Skip to main content
Log in

Exchange-rate volatility and commodity trade between the E.U. and Egypt: evidence from 59 industries

  • Original Paper
  • Published:
Empirica Aims and scope Submit manuscript

Abstract

In recent years, the effects of exchange-rate risk on trade flows have been studied for numerous cases, with studies focusing on disaggregated, industry-level exports and imports for a pair of countries’ import and export volumes. This study examines the specific case of Egypt’s trade with the European Union, applying cointegration analysis to quarterly data for 59 industries’ export and import flows. We find that, compared to studies of other countries, relatively few trade flows respond to increased risk in the short run. In the long run, however, a large proportion responds negatively. These include the major industries of petroleum and gas. Further analysis suggests that non-manufactures are particularly susceptible to increased risk, particularly for Egyptian exports.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. Indeed, our preliminary exercise showed that almost all variables were I(1) and a few were I(0). There was no I(2) variable.

  2. For example the ECMt−1 which will replace the linear combination of lagged level variables in model (1) is calculated as \( ECM_{t - 1} = LnIMP_{t - 1} - \frac{{\hat{\theta }_{2} }}{{\hat{\theta }_{1} }}LnY_{t - 1}^{EG} - \frac{{\hat{\theta }_{3} }}{{\hat{\theta }_{1} }}LnPM_{t - 1} - \frac{{\hat{\theta }_{4} }}{{\hat{\theta }_{1} }}LnVOL_{t - 1} \). The same applies for Eq. (2).

    Note that standard errors of normalized coefficients are calculated using non-linear least square technique and the Delta method. These standard errors are then used to calculate the t-ratios.

  3. For other applications of this approach see Bahmani-Oskooee et al. (2005), Bahmani-Oskooee and Hegerty (2007), Halicioglu (2007), Narayan et al. (2007), Tang (2007), Mohammadi et al. (2008), Wong and Tang (2008), De Vita and Kyaw (2008), Payne (2008), Chen and Chen (2012), and Wong (2013).

  4. Note that while critical values of the F test come from Pesaran et al. (2001), those for ECMt−1 come from Banerjee et al. (1998).

References

  • Abu Hatab AR, Shoumann NA, Xuexi H (2012) Exploring Egypt-China bilateral trade: dynamics and prospects. J Econ Stud 39(3):314–326

    Article  Google Scholar 

  • Achy L, Sekkat K (2003) The European single currency and MENA’s exports to Europe. Rev Dev Econ 7(4):563–582

    Article  Google Scholar 

  • Bahmani-Oskooee M, Hegerty SW (2007) Exchange rate volatility and trade flows: a review article. J Econ Stud 34:211–255

    Article  Google Scholar 

  • Bahmani-Oskooee M, Hegerty SW (2009a) The effects of exchange-rate volatility on commodity trade between the U.S. and Mexico. South Econ J 79:1019–1044

    Google Scholar 

  • Bahmani-Oskooee M, Hegerty SW (2009b) Exchange-rate risk and U.S.-Japan trade: evidence from industry level data. J Jpn Int Econ 22(4):518–534

    Article  Google Scholar 

  • Bahmani-Oskooee M, Hosny AS (2013) Long-run price elasticities and the Marshall–Lerner condition: evidence from Egypt–EU commodity trade. Eur J Dev Res 25:695–713

    Article  Google Scholar 

  • Bahmani-Oskooee M, Economidou C, Goswami GG (2005) How sensitive are Britain’s inpayments and outpayments to the value of the British pound. J Econ Stud 32:455–467

    Article  Google Scholar 

  • Bahmani-Oskooee M, Bolhasani M, Ardalani Z (2010) Exchange-rate volatility and U.S. commodity trade with the rest of the world. Int Rev Appl Econ 24:511–532

    Article  Google Scholar 

  • Bahmani-Oskooee M, Bolhasani M, Hegerty SW (2012a) Exchange-rate volatility and industry trade between Canada and Mexico. J Int Trade Econ Dev 21(3):391–410

    Article  Google Scholar 

  • Bahmani-Oskooee M, Harvey H, Hegerty SW (2012b) Exchange-rate volatility and industry trade between the U.S. and Korea. J Econ Dev 37(1):1–25. (With M. Bahmani-Oskooee and H. Harvey)

    Google Scholar 

  • Bahmani-Oskooee M, Harvey H, Hegerty SW (2012c) Exchange-rate volatility and Sweden’s trade with Germany: evidence from industry data. In: Pedersen ML, Christoffersen J (eds) Nordic countries: economic, political and social issues. Nova Science, New York

    Google Scholar 

  • Bahmani-Oskooee M, Harvey H, Hegerty SW (2013) The effects of exchange-rate volatility on commodity trade between the U.S. and Brazil. N Am J Econ Financ 25:70–93

    Article  Google Scholar 

  • Banerjee A, Dolado JJ, Mestre R (1998) Error-correction mechanism tests for cointegration in a single-equation framework. J Time Ser Anal 19:267–283

