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Exchange rate volatility and domestic investment in G7: are the effects asymmetric?

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Abstract

One strand of the literature in exchange rate economics argues and demonstrates that exchange rate uncertainty could affect domestic investment in either direction. In this paper, we argue and demonstrate that the effects of exchange rate volatility on domestic investment could be asymmetric, meaning that increased uncertainty may have different effect in size or direction than decreased uncertainty. We use data from each of the G7 countries and how those effects are asymmetric in the short run in almost all seven countries. However, short-run asymmetric effects translate into the long-run only in Germany and the U.S. While in the U.S., long-run effects are asymmetric, in Germany they are not.

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Notes

  1. Note that this model without the measure of exchange rate volatility was recently used by Bahmani-Oskooee and Baek (2020) to assess the asymmetric effects of exchange rate itself. We go beyond their model and add the volatility measure to assess asymmetric effects of exchange rate volatility.

  2. Note that by way of construction, a decline in the real effective exchange rate implies a depreciation of the domestic currency.

  3. This is known as normalization and estimates are known as normalized long-run effects. If we match normalized long-run estimates to elasticities in (1), we will have: \(\frac{{\hat{\beta }_{1} }}{{ - \hat{\beta }_{0} }} = \hat{b}\); \(\frac{{\hat{\beta }_{2} }}{{ - \hat{\beta }_{0} }} = \hat{c}\); \(\frac{{\hat{\beta }_{3} }}{{ - \hat{\beta }_{0} }} = \hat{d}\); and \(\frac{{\hat{\beta }_{4} }}{{ - \hat{\beta }_{0} }} = \hat{e}\).

  4. Bahmani-Oskooee (2020) has demonstrated that the t test here is exactly the t-test applied to test the significance of lagged error-correction term in Engle and Granger (1987) setting due to Banerjee et al. (1998).

  5. Note that the two main advantages of this approach are that short-run and long-run coefficients are estimated in one step and that variables could be a combination of I(0) and I(1).

  6. For some other applications see Apergis and Miller (2006), Arize et al. (2017), Halicioglu (2007), Delatte and Lopez-Villavicencio (2012), Nusair (2012, 2016), Gogas and Pragidis (2015), Durmaz (2015), Baghestani and Kherfi (2015), Al-Shayeb and Hatemi (2016), Lima et al. (2016), Aftab et al. (2017), Gregoriou (2017), Olaniyi (2019), Istiak and Alam (2019), and Hajilee and Niroomand (2019).

  7. Other diagnostics are also similar to those of the linear model, except that stability of the estimates is supported by both the CUSUM and CUSUMSQ tests.

  8. The Wald test reported as Waldlong is insignificant.

  9. Note that diagnostic statistics for all countries are similar to those of Canada and need no repeat. We also used Schwarz Bayesian Criterion (SBC) in selecting lag orders. Although it selected smaller number of lags, there was no change in overall conclusion.

  10. Note that interest rates in four countries (France, Germany, Italy, and Japan) have turned into negative in recent years (since 2015Q2). Thus, the log was not taken for the interest rate in these four countries.

  11. Note that while we concentrate on exchange rate uncertainty, Ebeke and Siminitz (2018) assess the impact of trade uncertainty on investment in the euro area and Hlatshwayo (2018) uses policy uncertainty. Future research should consider the trade uncertainty as an alternative measure of uncertainty.

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Authors and Affiliations

  1. The Center for Research on International Economics and The Department of Economics, The University of Wisconsin-Milwaukee, Milwaukee, WI, 53201, USA

    Mohsen Bahmani-Oskooee

  2. Department of Economics, School of Management, University of Alaska Fairbanks, Fairbanks, USA

    Jungho Baek

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  1. Mohsen Bahmani-Oskooee
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Correspondence to Mohsen Bahmani-Oskooee.

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Appendix

Appendix

1.1 Data sources and definition

Quarterly data over the period 1980Q1-2018Q2 are used for Canada, France, Japan, the United Kingdom, and the United States. Due to missing observations, the period is limited to 1986Q1-2018Q2 for the UK, 1991Q1-2018Q2 for Germany and 1995Q1-2018Q2 for Italy.

1.2 Sources

a. International Financial Statistics of the International Monetary Fund (IMF).

b. Statistics of the Organization for Economic Cooperation and Development (OECD).

1.3 Variables

I = Real gross capital formation (source a). Nominal figures are deflated by GDP deflator obtained from the source a.

Y = Real GDP (source a). Nominal figures are deflated by GDP deflator obtained from the source a.

r = Domestic interest rates defined as the three month T-bill rates (source b).Footnote 10

REX = Real effective exchange rate (source a). A decline in REX reflects a real depreciation of the domestic currency.

VOL = Volatility measure of REXt based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH 1, 1). Following Bahmani-Oskooee and Aftab (2017) we assume that our variable REX is random and it follows a first-order auto-regressive process, i.e. REXt = σ0 + σ1 REXt-1 + εt, where εt is white noise with E(ε) = 0 and V(ε) = h2. To forecast the variance of REX, the conditional variance of εt which is a time-varying variable needs to be estimated.Footnote 11

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Bahmani-Oskooee, M., Baek, J. Exchange rate volatility and domestic investment in G7: are the effects asymmetric?. Empirica 48, 775–799 (2021). https://doi.org/10.1007/s10663-020-09488-0

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