Effect of exchange rate uncertainty on bilateral trade performance in SAARC countries: A gravity model analysis

Banik, Banna Article Effect of exchange rate uncertainty on bilateral trade performance in SAARC countries: A gravity model analysis International Trade, Politics and Development (ITPD) Provided in Cooperation with: Department of International Commerce, Finance, and Investment, Kyung Hee University Suggested Citation: Banik, Banna (2021) : Effect of exchange rate uncertainty on bilateral trade performance in SAARC countries: A gravity model analysis, International Trade, Politics and Development (ITPD), ISSN 2632-122X, Emerald, Leeds, Vol. 5, Iss. 1, pp. 32-50, https://doi.org/10.1108/ITPD-08-2020-0076 This Version is available at: https://hdl.handle.net/10419/319558 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Effect of exchange rate uncertainty on bilateral trade performance in SAARC countries: a gravity model analysis Banna Banik Department of Offsite Supervision, Bangladesh Bank, Dhaka, Bangladesh, and Chandan Kumar Roy Credit Guarantee Scheme Unit, Bangladesh Bank, Dhaka, Bangladesh Abstract Purpose –Exchange rate uncertainty leads to an indecisive environment for imports and exports that would condense international trade, foreign direct investment, trade earnings, trade volumes, economic growth and welfare. This study aims to examine, empirically, the effect of exchange rate uncertainty on bilateral trade performance, focusing on eight SAARC member economies using the popular modified gravity model of trade. Design/methodology/approach –The paper includes eight SAARC members –Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, Pakistan and Sri Lanka panel data set over the period 2005–2018. The authors consider both standardized value (standard deviation) and conditional variance model to determine volatility of exchange rate. Primarily, ordinary least squares, random effects and fixed effects estimation techniques are employed to investigate the impact of exchange rate volatility. Endogeneity and robustness of the findings have been tested using the simultaneity-adjusted model and dynamic panel data two-step system GMM estimation techniques. Findings –Empirical findings endorse the view that exchange rate volatility lowers trade flows in the SAARC regions. However, this adverse effect of exchange rate uncertainty on trade is pretty small. The negative correlation between exchange rate volatility and bilateral trade remains consistent and significant after controlling of simultaneous causality, autocorrelation, year effects, country-pair heterogeneity and endogeneity irrespective of panel data estimation techniques and different measures of volatility. Originality/value –The present paper is original work. Keywords Gravity model, SAARC, Bilateral trade, Exchange rate uncertainty Paper type Research paper 1. Introduction The effect of exchange rate uncertainty on bilateral trade is a crucial issue of academic investigation as well as an essential concern of monetary policy relevance. The evidently favorable impact on trade performance of restricting exchange rate instability has become one of the key controversies for the single currency union, regional economic integration and different types of agreements on the fixed exchange rate. The world has already experienced the Asian crisis in 1997–1998 due to the collapse of the exchange rate started in Thailand. The crisis swept over and seriously damaged exchange values of currencies, stock markets and other asset prices in the East and Southeast Asian countries. It became a global financial ITPD 5,1 32 © Banna Banik and Chandan Kumar Roy. Published in International Trade, Politics and Development. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons. org/licences/by/4.0/legalcode The authors would like to thank anonymous reviewers for their constructive comments and suggestions. The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2586-3932.htm Received 20 August 2020 Revised 27 September 2020 10 October 2020 Accepted 6 December 2020 International Trade, Politics and Development Vol. 5 No. 1, 2021 pp. 32-50 Emerald Publishing Limited e-ISSN: 2632-122X p-ISSN: 2586-3932 DOI 10.1108/ITPD-08-2020-0076 crisis when it extended its effects to Russia and Brazil. Studies revealed that one of the possible causes of this crisis was maintaining a fixed exchange rate that was pegged such a rate (concerning US dollar), which is favorable to exporters. External economic forces were overlooked and left exposed to foreign exchange risk. In the mid-1990s, the Federal Reserve initiatives to raise interest rates against inflation directed to an appreciation of the US dollar value and attracted hot money to flow into the US economy. Currencies that are pegged to the US currency are also appreciated and, as a result, collapse the export growth and foreign investment. Though the South Asian Association for Regional Cooperation (SAARC) countries affected the least on this crisis, the experience of the crisis leads the SAARC policymakers to form a South Asian Customs Union (SACU) and South Asian Economic Union (SAEU) as early as 2015 and 2020, respectively, under the