Abstract
Aftab et al. (Empirica 43:461–485, 2016) in this journal assessed the impact of exchange rate volatility on Malaysia-EU trade at commodity level using the linear ARDL approach of Pesaran et al. (J Appl Econom 16:289–326, 2001) and did not find significant effects in most of the 81 Malaysian exporting and 66 importing industries. In this paper, we argue for asymmetric effects of exchange rate volatility on the same industries’ trades which implies using Shin et al.’s (Festschrift in Honor of Peter Schmidt, Springer, New York, 2014) nonlinear ARDL approach. While we find short-run asymmetric effects of volatility in almost all industries, we find evidence of adjustment asymmetry in 17 exporting and nine importing industries. We also find significant impact or short-run cumulative asymmetry in 12 exporting and six importing industries. The most important finding is significant long-run asymmetric effects in 36 Malaysian exporting industries and 25 Malaysian importing industries. Clearly, trade flows react to an increased exchange rate volatility differently than to a decreased volatility.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
For experiences of other countries see the review article by Bahmani-Oskooee and Hegerty (2007).
These are all 2-digit industries of Harmonized System (HS) for which continuous time-series data over the study period were available.
Pesaran et al. (2001) supply new critical values for this F test that account for integrating properties of variables. They demonstrate that their upper bound critical values could be used when variables are combination of I(0) and I(1). Since these are properties of almost all macro variables, there is no need for unit root testing here. Indeed, in our case, there was no I(2) variable.
At any point in time, the partial sum of positive values of a variable is the same as cumulative sum where the negative values are replaced by zeros. Similarly, the partial sum of negative values at a given time is the cumulative sum where positive values are replaced by zeros. For more on their formulas to generate the partial sums see Bahmani-Oskooee and Fariditavana (2016). More precisely,
\( POS_{t} = \sum\limits_{j = 1}^{t} {\Delta LnV_{j}^{ + } } = \sum\limits_{j = 1}^{t} {\hbox{max} (\Delta LnV_{j} } ,0),\quad and\quad NEG_{t} = \sum\limits_{j = 1}^{t} {\Delta LnV_{j}^{ - } } = \sum\limits_{j = 1}^{t} {\hbox{min} (\Delta LnV_{j} ,0).} \, \)
This recommendation is based on the dependency between POS and NEG variables.
For other applications of the partial sum concept and nonlinear ARDL approach see Apergis and Miller (2006), Delatte and Lopez-Villavicencio (2012), Verheyen (2013), and Bahmani-Oskooee et al. (2016). For applications of the linear model see De Vita and Abbott (2004), Narayan et al. (2007), Wong and Tang (2008), De Vita and Kyaw (2008), Halicioglu (2007), Hajilee and Al-Nasser (2014), and Durmaz (2015).
Note this list of 36 exporting industries includes even industries in which neither the POS nor the NEG variable was significant, due to the fact that we are testing the size difference. For example in industry coded 01, the POS variable carries a coefficient estimate of 0.2209 and the NEG variable carries an estimate of 0.4024. The Wald-L reveals that these are significantly different.
Note that the appropriate exchange rate should be the one that accounts for currencies of countries that are part of EU but not part of euro-zone. However, using ringgit-euro rate should not pose a major issue for two reasons. First, because of synchronization between the Euro and non-Euro members' currencies due to globalization all exchange rates within EU move together (Moore 2007). Indeed, the correlation coefficients between ringgit-euro rate and nine ringgit-non euro zone currencies was above 0.9 for most of them. Second, close to 85% of Malaysian trade is with 19 euro-zone countries. Future research should concentrate on constructing an effective exchange rate between each non-EU and non-euro zone country.
