U.K.-German Commodity Trade and Exchange-Rate Volatility: An Asymmetric Analysis

ABSTRACT

We assess the response of trade flows between Britain and Germany to exchange rate volatility. When we estimated a linear model, we found short-run effects of volatility in 36 British exporting industries that lasted into the long run only in 23 industries. The comparable number for German exporting industries were 42 and 17, respectively. However, when we estimated a nonlinear model, we found short-run asymmetric effects of volatility in 54 British exporting industries that lasted into the long run in 38 industries. The comparable number for German exporting industries were 64 and 42, respectively. Almost all affected industries were small.

KEYWORDS:

• 95 industries
• asymmetry
• euro-pound volatility
• U.K.- German trade

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website.

Notes

¹ Other studies that used data at the commodity or sectoral level are: Maskus (1986) who disaggregated U.S. exports to the U.K. by one-digit SITC categories and showed that exchange rate volatility has an adverse impact on only four industries. However, when Bahmani-Oskooee and Kovyryalova (2008) disaggregated U.K.-U.S. trade flows by three-digit SITC industries and included 177 three-digit industries in their analysis, they found much more support for significant effects of exchange rate volatility. While in most industries the effects of dollar-pound volatility were negative, in some industries it was positive. Doyle (2001), who considered Irish exports to the U.K. using data from two-digit industries, found positive effects of volatility in many industries. However, when De Vita and Abbott (2004) looked into U.K. exports to the EU14, they found no significant long-run effects of exchange rate volatility either at the bilateral level or at the sectoral level. None of the above studies engaged in asymmetric analysis. For details on these and other studies that have used either aggregate or bilateral data, see Bahmani-Oskooee, Huynh, and Nasir (2021).

² The appendix can be found online at www.tandfonline.com/uitj.

³ This section closely follows Bahmani-Oskooee, Huynh, and Nasir (2021).

⁴ The estimates of θ₀ in Equation (3) and ${\rho }_0$ in Equation (4) should also be negative if variables are to converge. Bahmani-Oskooee and Ghodsi (2018) and Bahmani-Oskooee (2020) have demonstrated that the t-test in this context is the same as the t-test that is used to establish the significance of a lagged error-correction term in the Engle and Granger (1987) framework.

⁵ Shin, Yu, and Greenwood-Nimmo (2014, 291) even recommend treating the two partial sum variables as a single variable so that the critical values of the F-test stay at high levels when we move from the linear to nonlinear model.

⁶ By meaningful, we mean cointegration is supported either by the F- or the t-test reported in Table 1.

⁷ Once again, when aggregate trade flows are used, the long-run effects of volatility on total U.K. exports to Germany are insignificant.

⁸ Note that in the largest German exporting industry, 87 (Vehicles other than railway or tramway rolling-stock, and parts and accessories thereof with 30.26% trade share), volatility carried a significantly negative coefficient. However, since neither the F- nor the t-test supported cointegration, it is excluded from the list.

⁹ Indeed, the Wald test was significant in a total of 38 industries, 12 of which are reported in Table 3.

¹⁰ Again, other diagnostics are similar to those of the U.K. export demand model and need no review here.

¹¹ For some other application of these methods, see Halicioglu (2007), Durmaz (2015), Gogas and Pragidis (2015), Baghestani and Kherfi (2015), Al-Shayeb and Hatemi-J (2016), Bahmani-Oskooee and Fariditavana (2016), Lima et al. (2016), Nusair (2012, 2017), Aftab, Syed, and Katper (2017), Arize, Malindretos, and Igwe (2017), and Hajilee and Niroomand (2019).