    Article  Google Scholar 

  • Chen S-W, Chen T-C (2012) Untangling the non-linear causal nexus between exchange rates and stock prices: new evidence from the OECD countries. J Econ Stud 39:231–259

    Article  Google Scholar 

  • De Vita G, Kyaw KS (2008) Determinants of capital flows to developing countries: a structural VAR analysis. J Econ Stud 35:304–322

    Article  Google Scholar 

  • Giorgioni G, Thompson JL (2002) Which volatility? The case of the exports of wheat. Appl Econ Lett 9(4):681–684

    Article  Google Scholar 

  • Halicioglu F (2007) The J-curve dynamics of Turkish bilateral trade: a cointegration approach. J Econ Stud 34:103–119

    Article  Google Scholar 

  • Kulkarni KG (1996) The J-curve hypothesis and currency devaluation: cases of Egypt and Ghana. J Appl Bus Res 12(2):1–8

    Google Scholar 

  • McKenzie MD (1999) The impact of exchange rate volatility on international trade flows. J Econ Surv 13:71–104

    Article  Google Scholar 

  • Mohammadi H, Cak M, Cak D (2008) Wagner’s hypothesis: new evidence from Turkey using the bounds testing approach. J Econ Stud 35:94–106

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Payne JE (2008) Inflation and inflation uncertainty: evidence from the Caribbean Region. J Econ Stud 35:501–511

    Article  Google Scholar 

  • Pesaran MH, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econom 16:289–326

    Article  Google Scholar 

  • Tang TC (2007) Money demand function for Southeast Asian countries: an empirical view from expenditure components. J Econ Stud 34:476–496

    Article  Google Scholar 

  • Wong HT (2013) Real exchange rate misalignment and economic growth in Malaysia. J Econ Stud 40:298–313

    Article  Google Scholar 

  • Wong KN, Tang TC (2008) The effects of exchange rate variablity on Malaysia’s disaggregated electrical exports. J Econ Stud 35:154–169

    Article  Google Scholar 

Download references

Acknowledgments

Authors would like to appreciate the valuable comments of two anonymous referees.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohsen Bahmani-Oskooee.

Appendix: Data definition and sources

Appendix: Data definition and sources

Quarterly data over 1994Q1-2007Q4 are used to carry out the empirical analysis. The data sources are as follows:

  1. (a)

    Central Agency for Public Mobilization and Statistics (CAPMAS), Arab Republic of Egypt.

  2. (b)

    EuroStat Online Database.

  3. (c)

    Ministry of Economic Development, Arab Republic of Egypt.

  4. (d)

    International Financial Statistics IMF (CD-ROM).

1.1 Variables

IMP i = For each commodity i, IMP is the (log) volume of Egyptian imports from the European Union. It is defined as (log) the ratio of the value of Egyptian imports from the European Union (EU) over the respective import price of commodity i. The imports data for 59 industries come from source a.

EXP i = For each commodity i, EXP is the (log) volume of Egyptian exports to the European Union. It is defined as (log) the ratio of Egyptian exports to the European Union (EU) over the respective export price of commodity i. The exports data for 59 industries come from source a.

Y EU  = EU (log) real GDP. The data come from source b.

Y EG  = Egyptian (log) real GDP. The data come from source c.

REX = Real bilateral exchange rate between the Egyptian pound and the EU euro (in logs). It is defined as (P EU .NEX/P EG ) where P EU is the price level in EU, NEX is the nominal bilateral exchange rate defined as number of Egyptian pounds per EU euro, and P EG is the price level in Egypt. Thus, an increase in REX reflects a real depreciation of the Egyptian pound. The CPI data (used as a proxy for P) and the NEX data come from source d.

VOL = Volatility measure of the real bilateral exchange rate (REX). It is estimated from a GARCH (1,1) model. As explained in detail in Bahmani-Oskooee et al. (2010), when a GARCH (p,q) specification is used to generate the volatility measure, square of conditional variance of the residuals from a first-order autoregressive model of REX is regressed on p lags of the squared residuals themselves and q lags of their conditional variance. The order of lags is judged by the significance of the lags. In most cases GARCH (1,1) yielded significant order. The data come from source d.

PM i = For each commodity i, PM is defined as (log) the ratio of import price of commodity i over P EG the price level in Egypt. The import price data for 59 industries come from source a, while the CPI data (used as a proxy for P EG ) come from source d.

PX i = For each commodity i, PX is defined as (log) the ratio of export price of commodity i over P EU the price level in EU. The export price data for 59 industries come from source a, while the CPI data (used as a proxy for P EU ) come from source d.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bahmani-Oskooee, M., Hegerty, S.W. & Hosny, A. Exchange-rate volatility and commodity trade between the E.U. and Egypt: evidence from 59 industries. Empirica 42, 109–129 (2015). https://doi.org/10.1007/s10663-014-9250-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10663-014-9250-6

Keywords

JEL Classification

Navigation