References
Aftab M, Ahmad R, Ismail I, Ahmed M (2016) Does exchange-rate uncertainty matter in the Malaysia-E.U. bilateral trade? An industry level investigation. Empirica 43:461–485
Apergis N, Miller S (2006) Consumption asymmetry and the stock market: empirical evidence. Econ Lett 93:337–342
Arize AC, Osang T, Slottje DJ (2000) Exchange-rate volatility and foreign trade: evidence from thirteen LDCs. J Bus Econ Stat 18:10–17
Bahmani-Oskooee M (1986) Determinants of international trade flows: case of developing countries. J Dev Econ 20:107–123
Bahmani-Oskooee M, Fariditavana H (2016) “Nonlinear ARDL Approach and the J-curve phenomenon. Open Econ Rev 27:51–70
Bahmani-Oskooee M, Hegerty SW (2007) Exchange rate volatility and trade flows: a review article. J Econ Stud 34:211–255
Bahmani-Oskooee M, Harvey H, Aftab M (2016) Asymmetry cointegration and the J-curve: new evidence from Malaysia-Singapore commodity trade. J Econ Asymmetries 14:211–226
Bussiere M (2013) Exchange rate pass-through to trade prices: the role of nonlinearities and asymmetries. Oxford Bull Econ Stat 75:731–758
De Grauwe P (1988) Exchange rate variability and the slowdown in growth of international trade. IMF Staff Papers, pp 63–84
De Vita G, Abbott A (2004) Real exchange rate volatility and US exports: an ARDL bounds testing approach. Econ Issues 9:69–78
De Vita G, Kyaw KS (2008) Determinants of capital flows to developing countries: a structural VAR analysis. J Econ Stud 35:304–322
Delatte A-L, Lopez-Villavicencio A (2012) Asymmetry exchange rate pass-through: evidence from major countries. J Macroecon 34:833–844
Doganlar M (2002) Estimating the impact of exchange rate volatility on exports: evidence from Asian Countries. Appl Econ Lett 9:859–863
Doroodian K (1999) Does exchange rate volatility deter international trade in developing countries? J Asian Econ 10:465–474
Durmaz N (2015) Industry level J-curve in Turkey. J Econ Stud 42:689–706
Hajilee M, Al-Nasser OM (2014) Exchange rate volatility and stock market development in emerging economies. J Post Keynes Econ 37:163–180
Halicioglu F (2007) The J-curve dynamics of turkish bilateral trade: a cointegration approach. J Econ Stud 34:103–119
Moore T (2007) Has entry to the European Union altered the dynamic links of stock returns for the emerging markets? Appl Financ Econ 17(17):1431–1446
Narayan PK, Narayan S, Prasad BC, Prasad A (2007) Export-led growth hypothesis: evidence from Papua New Guinea and Fiji. J Econ Stud 34:341–351
Peree E, Steinherr A (1989) Exchange rate uncertainty and foreign trade. Eur Econ Rev 33:1241–1264
Pesaran MH, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econom 16:289–326
Shin Y, Yu B, Greenwood-Nimmo M (2014) Modelling asymmetric cointegration and dynamic multipliers in a nonlinear ARDL framework. In: Sickles RC, Horrace WC (eds) Festschrift in Honor of Peter Schmidt. Springer, New York, pp 281–314
Verheyen F (2013) Interest Rate pass-through in the EMU-new evidence using nonlinear ARDL framework. Econ Bull 33(1):729–739
Wong KN, Tang TC (2008) The effects of exchange rate variablity on Malaysia’s disaggregated electrical exports. J Econ Stud 35:154–169
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
1.1 Data definition and sources
As mentioned the data set is the same as Aftab et al. (2016). Monthly data over the period June-2000 to Dec-2013 come from the following sources:
-
(a)
External Trade Statistics, Department of Statistics Malaysia,
-
(b)
Datastream, Thomson Reuters,
-
(c)
International Financial Statistics (IFS), International Monetary Fund (IMF)
1.2 Variables
X i = Malaysian real export flows to EU for each industry i. In the absence of the price level at the industry level on a monthly basis for our study period, nominal exports in terms of ringgit from source (a) are deflated by Malaysian CPI, from source (c).
M i = Malaysian real import flows from EU for each industry i. Again, in the absence of the price level at the industry level on a monthly basis, nominal imports in terms of ringgit (source a) are deflated by Malaysian CPI (source c).
IP EU t = EU industrial production index is used as a measure of economic activity in EU. Source b.
IP MY t = Malaysian industrial production index, source b. It should be noted that since index of industrial production excludes services and agricultural sectors, size of each economy is somewhat underrepresented using only the industrial sector of the destination market.
REX t = Real bilateral exchange rate calculated as \( REX_{t} = \frac{{NEX_{t} *CPI_{t}^{EU} }}{{CPI_{t}^{ML} }} \) where \( NEX_{t} \) is a nominal bilateral exchange rate defined as the number of Malaysian ringgit per euro. \( CPI_{t}^{EU} \) and \( CPI_{t}^{ML} \) are consumer price indices for EU and Malaysia, respectively. All data come from source c.Footnote 8
V t = Volatility measure of \( REX_{t} \) based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH 1, 1). See Aftab et al. (2016) for details.
Rights and permissions
About this article
Cite this article
Bahmani-Oskooee, M., Aftab, M. Malaysia-EU trade at the industry level: Is there an asymmetric response to exchange rate volatility?. Empirica 45, 425–455 (2018). https://doi.org/10.1007/s10663-017-9367-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10663-017-9367-5