High exchange rate volatility has implications for business and policy decisions and exchange rate movements are important in debates around trade and trade policies.

The purpose of the research was to determine the impact of exchange rate volatility on exports in emerging markets.

A lack of clarity in literature regarding this relationship increases the risk of improper planning by export organisations as well as implementing suboptimal economic policies.

This research analysed the effect of exchange rate volatility on emerging market exports using a sample of nine emerging countries from 1995 to 2010. Panel data analysis was conducted. Volatility was measured by Generalised Autoregressive Conditional Heteroscedasticity and conventional standard deviation in order to determine if the instrument of volatility used influenced the nature of the relationship between exchange rate volatility and exports. The Pedroni residual cointegration method was used to test for panel cointegration in order to determine if there was a long-run relationship.

The results showed that exchange rate volatility had a significant negative effect on the performance of exports, regardless of the measure of volatility used. It was also evident that a long-run relationship did exist.

The study concluded that the policy mix that will reduce exchange rate volatility (such as managed exchange rate regimes) and relatively competitive exchange rates were essential for emerging markets in order to sustain their exports performance.

This research provided policy makers of emerging market economies with new evidence pertaining to the relationship between exchange rate volatility and the performance of exports. This research contributed to the existing knowledge on the topic and provides a base for future research on related topics.

Countries have monetary policies that maintain exchange rates and they adopt either fixed or flexible exchange rate regimes. In recent times, most countries have a flexible exchange rate system. As this type of exchange rate system gained popularity, countries liberalised their economies, the effect of globalisation intensified and economic cooperation and trade between countries increased, exchange rate volatility also increased. As a result, the role of exchange rate uncertainty on trade became one of the critical issues for economic policy makers. Debates around this issue emanate because there is no consensus on whether exchange rate volatility affects foreign trade activity. By only looking at raw data without in-depth analysis, it is difficult to determine the nature of the relationship and a lack of clarity on this issue increases the risk of improper planning by exporting organisations as well as implementing suboptimal economic policies.

The debate on exchange rate volatility and exports is gaining attention in emerging market countries because the markets are characterised by high levels of exchange rate volatility compared with developed countries (Darrat & Hakim

The analysis pertaining to the effect of exchange rate volatility on exports relates to that of the behaviour of producers under uncertainty. As defined by Marston (

The theories that support the negative effect of exchange rate volatility on exports perceive that since exchange rate volatility presents uncertainty in the business environment, rational traders tend to avoid or reduce their exposure to uncertainty and any other form of risk by adjusting trade activities. Clark (

Earlier studies found a negative relationship between exchange rates and exports; these include,

In contrast to the above-discussed view, some theories state that exchange rate volatility could in fact underpin exports. This occurs because some exporters subscribe to the low-risk-low-return practice, tending to export more when exchange rate volatility is high (Côté

In contrast to most conventional views, some theories and studies are unable to determine any form of relationship between exchange rate volatility and exports. The unlikely effect of exchange rate volatility on exports is prevalent in countries with good hedging facilities, where future trade transactions are concluded at a certain exchange rate level. Any future movement of the exchange rate should thus not affect the already pegged price and volume of goods and services to be delivered. Hooper and Kohlhagen (

Literature suggested that there is no clear indication regarding the direction of the relationship between exchange rate volatility and exports performance, however the main findings in literature do suggest a negative relationship. Exchange rate volatility could carry both costs and benefits, with exporters responding differently to risk posed by the volatility.

The panel data set is one where there are repeated observations on the same units (Gujarati

The pooled model estimates a linear relationship between the dependent variable and the explanatory variables. It assumes a normally distributed residual, constant variance and the value of the error term to be zero (Wooldridge

The LSDV model estimates the fixed effects by allowing for the intercepts of the countries to vary and it assumes normal distribution of the residuals. Each dummy is absorbing the effects of each particular country. However, the LSDV model also has some weaknesses, including the fact that it consumes a lot of degrees of freedom if too many dummies are employed (Greene

Unlike the LSDV models, the fixed effect model assumes that the slope coefficients are constant, therefore they do not vary across the individual countries or over time (Hedeker & Gibbons

The random effect models assume that individual country-specific effects are random variables which are uncorrelated with the explanatory variables (Longford