roadmap of 1999 with a vision titled “greater coordination of monetary and exchange rate policy”(Banik and Gilbert, 2008). The establishment of SAARCFinance in 2002 by the SAARC central bank governors and finance secretariats is the initial doorstep in this direction. Maintaining a stable exchange rate movement is an important policy agenda of the SAARCFinance because a sharp fluctuation in the exchange rate in one member country can badly affect other member economies’external trade position where the exchange rate remains reasonably stable. In general, excessive volatility or fluctuation of exchange rate triggers uncertainties that directly affect international trade and the economy. Extreme volatility sends terrible signals for foreign investors and restricts foreign investment flow by reducing foreign direct investment in overseas operational facilities and capital investment (Horvath, 2005;Hagen and Zhou, 2005). The more frequent exchange rate volatility leads to an uncertain environment for imports and exports that would condense international trade, trade earnings, trade volumes, economic growth and welfare (Hall et al., 2010). Both international trade and investment choices become more complicated due to frequent change of exchange rates, and more volatility generates exchange rate risk. Because of exchange rate risk, exporters may prefer to shift to domestic market activities where returns are relatively less risky rather than continuing to export in overseas markets. Exchange rate volatility also pressures macroeconomic policy formulation, for example, for countries who are introducing an inflation-targeting regime, monetary authorities should amend the projected inflation target repeatedly because of modification of the level and volatility in the exchange rate. The SAARC established on December 8, 1985, with a common objective of accelerating economic growth and social development of South Asia. One of the successful initiatives of the SAARC is the formulation of SAARC Preferential Trade Arrangement (SAPTA) in 1993 to speed up and sustain mutual international trade through trade liberalization process (i.e. reduction of tariff rate). Lessening of tariff barrier through SAPTA allows the SAARC members for specialization in the capacity of production, which in turn helps to decrease the cost of production, increase in demand and leads to progress of bilateral trade among members. However, facilitating trade liberalization of each country to the international market is not only depending on lessening of tariff or nontariff barrier, but also there are substantial external shocks that could adversely affect bilateral trade among nations. Exchange rate uncertainty is one of the crucial external shocks for the efficient and smooth operation of international trade flows. Though SAARC member countries have introduced a flexible exchange rate regime after the 1990s, each member country has different monetary policy framework to determine their exchange rate regime. Thus, the degree of exchange rate uncertainty for each nation is different, and this uncertainty could affect bilateral trade performance of the SAARC countries. Numerous empirical studies have analyzed this issue, and definitely, these studies reveal mixed effects. The earliest empirical studies (Hooper and Kohlhagen, 1978;IMF, 1984) consider a few numbers of high-income countries and show no consistent trade effect of exchange rate fluctuation. The most recent studies apply popular gravity model approach Effect of exchange rate uncertainty 33 considering a sort of bilateral trade data to measure the impact of real exchange rate shock (Clark et al., 2004;Hayakawa and Kimura, 2009;Asteriou et al., 2016;Kang, 2016;Senadza and Diaba, 2017;Nguyen and Vo, 2017;Lin et al., 2018). Studies in this cluster find adverse and statistically significant effects; some show this negative effect is relatively small, and others argue that this negative effect is not robust using different econometric methods. A group of studies employs autoregressive conditional heteroskedasticity (ARCH) or generalized autoregressive conditional heteroskedasticity (GARCH) type of time-series methods (Grier and Smallwood, 2007;Hooy and Choong, 2010) to investigate the effect of exchange rate uncertainty on trade, but their findings are also mixed. Therefore, despite numerous efforts, the existing literature on the effect of exchange rate volatility on trade remains inconclusive. Prior empirical evidence on assessing the impact of exchange rate uncertainty on bilateral trade among the SAARC countries using panel data and the gravity model of trade is still absent. Existing literatures on how exchange rate fluctuation affects trade in SAARC countries are based on time-series analysis, very few and inconclusive. Thus, this paper investigates and contributes to the literature by analyzing the bilateral trade effects of exchange rate uncertainty using a sample of SAARC economies employing a gravity model approach. Second, it uses the SAARCFinance database on the determinants of the bilateral trade performance of eight SAARC countries. Third, the data covers the most up-to-date data from the year 2005 to 