Germany (14%); Belarus (9%); Ukraine (8%); and China (6%) in 2004, and China (22%); followed by Germany (11%); Belarus (5%); and the United States (5%) in 2018.
This section discusses the data and empirical strategy used in this study. The equations are estimated using: (i) real exchange rate data for the 2004–2018 period; (ii) annual disaggregated data on exports and imports of agricultural raw materials, chemicals, food, fuel, manufactured goods, ores and metals, textiles, and machinery and transport equipment; (iii) real GDP data; and (iv) geographical bilateral and cultural data (common official language, contiguity, and distance) — all for the same 2004–2018 period.
The daily nominal exchange rate data (retrieved from the IMF database) are expressed in Special Drawing Rights (SDRs) per national currency unit available for 71 countries (the Russian Federation and its 70 trading partners; see Appendix A, Table
Real exchange rate volatility (ERvolrit) was calculated employing the following formula (Dell’Araccia, 1999;
ERvolrit = std.dev.[ln(ERrit) – ln(ERrit –1)], (1)
where ERrit indicates the daily real exchange rate between Russia and country i, at time t, where t denotes days. The standard deviation is then calculated over a one-year period.
The relationship between the exchange rate fluctuations and the levels of exports and imports of agricultural raw materials, chemicals, food, fuel, manufactured goods, ores and metals, textiles, machinery and transport equipment, is measured by a gravity model where a set of fixed effects control for all the determinants of trade flows normally included in the standard gravity model specifications. Notably, the impact of volatility on trade is based on the following specifications:
log(Xrit) = β0 + β1 log(ERvolrit) + β2 log(MERrit) + β3 log(GDPit) +
+ β4 FDit + β5 log(ERvolrit) FDit + φi + ωt + εrit ; (2)
log(Irit) = β0 + β1 log(ERvolrit) + β2 log(MERrit) + β3 log(GDPit) +
+ β4 FDit + β5 log(ERvolrit) FDit + φi + ωt + εrit . (3)
Both equations use annual data, where log(Xrit) indicates the logarithm of the level of exports (that is, agricultural raw materials, chemicals, food, fuel, manufactured goods, ores and metals, textiles, machinery and transport equipment) from the Russian Federation to country i in year t, and log(Irit) — logarithm of the level of imports (that is, agricultural raw materials, chemicals, food, fuel, manufactured goods, ores and metals, textiles, machinery and transport equipment) from a country i to Russia in year t; log(ERvolrit) indicates the exchange rate volatility in year t; log(MERrit) indicates the mean value of the exchange rate in year t; log(GDPit) indicates the real GDP of a country i in year t; FDit indicates an OECD-dummy variable, which takes the value of 1 if a country i is an OECD member, and 0 in all other cases;
Exchange rate volatility has generally been considered to have a negative impact on the international flow of goods. In particular, this trend becomes more pronounced when countries switch to floating exchange rates as these are generally more prone to volatility. The underlying assumption is that, if exchange rate movements are not fully predicted, growing exchange rate volatility, which increases transaction costs for trading partners, will lead risk-averse agents to reduce their import/export activity, and to reallocate production towards domestic markets or other international markets (Dell’Araccia, 1999).
International trade in fuels. In 2004 and 2018, the share of fuel exports represented over 50% of total exports. In 2018, those exports totalled $203 billion, up from $65 billion in 2004 (Fig.
