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Does exchange-rate uncertainty matter in the Malaysia–E.U. bilateral trade? An industry level investigation

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Abstract

Due to ambiguity in the past literature, researchers have examined exchange rate volatility effect on trade using disaggregated data in recent years. Previous research has focused more on aggregated data having aggregation bias which has led to unnecessarily over-generalized findings. This study investigates the impact of exchange rate volatility on the Malaysian bilateral trade flows with European Union using industry level data. Our empirical findings, based on auto-regressive distributed lag framework, suggest that many import and export industries experience exchange rate volatility influence in the short run, while a very small number of industries show this effect in the long run. Moreover, the adverse impact of financial crisis (2007–2008) is more prevalent on import industries compared to export industries.

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Notes

  1. ECM t−1 is calculated by replacing the level variables in lag form in Eqs. 3 and 4 through normalization. For instance, we calculate the for Eq. 3 as \( {\text{ECM}}_{t - 1} = \ln {\text{X}}_{t - 1} - \frac{{\beta_{2} }}{{\beta_{1} }}\ln {\text{IP}}_{t - 1}^{EU} - \frac{{\beta_{3} }}{{\beta_{1} }}{\text{lnREX}}_{t - 1} - \frac{{\beta_{4} }}{{\beta_{1} }}\ln {\text{V}}_{t - 1} \) Similarly ECM t−1 is calculated for Eq. 4. The critical values for ECM t−1 are devised by Banerjee et al. (1998).

  2. Initially we have 97 industries in dataset; however after screening the missing value cases and figuring out appropriateness for econometric modelling, we come with the final data set of 80 export and 67 import industries.

  3. As Pesaran et al. (2001) point out that critical value should be modified if the fraction of periods with non-zero dummy variables does not tend to zero with the sample size T. In this study, the fraction of observations where dummy variable is non zero is only 7.14 %. So we are confident in the validity of our results.

  4. We have constructed the Malaysia–E.U bilateral trade by aggregating the Malaysian bilateral trade with twenty-eight European trading partners for each individual industry using SAS 9.1 data management tools.

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Authors and Affiliations

  1. Department of Finance and Banking, Faculty of Business and Accountancy, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Muhammad Aftab, Rubi Ahmad & Izlin Ismail

  2. Department of Management Sciences, COMSATS Institute of Information Technology, Park Road, Chak Shehzad, Islamabad, 44000, Pakistan

    Mumtaz Ahmed

Authors
  1. Muhammad Aftab
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  2. Rubi Ahmad
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  3. Izlin Ismail
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  4. Mumtaz Ahmed
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Corresponding author

Correspondence to Muhammad Aftab.

Appendix: Data and sources

Appendix: Data and sources

Sources

Monthly data are used over the period Jan-2000 to Dec-2013 to conduct this empirical study. The data is retrieved from the following three sources:

  1. a.

    External Trade Statistics, Department of Statistics Malaysia,

  2. b.

    Datastream, Thomson Reuters

  3. c.

    International Financial Statistics (IFS), International Monetary Fund (IMF)

For each industry out of total 80 Malaysian exporting industries to EU and 67 Malaysian importing industries from E.U.Footnote 2 (as per HS-2 digit code), the import and export data are taken from source a. While data for all other variables come from sources b except CPI data which are from source c.

Variables

D FC = 1, over the year Jan-2008 to Dec-2008, 0 elsewhere,Footnote 3 X i   = Natural logarithm of Malaysian export volume to E.U.Footnote 4 denominated in Malaysian ringgits for i-th industry. M i  = Natural logarithm of Malaysian import volume from E.U. denominated in Malaysian ringgits for i-th industry. IP EU = Natural logarithm of European industrial production index. IP M = Natural logarithm of Malaysian industrial production index. REX t  = Natural logarithm of real bilateral exchange rate (Malaysian ringgit/euro) calculated as \( REX_{t} = \frac{{(NEX_{t} )(CPI_{t}^{EU} )}}{{CPI_{t}^{Mal} }} \) where NEX t is nominal bilateral exchange rate (Malaysian ringgit/euro) and \( CPI_{t}^{EU} \) and CPI Mal t are consumer price indices for E.U. and Malaysia respectively. V = Volatility measure of REX. We find the presence of ARCH effect in the REX series so we use GARCH (p, q) model proposed by Bollerslev (1986) to measure \( REX \) volatility.

$$REX_{t} = \lambda_{o} + \sum\limits_{i = 1}^{k} {\lambda_{i} REX_{t - i} + \varepsilon_{t} ;\quad \varepsilon_{t} \, \sim N(0,h_{t}^{2} )}$$
(5)
$$h_{t}^{2} = \psi + \sum\limits_{i = 1}^{q} {\eta_{i} \varepsilon^{2} } + \sum\limits_{i = 1}^{p} {\zeta_{i} h^{2} }$$
(6)

Equation 5 is a conditional mean autoregressive (AR) process of order k. Using Schwarz information criterion (SIC) and Hannan-Quinn information criterion (HIC), the optimal lag length is found one. Equation 6 is conditional variance combining ARCH and GARCH terms (\( h^{2} {\text{ and }}\, \varepsilon^{2} \) respectively) and collectively called GARCH (p, q). We consider GARCH (1, 1) appropriate for estimating V.

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Aftab, M., Ahmad, R., Ismail, I. et al. Does exchange-rate uncertainty matter in the Malaysia–E.U. bilateral trade? An industry level investigation. Empirica 43, 461–485 (2016). https://doi.org/10.1007/s10663-015-9302-6

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