This study uses panel data tools for the benefits outlined above. A sample of nine emerging countries, namely Argentina, Brazil, India, Indonesia, Mexico, Malaysia, Poland, South Africa and Thailand, was used. The countries were selected from various regions within the emerging market group as per the International Monetary Fund country grouping. The countries produce different goods and services, large portions of which are exported either directly or indirectly to developed countries. The countries have flexible exchange rate regimes and have volatile exchange rates. They are the largest economies in the group in terms of nominal gross domestic profit (GDP). Panel data methods were most suited for this analysis to account for the said heterogeneity. Data availability was also taken into consideration. The data were collected from Datastream on a monthly- and seasonally-adjusted basis over the period 1995 to 2010. Therefore, the pooled data will have 1728 observations (i.e., 192 months multiplied by nine countries). The G7 industrial production was collected in constant US dollar terms, whilst exports data was collected in local currency terms at constant 1995 prices. Exchange rate volatilities were calculated from real effective exchange rates from the country's central banks. Real effective exchange rates were also used as an indicator of exchange rate levels. All the data, except for exchange rate volatility, were rebased, with January 1995 as the base year.

The key variables on this study are exports, exchange rate volatility, exchange rate levels, industrial production and terms of trade. A negative relationship is expected to prevail between a stronger exchange rate and the performance of exports, because stronger exchange rates make the exports to be expensive relative to those of other competitors. The opposite effect is expected for a weaker exchange rate. Since G7 countries are major global consumers, an increase in their industrial production indicates an increase in economic activity in those countries, which should push demand for imports from emerging markets. Therefore a positive relationship between G7 industrial production and emerging market exports should be expected (Baak

The accurate measure of exchange rate volatility is a controversial issue as there are various methods that can used, each with its own advantages and disadvantages. This research uses both the standard deviation and the Generalised Autoregressive Conditional Heteroscedasticity (GARCH). The use of both measures will help to determine the extent (if any) of difference in the results produced of the estimations with different measures of volatility. The standard deviation formula is estimated as follows:

where

The GARCH model models volatility through the change in the log of the exchange rate. It also accounts for volatility clustering in the time series (Brooks

Equation 3 is the conditional variance, which is a function of the mean (µ), the news about exchange rate volatility from the previous period, represented by the lag of the squared residual from the mean (which is the ARCH term) and the last period's forecast error variance (which is the GARCH term) (Onofowora & Owoye

Generally, panel data series, by their nature, are likely to show some degree of heterogeneity. In studies such as this one, where the sample has countries with unique characteristics, the individual variables may not have the same properties. The series may thus not be stationary and models with non-stationary data could lead to a spurious regression, with invalid coefficients and goodness of fit (Hoover

Panel unit root test: Exports.

Method | Level | |||
---|---|---|---|---|

Statistic | Probability | Cross-section | Observation | |

Levin, Lin & Chu |
−0.310 | 0.378 | 9 | 1683 |

Im, Pesaran & Shin W-statistic | 1.491 | 0.932 | 9 | - |

Levin, Lin & Chu |
−6.979 | 0.000 |
9 | 1674 |

Im, Pesaran & Shin W-statistic | −14.959 | 0.000 |
9 | 1674 |

*, indicates the rejection of the hypothesis of non-stationarity at 1% level of significance.

Panel unit root test: Exchange rate volatility − with Generalised Autoregressive Conditional Heteroscedasticity.

Method | Level | |||
---|---|---|---|---|

Statistic | Probability | Cross-section | Observation | |

Levin, Lin & Chu |
−3.716 | 0.000 |
9 | 1683 |

Im, Pesaran & Shin W-statistic | −11.190 | 0.000 |
9 | 1683 |

Levin, Lin & Chu |
−9.29 | 0.044 |
9 | 1674 |

Im, Pesaran & Shin W-statistic | −26.1 | 0.000 |
9 | 1674 |

GARCH, Generalised Autoregressive Conditional Heteroscedasticity.

*, indicates the rejection of the hypothesis of non-stationarity at 1% level of significance.

Panel unit root test: Exchange rate level – with standard deviation.