On the import side, the share of trade represented approximately 1% in 2004 and in 2018 (Figs
International trade in manufactured goods. Both exports and imports of manufactured goods constituted a significant share in trade between Russia and its trading partners. Export flows of manufactured goods represented around 20% of total exports in 2004 and in 2018 (Figs
Import flows of manufactured goods represented 49% and 52% in 2004 and 2018, respectively (Figs
International trade in machinery and transport equipment. Imports of machinery and transport equipment represented approximately a quarter of all imports; its share in total exports, however, was relatively modest in both 2004 and 2018, accounting for only 5% (Figs
On the export side, the reported results are comparable to those of imports. An increase in volatility resulted in a decrease in exports by 0,24% when the trading partner was a non-OECD member (for example, Kazakhstan, China, or India); however, the decrease in exports was relatively small, by only 0,04%, if a partner was an OECD member (for example, Germany, Spain, or the United States) (Appendix B, Table
International trade in chemicals. The import share of chemicals (approximately 9%) was double that of exports in both 2004 and 2018 (Figs
International trade in textiles. The trade in textiles made up the smallest share of all traded products in both 2004 and 2018. Imports increased from $1.7 billion to $7.5 billion, and accounted for 2% of total imports in 2004 and 2018 (Figs
No significant impact of volatility was observed on the trade in agricultural raw materials, food, and ores and metals. As described supra the empirical results differ depending on a product category. The results obtained may be summarized as follows.
First, in a number of cases, the empirical results broadly followed the prevailing economic logic, namely that growing exchange rate volatility increased uncertainty and posed risks to traders, thereby resulting in a decrease in the flow of goods. In this case, volatility had a negative impact on trade flows, such as in the case of exports of manufactured goods, and machinery and transport equipment. Notably, with regard to manufactured goods and machinery and transport equipment, the level of financial development also played a role. In particular, countries that were OECD members presumably experienced a less marked decrease in trade flows during the period of volatility.
Second, certain results demonstrate that exchange rate volatility can have a positive impact on trade flows. Specifically, a positive impact from trade volatility was found with regard to imports of fuels, textiles, chemicals, and manufactured goods. These results may appear to be counterintuitive; however, they are not unique. Comparable conclusions were found by
The empirical results obtained supra are also subject to the price elasticity of demand for these product categories.
In addition, the results of the analysis are also subject to the level of development of the financial markets both in Russia and in partner countries. This level of development determines the ability to use different hedging instruments to address potential volatility.
The development of the financial market in Russia is still ongoing. According to the Bank of Russia and the joint IMF and WB evaluation, a number of fundamental characteristics determine the financial market development in Russia; these include, inter alia, the country’s socio-economic standing, such as diversity of economy, standard of living, maturity of government and law institutions, and the level of integration of the domestic financial market into the global economy. The current status of Russia’s financial market is characterized by (i) poor capital market development; (ii) absence of a solid institutional investor base; (iii) high concentration in certain sectors; (iv) continued presence of misconducting players; and (v) weak financial market regulation and supervision (CBR, 2019; IMF, 2016).
In this context, an extensive use of various financial instruments to hedge against exchange rate volatility shocks remains questionable. Even though the trading volume has been growing during recent years, there is still a significant room for improvement. According to Moscow Exchange
It is important to note that the results obtained in this paper may also be subject to endogeneity.
Indeed, although being widely used in econometric, if rarely elsewhere, the IV approach is conceptually difficult and easily misused (
The results of the IV approach, on the one hand, confirm the results obtained supra. Notably, in line with previous results, IV estimation coefficients also demonstrate that exchange rate volatility had a positive impact on imports of fuels, textiles, chemicals and manufactured goods.
Performing the endogeneity test confirmed that in 15 specifications (out of a total of 16) exchange rate volatility is an endogenous variable. The only exception is the specification in which imports of fuels are regressed on exchange rate volatility and other variables. In other specifications, the instrument was found to be weak due to a relatively low correlation between exchange rate volatility and an instrumental variable: (E(ERvolrit Zrit)) ≠ 0.
For these reasons, endogeneity remains a key challenge in fully assessing the impact of exchange rate volatility on the flow of both exports and imports.
This paper has examined the relationship between exchange rate volatility and trade. It has also examined the impact of volatility on exports and imports between the Russian Federation and each of its 70 trading partners during the 2004–2018 period. In particular, the impact of volatility was evaluated on imports and exports of eight product categories, namely: (i) agricultural raw materials; (ii) chemicals; (iii) food; (iv) fuels; (v) manufactured goods; (vi) ores and metals; (vii) textiles; and (viii) machinery and transport equipment.