2018. Forth, the study assesses the effect of the volatility of exchange rate controlling for a broad set of bilateral information using the gravity model, for the first time, for the SAARC region. Finally, the study has significant policy implications related to the benefit of regional economic integration and free-trade policies of the SAARC region. The key findings in this study endorse the view that exchange rate volatility lowers trade flows in the SAARC regions. However, in line with the empirical results, the adverse effect of exchange rate uncertainty on trade is pretty small. Furthermore, the level of output between the countries is positively and significantly related to bilateral trade. The negative correlation between exchange rate fluctuation and bilateral trade remains consistent and significant after controlling of simultaneous causality, autocorrelation, country-pair heterogeneity and endogeneity irrespective of static and dynamic panel data estimation techniques. The paper is structured as follows –the next section analyzes the existing theoretical and empirical literature of the study. Section 3 provides the methodology, data description and sources and stylized facts. Section 4 illustrates the estimation strategy and results. Section 5 discusses the empirical results, and the final Section 6 concludes. 2. Review of existing literature 2.1 Theoretical foundation Several theoretical models have developed in the trade literature regarding the consequences of exchange rate uncertainty. The earliest and influential study by Clark (1973) illustrates a simple theoretical clarification on how the exchange rate fluctuation affects international trade. He assumes a small firm operating under a perfectly competitive market, produces single goods, does not require imported raw materials in the production process, fixed output and has access to financial hedging. The firm accepts only in foreign exchange; thus, the income and profitability of the firm depend on the exchange rate movement. Extreme exchange rates movement immediately transforms into uncertainty regarding the expected proceeds in domestic currency. As a result, the firm needs to reconsider the level of production as well as volume of exports to minimize the uncertainty. If the firm is risk-averse and concentrates to maximize its profit, the primary precondition is that the firm has to produce and export that level of output for which the marginal revenue surpasses its minimal cost to offset the risk of exchange rate fluctuations. In these uncertain situations, the firm’s profitability from export hooks only on exchange rate. Notably, the more frequent volatility ITPD 5,1 34 of the exchange rate leads a decline in the firm’s production and exports. Thus, this model confirms a negative association between exchange rate volatility and international trade. Considering the relative degree of risk aversion attitudes of the traders, Hooper and Kohlhagen (1978) also developed a theoretical model for evaluating the impact of exchange rate uncertainty on cost and volumes of trade. Their finding is that if the exporters are riskaverse, more volatility on the exchange rate will reduce the trade volume. Gagnon (1993) also has a supportive argument that volatility will contract the size of trade. However, several theoretical studies argue against the negative effect of exchange rate volatility on trade. Grauwe (1988) argues that the uncertainty of the exchange rate may have a negative or a positive impact on trade based on a producers’risk aversion attitude and profit maximization motives. If a firm shows a minor aversion to risk, it will manufacture fewer goods to export as more volatility on the exchange rate lowers the estimated marginal utility of export earnings. But a highly risk-averse producer will produce more to export to avoid an acute decrease in their income streams. Dellas and Zilberfarb (1993) and Broll and Eckwert (1999) also have similar findings. They conclude that an increase in uncertainty has both substitution effect and income effect. Substitution effect entails higher uncertainty reduces trade flows as an increase in the exchange rate risk forces the exporter to move to less risky export from risky export activities, which are referred to as the substitution effect. Besides, the income effect stimulates more allocation of resources into the exports of goods and services as an increase in exchange rate risk induces more export activities to compensate for the potential loss of expected revenue from export. For an extreme risk-averse firm, the income effect controls the substitution effect, and higher uncertainty leads to higher international trade rather than reduction. Several studies have confirmed the consideration that risk could also boost trade performance by Franke (1991), and Sercu and Vanhulle (1992). 