Measuring the International Influence of the RMB and its Regional Heterogeneity: Theory and Evidence from the Global FX Market Network

ABSTRACT

This study assesses the global and cross-regional influence of China’s currency, the renminbi (RMB). We first develop a theoretical framework to determine the channel through which progress in RMB internationalization can be mapped onto the market influence of the RMB exchange rate on that of other currencies. Using a volatility connectedness approach, we then construct a series of spillover indices reflecting the international influence of the RMB. We find that the RMB’s influence is worldwide but is smaller than that of major international currencies, such as the US dollar. Overall, the RMB has basically become a peripheral currency in Greater China, Central Asia, Eastern Europe, and some South Asian economies. Because of the Belt and Road initiative, regionalization of the RMB has also progressed in developing economies; however, China still has far to go in fully achieving internationalization of its currency.

KEYWORDS:

• RMB exchange rate
• global FX network
• volatility connectedness
• international influence

JEL:

• F31
• G15
• C32

Acknowledgments

We thank the two anonymous referees, the subject editor, and the editor-in-chief of this journal for their helpful comments and suggestions on earlier versions of this paper.

Disclosure Statement

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

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/1540496X.2024.2318313

Notes

1. Due to limitations on data availability, it is not possible to obtain exchange rate data for every economy in the world, so we select exchange rate data for as many economies as possible, given the available data. Precedents exist in the previous literature; for example, Mai et al. (2018) study the network structure of global exchange rates and also selected exchange rate data for only 52 major global currencies as their sample.

2. The optimal lag of lasso VAR is determined as $\mathit{p}$ = 1 according to AIC. The FEVD results tend to be stable when the forecast period $\mathit{H}$ ≥ 6; thus, we set $\mathit{H}$ = 6.

3. The analysis in Section 4.3 does not include the Greater China region.

4. To facilitate a comparison, the analysis in Section 4.4 does not include the five SDR currencies as spillover recipients. Therefore, RMB spillovers to USD, EUR, JPY, and GBP are considered in Sections 4.2–4.3 but not Section 4.4. As a result, the RMB spillovers in some regions as portrayed in Section 4.4 are slightly different from those in Sections 4.2–4.3; however, this does not affect the results of the comparison.

5. The empirical results of the prepandemic subsample modeling and a detailed description of the results are presented in Appendix G.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 72303027]; the National Social Science Foundation of China [grant number 23FJYB006]; the Fundamental Research Funds for the Central Universities [grant number 2242023S20013]; the National Key Research and Development Program of China [grant number 2021QY2100]; and the National Natural Science Foundation of China [grant number 72173018].