Method | Level | |||
---|---|---|---|---|

Statistic | Probability | Cross-section | Observation | |

Levin, Lin & Chu |
−4.45 | 0.000 |
9 | 1683 |

Im, Pesaran & Shin W-statistic | −11.19 | 0.000 |
9 | 1683 |

Levin, Lin & Chu |
5.81 | 0.002 |
9 | 1674 |

Im, Pesaran & Shin W-statistic | -28.61 | 0.000 |
9 | 1674 |

*, indicates the rejection of the hypothesis of non-stationarity at 1% level of significance.

Panel unit root test: Exchange rate level.

Method | Level | ||||
---|---|---|---|---|---|

Statistic | Probability | Cross-section | Observation | ||

Levin, Lin & Chu |
0.864 | 0.806 | 9 | 1683 | |

Im, Pesaran & Shin W-statistic | −0.660 | 0.255 | 9 | 1683 | |

Levin, Lin & Chu |
−8.265 | 0.000 |
9 | 1674 | |

Im, Pesaran & Shin W-statistic | −14.746 | 0.000 |
9 | 1674 |

*, indicates the rejection of the hypothesis of non-stationarity at 1% level of significance.

Panel unit root test: Terms of trade.

Method | Level | |||
---|---|---|---|---|

Statistic | Probability | Cross-section | Observation | |

Levin, Lin & Chu |
−0.896 | 0.185 | 9 | 1683 |

Im, Pesaran & Shin W-statistic | −1.362 | 0.086 | 9 | 1683 |

Levin, Lin & Chu |
−10.735 | 0.000 |
9 | 1674 |

Im, Pesaran & Shin W-statistic | −15.651 | 0.000 |
9 | 1674 |

*, indicates the rejection of the hypothesis of non-stationarity at 1% level of significance.

Panel unit root test: G7 Industrial production.

Method | Level | |||
---|---|---|---|---|

Statistic | Probability | Cross-section | Observation | |

Levin, Lin & Chu |
3.668 | 0.999 | 9 | 1683 |

Im, Pesaran & Shin W-statistic | 6.242 | 1.000 | 9 | 1683 |

Levin, Lin & Chu |
−3.745 | 0.000 |
9 | 1674 |

Im, Pesaran & Shin W-statistic | −12.696 | 0.000 |
9 | 1674 |

G7, a group of seven largest industrialised nations in the world, namely, Italy, France, USA, Germany, Japan, the UK and Canada.

*, indicates the rejection of the hypothesis of non-stationarity at 1% level of significance.

The results show that exchange rate volatility (measured by both GARCH and standard deviation) was stationary at its level, integrated at I(0) whilst exports, exchange rate level, terms of trade and G7 industrial production were not stationary at their levels, but were integrated to the first order, I(1). Whilst regression with non-stationary variables will yield spurious results, when the variables are cointegrated, there is no need to worry about spurious regression. Cointegration occurs when the errors of non-stationary series cancel each other out, resulting in a stationary error term. This implies that the variables may not be moving together in the short-run but have a true relationship in the long run (Perman & Stern

Pedroni residual cointegration test.

Panels | Series with GARCH Common autoregressive coefficients (within-dimension) | Series with Standard deviation Common autoregressive coefficients (within-dimension) | ||||||
---|---|---|---|---|---|---|---|---|

Statistic | Probability | Weighted | Probability | Statistic | Probability | Weighted | Probability | |

Panel v | −9.162 | 0.000 |
−8.790 | 0.000 |
−9.344 | 0.000 |
−11.548 | 0.000 |

Panel rho | −19.947 | 0.000 |
−24.488 | 0.000 |
−19.921 | 0.000 |
−24.209 | 0.000 |

Panel PP | −14.667 | 0.000 |
−21.125 | 0.000 |
−14.622 | 0.000 |
−20.882 | 0.000 |

Panel ADF | −10.612 | 0.000 |
−14.179 | 0.000 |
−10.688 | 0.000 |
−14.121 | 0.000 |

GARCH, Generalised Autoregressive Conditional Heteroscedasticity; ADF, Augmented Dickey Fuller; PP, Phillips Perron.

*, **, indicate the rejection of the hypothesis of non-stationarity at 1% and 5% level of significance, respectively.

Pedroni residual cointegration test.