2.2 Empirical literature Several studies have been carried out empirically to explore whether trade is influenced by exchange rate uncertainty. The most influential work on impact of exchange rate volatility on trade was conducted by Chowdhury (1993). Employing the multivariate error-correction model on time-series data set, he found that volatility of the exchange rate has a significant negative impact on the export volume of each G-7 member country. McKenzie (1999) surveys a comprehensive review of both the theoretical and empirical literatures to address the impact of volatility of exchange rate and trade flows. He points out that from both the theoretical and empirical point of view, impact of exchange rate volatility on trade flows is ambiguous and mixed. Ozturk (2006) conducts another detailed survey of empirical literature from 1978 to 2005. Till 2005, he addresses a total of 41 empirical works in his review and finds a mixed nexus between exchange rate volatility and trade. Majority of the studies have shown evidence of adverse effect, nine studies do not find any significant effect and ten studies have shown positive effect of exchange rate uncertainty on trade. Later on, Coric and Pugh (2010) extend the literature survey of Ozturk (2006) and employ MRA (meta-regression analysis) of the results of 64 existing studies. They concluded that 33 studies found an unfavorable effect of exchange rate risk on trade flows, six empirical papers found that volatility improves trade performance and rest 25 studies do not confirm these results. Table 1 summarizes recent empirical studies on how exchange rate volatility affects trade from 2010 and onward Overall findings at both theoretical and empirical levels show that the effect of the exchange rate uncertainty on trade flow is unclear. From the theoretical point of view, the result may be negative or positive but subject to the model assumptions, precisely the attitude of exporters to meet up exchange rate risk. From an empirical perspective, most of the empirical papers generally rely on OLS estimations, which may suffer from omitted variable Effect of exchange rate uncertainty 35 bias or endogeneity problems. A limited number of studies employ a GMM estimator to address the potential problem of endogeneity, but these studies do not care about on instrument proliferation issue. As a result, biased estimators might be derived. Moreover, empirical studies have provided limited evidence of the effect of exchange rate uncertainty on bilateral trade performance for SAARC countries, mainly using the gravity model of trade and system GMM estimation. Hooy and Choong (2010) conduct an empirical analysis on the impact of exchange rate volatility on world and intratrade flows of four SAARC countries (Bangladesh, India, Pakistan and Sri Lanka). They employ exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model to compute the conditional exchange rate volatility and bound testing approach on export demand function and find that volatility significantly and positively induces real export in most of the SAARC countries. Using annual time-series data of three South Asian countries, Mukhtar and Malik (2010) investigate the effect of exchange rate volatility on export. Employing cointegration and vector error correction model techniques on long-run export demand function, they find that, both in the short run and long run, volatility of exchange rate exerts significant negative effects on exports of India, Pakistan and Sri Lanka. The aforementioned two studies only consider four countries out of eight SAARC members and mostly based on time-series approaches. This empirical paper is different from these previous papers with respect to empirical specification, estimation strategy, scopes and crucial bilateral control factors that have a substantial impact on bilateral trade. Several influential empirical papers in the field of international trade use gravity model intensively to find out the determinants of bilateral trade (such as Krugman, 1991;Frankel, 1992;Bayoumi and Eichengreen, 1995). Mainly, trade performance between two economies or intraindustry trade flows could be well explained by gravity model, which cannot be resolved by other econometric models and economic theories. In this model, the trade between two countries is proportional to their GDP and inversely related to their geographical distance, which infers countries with higher GDP tend to trade more and more distance between countries (a proxy of transport cost) should discourage bilateral trade. Study Sample period Estimation strategy Results (trade/export) Chit et al. (2010) 1982–2006, Q FE, RE, GMM, gravity Negative, significant Hooy and Choong (2010) 1981–2005, A EGARCH, bound test Positive, significant Hall et al. (2010) 1980–2006,Q GMM, TVC Positive, significant Olayungbo et al. (2011) 1986–2005, A OLS, FE, GMM, gravity Positive, significant Umaru et al. (2013) 1970–2009, A OLS, ARCH, GARCH Positive, significant Nishimura and Hirayam (2013) 2002–2011, M ARCH, ARDL Mixed effects Serenis and Tsounis (2013) 1990–2012, Q VECM Negative, significant Vieira and MacDonald (2016) 2000–2011, A System GMM Negative, significant Asteriou et al. (2016) 1995–2012, M ARDL, GARCH Mixed effects Chi and Cheng (2016) 2000–2013, Q GARCH Positive, significant Kang (2016) 2003–2015, A FE, gravity model Positive, significant Aftab et al. (2017) 2000–2013, M GARCH, ARDL Negative effects Senadza and Diaba (2017) 1993–2014, A GARCH, EGARCH Mixed effects Nguyen and Vo (2017) 2002–2015, A