Tohoku University Research Center for Policy Design Discussion Paper

TUPD-2023-002

A Dynamic Analysis of Chinese Yuan (RMB)
Internationalization with Bayesian-Based Time Series Model

Zihe Li
Graduate School of Economics and Management, Tohoku University

Jun Nagayasu
Graduate School of Economics and Management, Tohoku University

February 2023

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A Dynamic Analysis of Chinese Yuan (RMB)
Internationalization with Bayesian-Based Time Series Model∗
Zihe Li†Jun Nagayasu‡

Graduate School of Economics and Management
Tohoku University
February 11, 2023
Abstract
The paper dynamically analyzes the recent RMB internationalization development.
Unlike previous studies, we study the level of currency internationalization using explanatory factor analysis and rolling correlation calculation and report evidence that
the Chinese RMB is still not a completely internationalized currency, but overall isinternationalizing with a positive trend. Moreover, based on a Bayesian-based causal
impact analysis, we study the effect of the COVID-19 pandemic on the process of RMB
internationalization.

Keywords: RMB internationalization; Factor analysis; COVID-19 pandemic; Bayesian
Causal impact
JEL Classification: C11, C32, C58, F31∗

The paper was presented at the Annual Conference of the Japan Society of Monetary Economics
in November 2022. We wish to acknowledge the helpful comments from Ayumu Sugawara and Taiyo
Yoshimi. Financial support was provided by a grant from the Pioneering Research Support Project.
†
Email: li.zihe.s8@dc.tohoku.ac.jp. Address: Tohoku University, Graduate School of Economics
and Management, 27-1 Kawauchi, Aoba Ward, Sendai, Miyagi 980-0862 JAPAN‡
Email: jun.nagayasu.d8@tohoku.ac.jp. Address: Tohoku University, Graduate School of Economics and Management, 27-1 Kawauchi, Aoba Ward, Sendai, Miyagi 980-0862 JAPAN1

Introduction

During the Second World War, the US Dollar (USD) became the world’s most powerfulcurrency and is still of central status. However, after the collapse of the Bretton Woods
System1 in 1973 and the global recession triggered by the US subprime loan crisis fromthe end of 2007 to 2009, people started to realize that there are problems in the USDcentered system and this powerful currency might not be as stable as they thought(Cooper et al., 2009; Goldberg, 2010; Kim, 2011; Wyplosz, 2010). Meantime, China
overtook Japan and became the world’s second-largest economy. Chinese economy
developed so rapidly that scholars turned their focal point to China’s currency, the Yuan
(RMB). The Chinese government has also mapped out strategies, projects, and policies
to promote RMB internationalization. For example, the Belt and Road Initiative
Project, also called “One Belt One Road (OBOR)”, was proposed by President Xi
Jinping in 2013, aiming at promoting international trade and investments in nearly 70
countries often located near China. As a result, International Monetary Fund (IMF)
included RMB into the Special Drawing Rights (SDR) basket2 in 2016, admitting its
global impact on the world’s economy. According to the reported currency SDR value
from Bank for International Settlement (BIS), RMB is weighted 10.92% in the currency
basket, ranking in third place in 2015. Considering these backgrounds, discussing thestrategy and policy choices for promoting the RMB to become an internationalized
currency is meaningful.
We examine the recent situation of currency internationalization and the impactof a recent pandemic (COVID-19) on RMB internationalization, with the Exploratory
Factor Analysis (EFA) and the Causal Impact (CI) Analysis. A fall in global tradecaused by COVID-19 is expected to reduce the use of foreign exchanges such as the
RMB. But since the determination of settlement currencies requires agreement between
traders through a legal process, RMB internationalization may have been affected
little. Therefore, we clarify if there are any changes in RMB internationalization using
advanced time-series techniques.
The paper is written based on several theories and previous empirical works. According to the standard economic theory, a currency is defined as money with functions
including the unit of account, the medium of exchange, and the store of value. Nor1

The Bretton Woods System is a monetary management system established after the signing of
the Bretton Woods Agreement in 1944 among the United States (US), Canada, Western European
countries, Australia, and Japan.
2
The Special Drawing Rights (SDR) was created by the International Monetary Fund (IMF) in
1969. SDRs are units of account for the IMF and represent a claim to currency held by IMF member
countries for which they may be exchanged. The SDR weights and values are calculated every five
years.

2

rlof (2009) expands this definition specifically for the world’s key currencies with theprivate and public sectors. We use a factor model in the empirical analysis to obtaina proxy for key currencies from major exchange rates assumed to possess these threefunctions for key currencies and infer the underlying factors behind currency internationalization. The next section reviews previous studies to clarify our contributions toexisting literature.