Groups | Individual autoregressive coefficients (between-dimension) | |||
---|---|---|---|---|

Statistic | Probability | Statistic | Probability | |

Group rho | −26.867 | 0.000 |
−26.913 | 0.000 |

Group PP | −24.365 | 0.000 |
−24.266 | 0.000 |

Group ADF | −16.847 | 0.000 |
−16.951 | 0.000 |

GARCH, Generalised Autoregressive Conditional Heteroscedasticity; ADF, Augmented Dickey Fuller; PP, Phillips Perron.

*, **, indicate the rejection of the hypothesis of non-stationarity at 1% and 5% level of significance, respectively.

The null hypothesis proposed by Pedroni (

The model for this study is based on a standard exports equation, with the performance explained by exchange rate volatility, the level of the exchange rate, demand conditions in major countries and terms of trade. The data for all the variables, except for exchange rate volatility, was logged. The generic export equation is therefore estimated as:

and, therefore:

Statistical tests were used to choose the appropriate model. The LSDV model estimates the fixed effects by allowing for the intercepts of the countries to vary and it assumes normal distribution of the residuals. Each dummy absorbs the effects of each particular country. The results of the LSDV estimation, with South Africa as a benchmark country, are presented in

LSDV estimation (dependent variable: exports).

Variable | A – GARCH | B – Standard deviation | ||||
---|---|---|---|---|---|---|

Coefficient | Coefficient | |||||

ExRateVOL | −0.21 | 0.013 |
−0.30 | 0.033 |
||

ExRate | −0.35 | 0.001 |
−0.35 | 0.001 |
||

G7 | 5.51 | 0.000 |
5.54 | 0.000 |
||

Tot | 0.75 | 0.021 |
0.77 | 0.020 |
||

Argentina | 42.42 | 0.000 |
42.34 | 0.000 |
||

Brazil | 106.64 | 0.000 |
107.22 | 0.000 |
||

India | 63.54 | 0.000 |
64.54 | 0.000 |
||

Indonesia | 50.16 | 0.003 |
49.04 | 0.002 |
||

Malaysia | 55.21 | 0.000 |
56.09 | 0.000 |
||

Mexico | 149.07 | 0.000 |
149.03 | 0.000 |
||

Poland | 165.76 | 0.000 |
166.74 | 0.000 |
||

Thailand | 22.92 | 0.050 |
22.95 | 0.053 |
||

0.57 | - | 0.56 | - | |||

Adjusted R-squared | 0.56 | - | 0.56 | - |

GARCH, Generalised Autoregressive Conditional Heteroscedasticity; LSDV, Least Squares Dummy Variable; EXRATEVOL, Exchange Rate Volatility; EX RATE, Exchange Rate;

*, **, indicate the rejection of the hypothesis of non-stationarity at 1% and 5% level of significance, respectively.

As discussed earlier, despite the advantage of making it possible to compare individual country-specific effects, the LSDV model has several limitations. As a result, it was used in this research. The fixed and random effect models were employed. Before the models are estimated appropriately, it is essential to check if the countries are similar (in case of the fixed effect) and if there are random effects (in case of the random effect) in the models. The redundant fixed effect test was used to test if there are similarities in the countries. The results are presented in

Redundant fixed effects tests.

Method - Effects test | GARCH | Standard deviation | ||||
---|---|---|---|---|---|---|

Statistic | d.f. | Statistic | d.f. | |||

Cross-section F | 103.8 | (8,158) | 0.000* | 116.184 | (8,158) | 0.000* |

Cross-section Chi-square | 673.0 | 8 | 0.000* | 736.999 | 8 | 0.000* |

d.f. – Degrees of freedom;

*, indicates the rejection of the hypothesis of non-stationarity at 1% level of significance.

The hypothesis is that the countries are different from each other. The outcome of the test shows that the null hypothesis is rejected at 1% level of confidence. Therefore, the countries are different and that fixed panel models could be carried out appropriately. The redundant fixed effect test therefore shows that the coefficients in the fixed effect models are significantly different and suggested that the fixed effect model could be appropriate. However, it is also crucial to test which method between the fixed and random effect model is most appropriate to account for heterogeneity. The Hausman test was conducted in order to check for the presence of random effect in the models and the results are presented in

Random effects – Hausman test.