OLS, FE, GMM No clear evidence Lin et al. (2018) 1970–2000, A FE, 2SLS, gravity Negative, significant Vo et al. (2019) 2000–2015, A GARCH, OLS, ECM Negative, significant Bajo-Rubio et al. (2019) 1994–2014, Q GARCH, OLS No clear evidence Sugiharti et al. (2020) 2006–2018 M ARDL, NARDL Negative, significant Kumar et al. (2020) 1981–2017, A Panel ARDL, ECM Negative, significant Note(s):A5Annual, Q 5Quarterly, M 5Monthly Source(s): Authors’Compilation Table 1. Empirical literature survey ITPD 5,1 36 3. Methodology and data 3.1 Model specification Extensive body of literature on international economics employs the gravity model to explore the nexus between the macroeconomic variable and bilateral trade performance. Consistent with the previous literature (Dell’Ariccia, 1999;Waugh, 2010;Kang, 2016;Egger and Staub, 2016;Nguyen and Vo, 2017), we also employ a modified gravity model for our panel data set. The stand of the gravity model is that the international trade (bilateral exports, imports or total trade) between two economies is proportional to their size (level of output) and inversely proportional to the distance between them. This model has gained substantial popularity as it allows researchers to account for the bilateral difference in country characteristics such as exchange rate volatility, common border, common language and any other important bilateral characteristics of the countries for which the model is termed as modified gravity model. Therefore, the baseline model can be presented as: Bi Tradeijt ¼ α þγ1ExRate Volijt þγ2Level of Outputijt þγ3Output Volijt þγ4Level of Incomeijt þγ5Bi Distanceij þγ6Languageij þγ7Borderij þ ε ijt where iand jdenote the importer and counterpart country, respectively, and tindicates time. The dependent variable in the aforementioned equation is Bi_Trade ijt , which refers to the log of annual bilateral exports between iand jand vice versa in year tand is measured using the following method (Larra ın and Tavares, 2003). Bi_Tradeijt ¼ Exportijt GDPit þExportjit GDPjt 2 ExRate_Volijt denotes exchange rate volatility between the two countries at year tand refers to the unexpected movement in the bilateral trade due to the uncertainty of receipt of the domestic currency. Volatility or fluctuation of the exchange rate is also referred to as exchange rate risk, which is normally measured by the SD. Following Larra ın and Tavares (2003), we use the SD of the exchange rate as a measure of exchange rate volatility. We first calculate the bilateral exchange rate and then estimate the SD using the following equations: Exchange Rateijt ¼Nominal Exchange Rateijt 3Consumer Price Indexit Consumer Price Indexjt ExRate_Volijt ¼Standard Deviation Exchange Rateijt ARCH model has been extensively applied to determine the exchange rate volatility in the area of financial economics (Hasanov et al., 2011). Along with SD, we also use the ARCH model to measure the volatility of the exchange rate as well as to confirm the robustness of the results. The conditional variance, σ 2 ijt ¼β0þβ1 ε 2 ijt−1þβ2 ε 2 ijt−2þ...þβm ε 2 ijt−m where ε 2 ijt is the squared residuals and βare the ARCH estimates. Level of Outputijt denotes the output of the economy, measured as the mean of the log of GDP of countries iand jin year t. Following Nguyen and Vo (2017), output level is measured using the following equation: Effect of exchange rate uncertainty 37 Level of Outputijt ¼lnðGDPÞit þlnðGDPÞjt 2 We control volatility of output (Output_Volijt), which is constructed as the SD of the growth of the log of GDP of between iand j. Moreover, domestic demand is a key factor of international trade. In line with the previous study, we control the level of income (Level of Incomeijt)asa proxy of market demand of countries iand j. This can be measured using the following formula: Level of Incomeijt ¼lnðPer Capita GDPÞit þlnðPer Capita GDPÞjt 2 We also control geographical distance between iand jðDistanceijÞ, which refers to the natural logarithm of the distance (in kilometers) between two capital cities of the two trading partner i and j. The higher the increase in distance associated with higher costs of transportation. An increase in the transportation cost leads to increase in the unit price of final goods for selling, thus decreasing its demand. Hence, a negative effect on bilateral trade for this variable is expected. Languageij and Borderij represent the dummy of common language and common border between iand j. Finally, ε ijt is the error term. 3.2 Data sources Our sample consists of bilateral trade between eight SAARC members –Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, Pakistan and Sri Lanka over the period 2005–2018. Annual bilateral export data are obtained from the IFS (IMF) database. Volatility of exchange rate, level of output, volatility in output and income level between country iand jare calculated using the SAARC Finance database. The details about the data definition and sources are reported in Table A1. However, Table 2 illustrates the descriptive statistics of the transformed variables. The total number of country-pair groups for the gravity model analysis is 56. The correlation matrix among the variables is presented in Table 3. Except for the level of output and common language similarities, all other variables are negatively correlated with trade performance of the SAARC country-pair. Most of the correlation coefficients of the variables are found to be lower than 0.6; therefore, multicollinearity is not a significant issue in our analysis. Moreover, we also employ variance inflation factor (VIF) test (Table A2) and find that the mean value of VIF is 1.36 (<10), which also implies that multicollinearity is very low in the OLS model (Kutner et al., 2004). 