The empirical results obtained in the research demonstrate that exchange rate volatility has a different impact on different product categories. In a number of cases, these results indicate that exchange rate volatility has a negative impact on exports of manufactured goods, and machinery and transport equipment. However, a positive and significant impact of volatility on trade is observed in both imports of fuels, textiles, chemicals, and manufactured goods.
Bearing these results in mind, it is important to note that they may be subject to price elasticities, level of financial development and an endogeneity distortion. With regards to the endogeneity issue, exchange rate volatility is potentially an endogenous variable. This endogeneity might originate from reverse causality. An instrumental variable approach was used in an attempt to address this issue. However, the selected instrument, namely changes in countries’ exchange rate regimes, may be relatively weak due to its poor correlation with the endogenous variable, which is exchange rate volatility. Therefore, further research is needed to define a stronger instrument to tackle the endogeneity issue.
Algeria | Indonesia | Peru |
Australia | Iran, Islamic Rep. | Philippines |
Austria | Ireland | Poland |
Bahrain | Israel | Portugal |
Belgium | Italy | Qatar |
Botswana | Japan | Saudi Arabia |
Brazil | Kazakhstan | Singapore |
Brunei | Korea, Rep. | Slovak Republic |
Canada | Kuwait | Slovenia |
Chile | Latvia | South Africa |
China | Libya | Spain |
Colombia | Lithuania | Sri Lanka |
Cyprus | Luxembourg | Sweden |
Czech Republic | Malaysia | Switzerland |
Denmark | Malta | Thailand |
Estonia | Mauritius | Trinidad and Tobago |
Finland | Mexico | Tunisia |
France | Montenegro | United Arab Emirates |
Germany | Nepal | United Kingdom |
Greece | Netherlands | United States |
Greenland | New Zealand | Uruguay |
Hungary | Norway | Venezuela |
Iceland | Oman | |
India | Pakistan |
Variable | Model 1 | Model 2 | Model 3 |
lnrervlt | 0.057 (0.579) |
0.057 (0.580) |
0.504 (0.584) |
lnrermean | –0.019 (0.132) |
–0.018 (0.132) |
0.046 (0.131) |
lngdp | 1.428 (1.540) |
1.441 (1.550) |
–0.548 (1.620) |
findev | –0.156 (1.130) |
–10.069 (2.930) |
|
interactio~d | –1.908 (0.522) |
||
cons | –27.549 (41.400) |
–27.800 (41.500) |
27.040 (43.500) |
Observations | 468 | 468 | 468 |
Adjusted R2 | 0.076 | 0.076 | 0.106 |
Variable | Model 1 | Model 2 | Model 3 |
lnnervlt | –0.131 (0.112) |
–0.139 (0.112) |
0.405* (0.228) |
lnnermean | 0.090 (0.101) |
0.089 (0.101) |
0.104 (0.0995) |
lngdp | 1.889** (0.923) |
1.863** (0.924) |
0.600 (1.030) |
findev | 0.672 (0.707) |
–2.227* (1.28) |
|
interactio~d | 0.600*** (0.221) |
||
cons | –44.943* (13.600) |
–44.730* (13.600) |
–8.224 (14.900) |
Observations | 632 | 632 | 632 |
Adjusted R2 | 0.080 | 0.081 | 0.098 |
Variable | Model 1 | Model 2 | Model 3 |
lnnervlt | –0.059 | –0.062 | –0.117* |
(0.038) | (0.038) | (0.608) | |
lnnermean | –0.004 | –0.004 | –0.007 |
(0.0339) | (0.0339) | (0.034) | |
lngdp | 0.857*** | 0.843*** | 0.968*** |
(0.261) | (0.261) | (0.382) | |
findev | 0.355 | 0.660* | |
(0.225) | (0.349) | ||
interactio~d | 0.066 | ||
(0.058) | |||
cons | –11.451* | –11.287* | –14.816** |
(6.780) | (6.780) | (7.440) | |
Observations | 912 | 912 | 912 |
Adjusted R2 | 0.117 | 0.119 | 0.121 |
Variable | Model 1 | Model 2 | Model 3 |
lnnervlt | 0.015 | 0.016 | 0.158** |
(0.401) | (0.402) | (0.0646) | |
lnnermean | –0.047 | –0.047 | –0.039 |
(0.0357) | (0.0357) | (0.0357) | |
lngdp | 1.574*** | 1.577*** | 1.240*** |
(0.276) | (0.276) | (0.300) | |
findev | –0.071 | –0.863** | |
(0.237) | (0.369) | ||
interactio~d | –0.172*** | ||
(0.0616) | |||
cons | 30.581*** | –30.610*** | –21.093*** |
(7.180) | (7.180) | (7.920) | |
Observations | 902 | 902 | 902 |
Adjusted R2 | 0.366 | 0.372 |
Dependent variable: log of imports of machinery and transport equipment.
