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09-08-2022

Asymmetric effects of exchange rate volatility on trade flows: evidence from G7

Authors: Mohsen Bahmani-Oskooee, Huseyin Karamelikli, Farhang Niroomand

Published in: Journal of Economics and Finance

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Abstract

Current trend in applied research points at application of Shin et al.’s (2014) nonlinear ARDL approach to asymmetric error-correction modeling and asymmetric cointegration. Two studies in the literature have applied these approaches to assess the short-run and long-run asymmetric effects of exchange rate volatility on aggregate exports and imports of Asian and African countries, respectively. We add to this new literature by considering the experiences of G7 countries. We find that trade flows of almost all countries are affected by volatility asymmetrically in the short run. In the long run, while French and Italian exports are boosted by increased volatility, German exports are hurt. On the other hand, decreased volatility reduces French and Italian exports. As for G7 imports, increased exchange rate volatility hurts imports of Canada, Germany, France, Italy, and the U.K. in the long run and decreased volatility boosts their imports.
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Footnotes
1
It should be acknowledged that Bahmani-Oskooee and Aftab (2017) were the first to inorduce the idea of asymmetric analysis who used export and import data at the industry level between Malaysia and the U.S.
 
2
G7 is a coalition of seven countries that have the largest and most advanced economies in the world. They all together produce almost 1/3rd of the gross domestic product globally based on purchasing power parity. It was originally founded in 1973 in response to the collapse of the exchange rate in the 1970s, the energy crisis, and the recession that followed. Although each member may have different characteristics in terms of size of their economy and composition of trade, their common goal is to find solutions to global concerns such as economics, trade, security, climate change, etc.
 
3
This section closely follows Bahmani-Oskooee and Arize (2020).
 
4
Note that income elasticities in (1) and (2) could be negative if economic growth in the world or at home is due to an increase in production of import-substitute goods (Bahmani-Oskooee 1986).
 
5
This process is referred to as normalization rule and long-run estimates are referred to as normalized estimates.
 
6
Note that estimates of θ1 in (3) and ρ1 in (4) must be negative if variables are to adjust toward their long-run equilibrium values.
 
7
Since almost all macro variables are either I(0) or I(1), there is no need for pre-unit root testing under this method.
 
8
The two partial sum variables are constructed as \(POS_t\;=\;{\textstyle\sum_{j=1}^t}\;max\;\left(\Delta\;\ln\;V_j,\;0\right)\), and \(NEG_t\;=\;{\textstyle\sum_{j=1}^t}\;min\;\left(\Delta\;\ln\;V_j\;0\right)\).
 
9
See Shin et al. (2014, p. 291).
 
10
For some other application of these methods see Gogas and Pragidis (2015), Durmaz (2015), Baghestani and Kherfi (2015), Al-Shayeb and Hatemi-J.(2016), Lima et al. (2016), Aftab et al. (2017), Arize et al. (2017), and Gregoriou (2017).
 
11
Note that in addition to two cointegration tests in Panel C of Table 1, we have also reported some additional diagnostics. The Lagrange multiplier test is reported as LM to identify models in which the residuals are correlated. Since it is insignificant in most models, residuals are autocorrelation free. Ramsey’s RESET test is also reported to identify misspecified models. Stability of estimated coefficients are tested by using the CUSUM and CUSUMSQ tests. Graphically, they are presented in Fig. 1 and the results are summarized in tables by reporting CUSUM sa CS and CUSUMSQ as CS2. Indicating stable estimates by “S” and unstable ones by “U”, clearly most estimates are stable. The CUSUM and Finally, to judge the goodness of the fit, we have reported the size of adjusted R2.
 
12
Other diagnostics are similar to those in Table 1 and no need to repeat.
 
13
One major shortcoming of our study is that the results could suffer from aggregation bias. Future research must disaggregate each of the G7 country’s trade by trading partners and estimate the models using bilateral trade data. Even at bilateral level, additional disaggegation by commodity is recommended. An example is Bahmani-Oskooee and Aftab (2017).
 
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Metadata
Title
Asymmetric effects of exchange rate volatility on trade flows: evidence from G7
Authors
Mohsen Bahmani-Oskooee
Huseyin Karamelikli
Farhang Niroomand
Publication date
09-08-2022
Publisher
Springer US
Published in
Journal of Economics and Finance
Print ISSN: 1055-0925
Electronic ISSN: 1938-9744
DOI
https://doi.org/10.1007/s12197-022-09597-5

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