What determines the long-term volatility of the offshore RMB exchange rate?

ABSTRACT

China has been promoting the internationalisation of the RMB for two decades. As a result, its offshore foreign exchange market has been substantially revitalised, despite that China’s capital market remains partially open. The movements of offshore exchange rates can provide crucial clues to understanding the offshore market structure and the effects of interventions by Chinese monetary authorities. Using the GARCH-MIDAS model, this paper examines how the degree of openness and economic fundamentals – both observed and unobserved – affect the long-term volatility of offshore exchange rates. We find that, first, trade openness attenuates the long-term volatility, while financial openness has no effect. Specifically, the use of bilateral currency swap agreements and the relaxation of capital control are associated with lower volatility, while stock market connect programs tend to increase the volatility. Second, observed fundamentals, including relative measures for growth, interest rate, and money supply, have significant negative effects on offshore volatility. Third, although economic policy uncertainty and market risk affect long-term volatility, liquidity does not appear to be a culprit of volatility. In terms of FX interventions, direct intervention decreases the volatility, while oral intervention increases volatility.

KEYWORDS:

• RMB offshore market
• exchange rate volatility
• capital account liberalisation
• the ‘8/11‘ exchange rate reform
• GARCH-MIDAS

JEL CLASSIFICATION:

• F31
• F38
• F41

Acknowledgments

The authors would like to thank the anonymous referee and the Editor-in-Chief for suggestions that have helped improve the paper. Insightful comments from Dae-Hwan Kim and Won-Joong Kim on an earlier version of this paper are also gratefully ackowledged. All remaining errors are the authors' own.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ The China Foreign Exchange Trade System (CFETS), also known as the China Foreign Exchange Trading Center, is affiliated with the People’s Bank of China (PBoC).

² In our variable selection process, we discard policies variables that have a small sample size and/or have too many missing observations (e.g., the proxy for the bond market connect program).

³ To limit large and sudden inflows of international capital, each QFII was subject to an investment quota. Since QFIIs rarely exhaust their quota, the aggregate size of the QFII quota does not indicate the actual capital flow, but reflects the monetary authorities’ attitude towards market opening-up. A larger aggregate size of QFII indicates that Chinese monetary authorities are optimistic about market opening-up and shows that they have the confidence to stabilise the market.

⁴ In finance. risk refers to the case that we know the potential outcomes and their probabilities; in contrast, uncertainty refers to the case that the outcomes and/or the probabilities are unknown. VIX reflects the expectation, psychology, and forward-looking behaviour of investors towards the market. EPU, on the other hand, reflects the judgement and decision-making power of the government and monetary authorities over the macroeconomy.

⁵ The economic policy uncertainty measure was developed by Baker et al. (2016) and can be downloaded from http://www.policyuncertainty.com/china_monthly.html.

⁶ In order to confirm whether Cpr_dist can act as a proxy for foreign exchange interventions, we calculated two FX intervention indicators, respectively, according to Levy-Yeyati et al. (2013) and Lu et al. (2022), and examined their correlation with Cpr_dist. The Spearman correlation coefficients between the two FX intervention indicators and Cpr_dist are both above 0.5 and are statistically significant. Therefore, we believe that Cpr_dist can better reflect the foreign exchange interventions of PBoC.

⁷ These notices were manually collected from the website of the State Administration of Foreign Exchange of China (SAFE), available at http://www.safe.gov.cn/safe/zcfg/index.html.

⁸ See, for example, additional analysis in Conrad and Loch (2015), Conrad et al. (2018), Amendola et al. (2019), Salisu et al. (2020), and Mo et al. (2018).

⁹ Conrad and Kleen (2020) expand the explanatory variables of the GARCH-MIDAS to two and found that adding VIX into the model greatly improves the power of fundamental variables.

¹⁰ Although we did not find any literature that touches on the direct impact of QFII on exchange rate volatility, there are empirical studies that indirectly support our view. (1) Al-Abri and Baghestani (2015) show that larger foreign liabilities reduce RMB exchange rate volatility. (2) After the introduction of QFII, Huo and Ahmed (2018) find that the volatility of China’s stock index futures decreased. Given that there is positive volatility spillover from the Chinese stock market to the foreign exchange market (Yang, 2017; Zhao, 2010), the exchange rate volatility will decrease in the post-QFII period. (3) Beatson and Chen (2018) and Liu and Wei (2019) find that the QFII scheme brings higher performance to listed A-share companies. Since corporate performance is conducive to economic growth, and greater economic growth will stabilise exchange rates, we infer that QFIIs help reduce exchange rates fluctuations in the long run.

¹¹ The correlation coefficient between interest rate differentials and exchange rate fluctuations is significantly −0.44.

¹² In August 2015, the market-oriented reform of the central parity rate (CPR) mechanism resulted in a substantial increase in exchange rate volatility. Despite this institutional change, we can still find some clues between interest rates and exchange rate fluctuations.

¹³ We use the GARCH-MIDAS model to examine the effects of $\mathrm{\Delta }$VIX of China and the US on exchange rate volatility separately. The $\theta$ coefficients show that the US VIX has a greater impact on exchange rate fluctuations than the Chinese VIX.

¹⁴ Why do $\theta$s of $\mathrm{\Delta }$VIX and EPU have different signs? To answer this question, we use two strategies: (1) applying the DCC-GARCH model to study the time-varying correlation between relative VIX (or relative EPU) and offshore exchange rate volatility; (2) using the GARCH-MIDAS model to examine how the $\mathrm{\Delta }$EPUs of China and the US affect exchange rate volatility, respectively. The results show that (1) the dynamic correlation between the US EPU and volatility is almost zero, while that between the Chinese EPU and volatility is much larger. In contrast, the correlation between the US VIX and volatility is much higher than that between the Chinese VIX and volatility. (2) The $\theta$ coefficient of $\mathrm{\Delta }$EPU_cn is larger than the $\theta$ coefficient of $\mathrm{\Delta }$EPU_us, meaning that China’s EPU has a greater impact on volatility than the US EPU. In short, based on the model involving EPU (VIX), volatility is affected more by domestic information than by foreign information.

¹⁵ See International Monetary Fund (IMF), “Policy Response to Covid-19”, July 2, 2021; available at https://www.imf.org/en/Topics/imf-and-covid19/Policy-Responses-to-COVID-19#U .

¹⁶ Due to space limit, the estimation results are not shown here but are available from the authors upon request.

Additional information

Funding

The work was supported by the Education Commission of Anhui Province of China [SK2019A0469] and Anhui University of Finance and Economics [ACKYC21033].