Variable | Model 1 | Model 2 | Model 3 |
lnnervlt | –0.048 | –0.056 | –0.265*** |
(0.0589) | (0.0579) | (0.0947) | |
lnnermean | –0.039 | –0.039 | –0.050 |
(0.0514) | (0.0513) | (0.0512) | |
lngdp | 1.470*** | 1.427*** | 2.023*** |
(0.453) | (0.452) | (0.499) | |
findev | 0.853** | 2.027*** | |
(0.340) | (0.540) | ||
interactio~d | 0.255*** | ||
0.0914) | |||
cons | –29.402** | –28.745** | –45.418*** |
(11.800) | (11.800) | (13.200) | |
Observations | 873 | 873 | 873 |
Adjusted R2 | 0.183 | 0.189 | 0.197 |
Dependent variable: log of exports of machinery and transport equipment.
Variable | Model 1 | Model 2 | Model 3 |
lnnervlt | –0.074 | –0.073 | –0.236*** |
(0.0532) | (0.0533) | (0.0854) | |
lnnermean | –0.030 | –0.030 | –0.040 |
(0.047) | (0.047) | (0.0465) | |
lngdp | 0.239 | 0.242 | 0.618 |
(0.365) | (0.366) | (0.396) | |
findev | –0.078 | 0.833* | |
(0.315) | (0.490) | ||
interactio~d | 0.198** | ||
(0.0816) | |||
cons | 2.433 | 2.398 | –8.245 |
(9.510) | (9.520) | (10.500) | |
Observations | 905 | 905 | 905 |
Adjusted R2 | 0.102 | 0.102 | 0.109 |
Variable | Model 1 | Model 2 | Model 3 |
lnnervlt | –0.000 | 0.000 | 0.138** |
(0.0414) | (0.0415) | (0.0691) | |
lnnermean | –0.024 | –0.024 | –0.017 |
(0.0368) | (0.0368) | (0.0368) | |
lngdp | 1.072*** | 1.074*** | 0.726** |
(0.295) | (0.295) | (0.326) | |
findev | –0.053 | –0.823** | |
(0.241) | (0.391) | ||
interactio~d | –0.166** | ||
(0.0665) | |||
cons | –18.677** | –18.706** | –8.847 |
(7.740) | (7.740) | (8.670) | |
Observations | 843 | 843 | 843 |
Adjusted R2 | 0.298 | 0.298 | 0.304 |
Variable | Model 1 | Model 2 | Model 3 |
lnnervlt | 0.030 | 0.037 | 0.224*** |
(0.0428) | (0.0426) | (0.072) | |
lnnermean | –0.047 | –0.047 | –0.039 |
(0.0376) | (0.0375) | (0.0374) | |
lngdp | 0.315 | 0.351 | –0.196 |
(0.332) | (0.331) | (0.370) | |
findev | –0.746*** | –1.785*** | |
(0.245) | (0.405) | ||
interactio~d | –0.224*** | ||
(0.0696) | |||
cons | –0.439 | –0.951 | 14.332 |
(8.700) | (8.650) | (9.820) | |
Observations | 845 | 845 | 845 |
Adjusted R2 | 0.175 | 0.185 | 0.196 |