Method - Test Summary | GARCH (1:1) | Standard deviation | ||||
---|---|---|---|---|---|---|

Chi-square statistic | Chi-square d.f. | Probability | Chi-square statistic | Chi-square d.f. | Probability | |

Cross-section random | 6.634 | 4 | 0.1566 | 6.203 | 4 | 0.977 |

d.f., Degrees of freedom;

The Hausman test checks the presence of the random effects in the random effect models. It checks whether the unique error terms are correlated with the explanatory variables (Greene

Dependent variable: Exports.

Method | OLS | Fixed effect model | Random effect model | |||
---|---|---|---|---|---|---|

A–GARCH | B–Standard deviation | C–GARCH | D–Standard deviation | E–GARCH | F–Standard deviation | |

Exchange rate volatility | −0.860 |
−3.644 |
−2.577 |
−3.142 |
−2.566 |
−3.153 |

−2.025 | −3.366 | −2.467 | −3.560 | −2.455 | −3.720 | |

0.042 | 0.063 | 1.045 | 0.029 | 0.045 | 0.028 | |

Real exchange rate | −0.050 |
−0.115 |
−0.307 |
−0.278 |
−0.269 |
−0.300 |

−0.642 | −0.555 | −5.896 | −8.076 | −6.431 | −5.965 | |

0.014 | 0.059 | 0.032 | 0.034 | 0.038 | 0.036 | |

G7 industrial production | 3.209 |
5.384 |
5.182 |
4.784 |
1.618 |
1.621 |

−9.871 | −12.043 | −12.104 | −9.724 | −2.871 | −2.839 | |

0.097 | 0.447 | 0.428 | 0.492 | 0.012 | 0.018 | |

Terms of trade | 1.785 |
1.979 |
0.746 |
0.778 |
0.731 |
0.758 |

−1.979 | −1.954 | −1.729 | −1.952 | −1.022 | −1.124 | |

0.039 | 0.044 | 0.1302 | 0.157 | 0.121 | 0.148 | |

Adjusted |
0.242 | 0.221 | 0.98 | 0.98 | 0.839 | 0.839 |

Number of observations | 1728 | 1728 | 1728 | 1728 | 1728 | 1728 |

GARCH, Generalised Autoregressive Conditional Heteroscedasticity; OLS, Ordinary Least Square; G7, a group of seven largest industrialised nations in the world, namely, Italy, France, USA, Germany, Japan, the UK and Canada; TOT, Terms of Trade.

According to literature, no consensus was established regarding whether exchange rate volatility affects exports either negatively or positively. This section will present the empirical findings of this study. Tree panel data models were estimated and the results are presented in

White's cross-section standard errors and covariance methods were used to correct for heteroscedasticity and autocorrelation in order to obtain correct standard errors. The coefficients in the core models (i.e., the random effect models) make economic sense, are in line with preliminary expectations and support the arguments by most of other theories and similar empirical studies reviewed earlier. The results confirm that the performance of exports is driven by various factors in the economy, including exchange rate volatility, the level of the exchange rate, economic activities and terms of trade.

The aim of this research was to analyse the determinants of exports, with a specific focus on the effect of exchange rate volatility on the performance of exports. Panel data models were used to determine the nature of the relationship between the two variables in a sample of nine emerging countries. Two measures of exchange rate volatility, namely standard deviation and GARCH, were used.

Both models (i.e., the one with exchange rate volatility measured by GARCH [column E] and the one with exchange rate volatility measured by standard deviation [column F] in

In line with the expectations, the core model also showed that the level of the exchange rate has a negative and statistically significant effect on exports, with a 1% appreciation of the exchange rate,

The coefficients of G7 industrial production are both positive and statistically significant at the 1% level in both models. This is in line with economic theory and shows that an increase in economic activity in G7 countries tends to underpin demand for imported goods and services used in the production process. This will imply a rise in exports of trading partners. The core model shows that a 1% increase in G7 industrial production,

Terms of trade measure countries’ export competitiveness, hence an improvement in terms of trade is expected to have a positive influence on exports. The core models proved this to be true by producing positive coefficients, with a 1% increase in terms of trade resulting in about 0.76% and 0.73% rise in exports, as seen in

Overall, the core models produced adjusted

Exchange rate volatility, the key variable in this research, captures risk or uncertainty faced by exporters in international trade. Such uncertainty could have implications on exporters’ decision making processes. The outcome of this research suggests that uncertainty in emerging markets business environment, which results from exchange rate instability, discourages exporters from expanding capacity and engaging more in foreign trade.