3.3 Some basic stylized facts To analyze the potential effects of volatility on bilateral trade performance, we first provide some stylized facts and examples from our country-level annual data to offer a basic Variable Obs Mean Median Std. Dev Min Max Bilateral Trade 718 4.369097 4.634518 3.008702 0 9.721754 ExRate_Vol 784 2.16e-10 0.3357085 1 0.3866572 4.510213 Level of output 718 10.41026 10.43137 1.535177 6.570067 13.72588 Output_Vol 680 5.866776 6.140284 1.859783 0.5704427 9.393717 Level of income 718 7.219996 7.217312 0.6387917 5.708571 8.788816 Bilateral distance 784 7.364199 7.628828 0.6733352 5.925998 8.133459 Language 784 0.0114821 0 0.0414679 0 0.2166 Border 784 0.1785714 0 0.3832375 0 1 Table 2. Summary statistics of the variables ITPD 5,1 38 Variables Bi_Trade ExRate_Vol Level of output Output_vol Level of income Bi_Distance Language Border Bi_Trade 1.0000 ExRate_Vol 0.0753* 1.0000 Level of output 0.5380* 0.1955* 1.0000 Output_vol 0.1349* 0.0172 0.1151* 1.0000 Level of income 0.1699* 0.0236 0.0588 0.1409* 1.0000 Bi_Distance 0.6547* 0.1690* 0.1322* 0.1968* 0.3549* 1.0000 Language 0.2585* 0.1449* 0.1411* 0.0158 0.1698* 0.4351* 1.0000 Border 0.3486* 0.0403 0.4743* 0.2178* 0.2038* 0.4576* 0.0947* 1.00000 Note(s): * Significant at 10% level Table 3. Correlation matrix of variables Effect of exchange rate uncertainty 39 border on trade is that Sri Lanka and Maldives do not have any physical common border with other SAARC countries. Besides, SAARC countries are operating on outdated and poor corridor management system. Studies found that modernization of cross-border facilities such as implementation of paperless trade, one-stop customs procedures and single window system could reduce trade cost and time between countries (Roy and Xiaoling, 2020). Additionally, political interest as well as national benefits might be the main consideration for bilateral trade among SAARC nations. 6. Conclusion This study investigates the effect of uncertainty arising from exchange rate volatility on bilateral trade using annual data from 2005 to 2018 on eight SAARC countries. Our model specification is modified gravity model of trade, and we initially employ pooled OLS, RE and FE estimation techniques to obtain the estimate of exchange rate uncertainty. We find that there exists a negative and significant relationship between uncertainty and trade performance, which implies that excessive volatility reduces trade flows between SAARC countries. We use one-year lag of main explanatory variable in all models and two-step system GMM estimation techniques to control the problem of a potential simultaneity bias, heteroskedasticity and autocorrelation. The empirical findings from the GMM estimation also confirm the adverse effect of exchange rate uncertainty on trade flows and point out that this negative relationship is not driven by simultaneous causality bias. Future direction of the research in this area should look at more industry-level and sectorwise disaggregated data. Exchange rate volatility might have a different shock across different sectors and industries. Policy initiatives to hedge against unpredicted volatility of the exchange rate are required to encourage the bilateral trade flow in SAARC region. The member countries should step forward to set up a transparent exchange rate system so that the stability of the exchange can be maintained over a longer period. 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Effect of exchange rate uncertainty 49 Appendix Corresponding author Banna Banik can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] Variables Definitions Source Trade Goods, value of exports, free on board (FOB), US dollars (to counterpart SAARC country) Directions of Trade Statistics, IMF Nominal Exchange Rate Exchange rates, National currency per US dollar, period average, national currency per US dollar, rate International Financial Statistics, IMF Consumer Price Index (CPI) Annual CPI data: average of monthly CPI data SAARCFINANCE Database GDP Gross domestic products at market prices (USD million) SAARCFINANCE Database Total Population Population (no. in millions) SAARCFINANCE Database Common Language Common native language Geography Database of CEPII Common Border 1 for contiguity Bilateral Distance Simple distance between capitals (capitals, km) Variable VIF 1/VIF Bilateral distance 1.75 0.571008 Common border 1.64 0.610143 Level of output 1.45 0.691415 Common language 1.32 0.757116 Level of income 1.18 0.844329 Output volatility 1.1 0.911331 Exchange rate volatility 1.09 0.915359 Mean VIF 1.36 Table A1. Sources of data Table A2. Multicollinearity test ITPD 5,1 50