The role of crude oil demand and supply shocks on exchange rates: empirical evidence from South Korea

ABSTRACT

The primary contribution of the present paper is to explicitly take into consideration the role that the three different components of crude oil shocks – oil supply shocks, aggregate demand shocks, and oil-specific demand shocks – when studying the nexus between oil price shocks and the South Korean won (KRW). We discover that oil demand shocks have had significant impacts on KRW over the past two decades: that is, an upsurge in oil prices driven by positive shocks in aggregate demand and oil-specific demand appears to appreciate KRW. However, oil supply shocks turn out to have negligible impacts. We also unveil that two oil demand shocks seem to asymmetrically influence KRW in the short run.

KEYWORDS:

• KRW
• oil demand shocks
• oil supply shocks
• South Korea
• SVAR

JEL CLASSIFICATION:

• C32
• F31
• Q43

Acknowledgments

I thank the two referees for their careful reading of our manuscript and their many insightful comments and suggestions that improved the quality of the initial manuscript. Any remaining errors are my sole responsibility. This research is financially supported by the award from the University of Alaska Foundation Harold T. Caven Professorship.

Disclosure statement

No potential conflict of interest was reported by the authors.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Notes

¹ It should be pointed out that our paper is part of a larger literature on the macroeconomic impacts of supply and demand shocks in the global crude oil market. For instance, Lee and Cho (2021) study the effects of oil supply and demand shocks on five macro-variables – consumer price index, producer price index, index of manufacturing production, GDP, and private consumption – in the case of South Korea. Mokni (2020) assesses the impacts of different oil shock components on stock market returns in four oil exporters and four oil importers including South Korea.

² Examples comprise McLeod and Haughton (2018), Kisswani, Harraf, and Kisswani (2019), Baek and Kim (2019), and Baek (2021b).

³ Ji et al. (2020) also address the impacts of the different oil shock components on the currencies of oil importers including KRW. However, they do not consider the asymmetry of oil price shocks in their assessment.

⁴ For more details, refer to Kilian (2009).

⁵ The advantage of SVAR over traditional macroeconometric models is that the estimated outcomes are not hidden by a large and complicated structure (so-called black box) but are easily estimated and interpreted. Another advantage is the necessary restrictions required for the identification of the underlying structural model can easily be provided by economic theory. However, the outcomes from SVAR are quite sensitive to identification restrictions. In addition, the structural shocks provided by SVAR may reflect omitted variables in the model. Thus, if these omitted variables correlated with the included variables, the estimated structural shocks could be biased.

⁶ We use natural logarithms of$\hspace{0.17em}pr{o}_t$and $rp{o}_t$.

⁷ The real effective exchange rate gauges the strength of a home currency relative to a basket of other currencies. In the present article, therefore, an increase in $e{r}_t$ means a real appreciation of KRW.

⁸ Since NARDL is an extension of Pesaran, Shin, and Smith (2001)autoregressive distributed lag (ARDL), which can be adopted regardless of whether series are I(0) or I(1), it basically rules out pre-unit root testing. Another advantage of NARDL is both the short- and long-run dynamics among explanatory variables can be estimated simultaneously using a single step. However, since NARDL is a single-equation approach, it may not be able to correct the potential endogeneity of the explanatory variables, thereby yielding inefficient estimates of the short- and long-run relationships. Further, the NARDL uses the cointegrating relationship as a prerequisite to estimate the short-run dynamics, so it cannot be estimated without identifying the existence of a cointegration relationship.

⁹ For example, $s{s}_t^{+}=\sum _{j=1}^t\mathrm{m}\mathrm{a}\mathrm{x}\left[\mathrm{\Delta }ln\left(s{s}_t,0\right)\right]$, $s{s}_t^{-}=\sum _{j=1}^t\mathrm{m}\mathrm{i}\mathrm{n}\left[\mathrm{\Delta }ln\left(s{s}_t,0\right)\right]$.

¹⁰ Since the 2008 Great Recession (2007:M10–2009:M6) and the COVID-19 pandemic (2020:M1-2020M12) could contribute to fluctuating the value of the South Korean won, we incorporate two dummies regarding these market shocks in Eq.(8). In addition, AIC recommends taking a lag order of six months (n = 6) for the analysis.

¹¹ This finding is a bit at odds with Zhang and Baek (2022) who discover the asymmetry of KRW in both the short and long run. This work, however, assumes the exogeneity of oil price shocks in their assessment.

Additional information

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.