The dependence of emerging markets on major countries for economic prosperity (which was evident by a statistical significant influence of G7 economic activity on export) makes them vulnerable to exogenous factors, such as the recently experienced 2008–2009 global financial crisis. Emerging economies need to diversify their exports to different regions evenly and invest in infrastructure and skills that will enable them to transform their major exports to usable finished products. This will increase specialisation and help to achieve some of the critical macroeconomic objectives, such as employment creation.

Schwab (

In general, the research confirmed that exchange rate volatility has a statistically negative effect on the performance of exports. Based on these findings, policy makers in export-oriented emerging countries should strive for a policy mix that will ensure stable and relatively weak exchange rates. This is recommended because stable exchange rates will ensure certainty, helping investors to make accurate planning and reduce operational risk, whilst competitive exchange rates will help to ensure that the goods remain competitive relative to foreign markets. A combination of a stable exchange rate environment and a competitive currency will help to attract investment, increase aggregate output and improve countries’ economic prosperities.

This research was intended to provide empirical analysis on the determinants of exports, with specific focus on the effect of exchange rate volatility on the performance of exports. Panel data models were used to determine the nature of the relationship between the two variables in a sample of nine emerging countries. Two measures of exchange rate volatility, namely, standard deviation and GARCH, were used. The core models were unable to find evidence of either positive or no relationship between exchange rate volatility and exports, as was suggested by some theories and studies. The models showed that regardless of the measure of exchange rate volatility used, exchange rate volatility has a negative effect on exports, which was in line with preliminary expectations, economic theory and most other similar studies. The negative impact of exchange rate volatility on exports suggests that emerging market exporters are also generally risk averse, with a tendency of reducing their trade activities when the exchange rates became persistently volatile. It thus makes economic sense to expect some reduction in exports growth when the exchange rates become volatile. The exporters could respond by temporarily suspending international trade or reducing their participation in such activities until some sense of certainty returns through reduced exchange rate volatility. In extreme scenarios, high exchange rate volatility could therefore be expected to induce exporters to discontinue international trade activities completely and focus on supplying domestic markets.

The authors declare that they have no financial or personal relationship(s) that may have inappropriately influenced them in writing this article.

M.J.K. (University of Johannesburg) conducted research for his minor dissertation for fulfilment of a Master's degree in Economics. He also contributed as author and re-worked the minor dissertation into an article. I.B. (University of Johannesburg) contributed as supervisor and author to the literature review, empirical analysis and submission. M.P. (University of Johannesburg) contributed as co-supervisor and author to the literature review and the empirical analysis.

Summary of selected studies on the impact of exchange rate volatility.

Researcher | Name of the study | Data period | Variables | Exchange rate volatility measure | Estimation method | Results |
---|---|---|---|---|---|---|

Ahmed ( |
‘Exchange rate volatility and international trade growth: evidence from Bangladesh’ | Monthly: May 2003 to Dec 2008 | Real exports, real bilateral exchange rate, Industrial production index of importing countries, relative price level, exchange rate volatility | Standard deviation | Error-correction models | Exchange rate volatility does not have a significant impact on exports of Bangladesh for both short-run and long-run. |

Arize, Malindretos & Kasibhatla ( |
‘Does exchange-rate volatility depress export flows: the case of LDCs’ | Quarterly: 1973 to 1998 | Export goods, world demand conditions (world GDP), relative prices, terms of trade | 7-period moving-sample standard deviation | Johansen method of cointegration | Negative affect of exports. |

Bailey ( |
‘Trade and investment performance under floating exchange rates: the U.S. experience’ | Quarterly: 1975 to 1988 | US export volume, foreign income, real exchange rate, exchange rate volatility | Standard deviation and percentage change in real effective exchange rate | Ordinary least squares | Insignificant negative relationship. |

Bélanger, Gutiérrez & Raynauld ( |
‘Exchange rate variability and trade flows: sectoral estimates for the US-Canada case’ | Quarterly: 1976 to 1987 | US exports to Canada, nominal exchange rate | Squared forecast error three month forward spread | Cross-section method | Significant negative relationship in two sectors. |

Brada & Méndez ( |
‘Exchange rate risk, exchange rate regime and the volume of international trade’ | Annual: 1973 to 1977 | Exports amongst 30 countries and dummy for exchange regime | Dummy for real exchange | Cross-section method | Significantly positive during the period of floating exchange rate regime. |

Bredin, Fountas & Murphy ( |
‘An empirical analysis of short-run and long-run Irish export functions: does exchange rate volatility matter?’ | Quarterly: 1978 to 1998 | Export volume, foreign income, relative prices and exchange rate volatility | Standard deviation | Error-correction models | Exchange rate volatility has no effect in the short-run but a significant positive effect in the long-run. |

Chowdhury ( |
‘Does exchange rate volatility depress trade flows? Evidence from error-correction models’ | Quarterly: 1973 to 1990 | Real export volume, real foreign economic activity, relative prices, terms of trade | Real exchange rate standard deviation | Error-correction models | Exchange rate volatility has a negative effect on exports. |

Dogănlar ( |
‘Estimating the impact of exchange rate volatility on exports: evidence from Asian countries’ | Quarterly: 1980 to 2002 | Real exports, industrialised countries industrial production, terms of trade, exchange rate uncertainty | Moving standard deviation of the growth of the real exchange rate | Error-correction models | Exchange rate volatility reduced real exports. |

Esquivel & Larraín ( |
‘The impact of G-3 exchange rate volatility on developing countries’ | Annually: 1973 and 1998 | Real exports, real world GDP, real effective exchange rate, exchange rate volatility | Standard deviation of the growth rates of real exchange rates | Vector autoregressive model | Exchange rate volatility has negative impact on trade flows. |

Kandilov ( |
‘The effects of exchange rate volatility on agricultural trade’ | Annually: 1975 to 1997 | Real exports, GDP, population growth, transport costs, distance between nations, exchange rate volatility | GARCH specification | Gravity equation model | Exchange rate volatility has a large negative impact on agricultural trade between members of the G 10 countries. |

Koray & Lastrapes ( |
‘Real exchange rate volatility and U.S. bilateral trade: a VAR approach’ | Monthly: 1961 to 1985 | Real exports, relative prices, imports of trading partners, exchange rate volatility | 12-month moving standard deviation of real exchange rate | Vector autoregressive model | Negative, but weak. |

Kroner & Lastrapes ( |
‘The impact of exchange rate volatility on international trade: reduced form estimates using the GARCH-in-mean model’ | Monthly: 1973 to 1990 | Exchange rate variability, international trade, reduced form estimates | GARCH model | Ordinary least squares | Mixed results. |

Héricourt & Poncet ( |
‘Exchange rate volatility, financial constraints and trade: empirical evidence from Chinese firms’ | Monthly: 2000 to 2006 | Export performance, financial vulnerability, financial development, export performance and real exchange rate volatility | Standard deviation of monthly log differences | Panel analysis | Negative effect. |

Chit, Rizov & Willenbockel ( |
‘Exchange rate volatility and exports: new empirical evidence from the emerging East Asian economies’ | Quarterly: 1982 to 2006 | Real exports, relative prices, home income, foreign income, volatility | GARCH and standard deviation | Panel analysis | Negative relationship. |

Grier & Smallwood ( |
‘Exchange rate shocks and trade: a multivariate GARCH-M approach’ | Monthly: 1973 to 2007 | Multilateral exports, foreign income, volatility | GARCH | Multivariate GARCH | Negative relationship. |

Verheyen ( |
‘Bilateral exports from euro zone countries to the US – Does exchange rate variability play a role?’ | Monthly: 1992 to 2010 | Bilateral exports, volatility and foreign income | Standard deviation and GARCH | Bounds testing approach | Negative relationship. |

Hall |
‘Exchange-rate volatility and export performance: Do emerging market economies resemble industrial countries or other developing countries?’ | Quarterly: 1980 to current | Exports, volatility, income | Standard deviation and GARCH | Panel analysis with time-varying parameter and generalised method of moments | Negative relationship in developed countries but inconclusive for emerging markets (open capital markets reduce effect). |

GDP, Gross Domestic Profit; GARCH, Generalised Autoregressive Conditional Heteroscedasticity.