2

Literature Review

While the discussion on currency internationalization started very early, RMB internationalization became a popular topic after 2010. Most researches were qualitativeand discussed the possibility of RMB internationalization or whether RMB was an internationalized currency at their time. These studies often concluded that RMB is not
yet an internationalized currency, but it can become one (Dobson and Masson, 2009;
Huang and Lynch, 2013; Nakagawa, 2004; Tung et al., 2012).
After 2013, different topics and more quantitative research appeared, while some researchers continue to study whether the RMB is already an internationalized currency
since little evidence has been provided to support the complete internationalization of
the RMB(Campanella, 2014). One popular topic is the analysis of influential factorsof RMB internationalization. Factors such as economic size (Chen, 2018; Peng andTan, 2017), economic degree of freedom and financial openness (Jin and Du, 2014;
Lyratzakis, 2014; Peng and Tan, 2017; Wu and Tang, 2018; Zhang, 2013), the volume
of international trade and investments (Wu and Tang, 2018; Zhang, 2017), and thestability of exchange rate and its system (Fan et al., 2015; Wu and Tang, 2018) aremost highly mentioned. Other scholars analyze the relationship between RMB internationalization and specific events such as constructing RMB offshore market in Hong
Kong (Maziad and Kang, 2012), proposing the Belt and Road Initiative (Wu and Tang,
2018; Yu and Cao, 2015; Zhang et al., 2017), being included in Special Drawing Right(Wei, 2020), and promoting RMB international invoicing (Lai and Yu, 2015). These
factors are selected based on the criteria for key currencies.
Norrlof (2009) summarizes the key currency definition and functions that can beused to assess currency internationalization. As the function of the unit of account,a currency should be able to indicate prices of goods and services (e.g., export andimport). In this respect, market size and currency stabilization can become factors
in determining payment currencies. The function of medium of exchange serves as apayment means for individuals and officials when trading. This may be reflected in the3share of a currency in the world’s trading activity. As the function of store of value,
the definition is highly related to exchange rate stability. The currency’s value should
not decline excessively over time if it has a high store of value.
Among quantitative research, constructing index models is most commonly used tomeasure the RMB internationalization degree. Currently, the most authoritative index
specifically for the Chinese Yuan is called “RMB Internationalization Index (RII)” developed by a group of scholars from the International Monetary Institution, Renmin
University of China (Tu et al., 2013). Other scholars construct indexes or economic
models to measure the internationalization degree from different aspects. For example, Dobson and Masson (2009) measure the RMB internationalization degree with anindex called Reserve Currency Ratio with IMF data simply from the reserve aspect.
Tung et al. (2012) construct a currency internationalization degree index with Principal Component Analysis (PCA) to measure current RMB internationalization status.
Zhang (2017) creates a macroeconomic model with three sectors, including household,enterprise, and government to measure the RMB internationalization degree. Chenget al. (2018) examine the internationalization degree under trading patterns with amodel including trading volume, trading value, market concentration, and trading network.
Few researchers study RMB internationalization, specifically the relationship between the COVID-19 pandemic and RMB internationalization. Most of them focus on
discussing the influence of the COVID-19 pandemic on other financial aspects. Wei
et al. (2020) conclude that the COVID-19 outbreak in China caused the foreign exchange markets to become unstable and risky, which might have already changed themonetary system. Cristina and Ramona (2020) conclude that the COVID-19 pandemic
will promote a more profound reform of the world and Chinese financial systems. But
they have yet to make specific conclusions like this research.
Therefore, despite many academic works on RMB internationalization, many questions still need to be addressed. For example, scholars have yet to agree on whether
the RMB has already been internationalized. Additionally, there are many ways todefine RMB internationalization and its realized conditions. Tavlas (1990) and Kenen(2011) consider that the three functions of key currencies (medium of exchange, unitof account, and store of value) should be realized simultaneously so that the currency
can be regarded as an internationalized currency. Zhou and Wen (2001) deem that thefunctions should be realized step by step following different stages, including forming
exchange relations with other countries, opening currency and capital projects, and becoming other countries’ storage currency. Also, Liu (2003) and Dong and Yao (1997)4

conclude that the process of RMB outside the home country should be considered RMB
internationalization.
Against this background, we contribute to the following aspects of the currency or
RMB internationalization area. First, recent situations of RMB internationalization
are updated because the article covers recent data. Second, we obtain a proxy fora key currency from selected exchange rates based on the EFA and propose using
rolling correlation to measure the level of currency internationalization. Finally, we
conduct the CI analysis to investigate the influence of the COVID-19 pandemic on
RMB internationalization using the factors from the EFA.

3

Exploratory Factor Analysis

There are several ways to find a benchmark for the key currency. Here, we use a factor
model that allows us to extract common features among economic variables because
currency internationalization is abstract progress. Other measurements such as theforeign reserves data from IMF Currency Composition of Official Foreign Exchange Reserve (IMF-COFER)3 and currency international payment share data from the Society
for Worldwide Interbank Financial Telecommunication (SWIFT)4 may be more direct
approaches to examine currency internationalization. COFER data order currencies
with SDR basket weights as a reference to show the ranking of total foreign exchange
reserves of currencies. It can, in some aspects, reflect countries’ willingness to have thecurrency due to its power and stability. However, the data are in quarter frequency,
and the time range is quite short (from 2016 to the present). The amount of observations is not enough for accurate empirical research. SWIFT data provide monthly
information on RMB’s value share as a global payment currency in the world’s trade
markets from 2012 to the present. Although the SWIFT dataset’s time range is longer
than that of COFER, part of RMB data are categorized as “Others” due to its low
share, causing missing data. For these reasons, we estimate a proxy for key currencies
using the factor model and major exchange rates.
Our approach is similar to Tung et al. (2012), who applied PCA to key determinantsof currency internationalization, such as the market capitalization ratio, the GDP ratio,
price stability, and exchange rate stability. However, since these data are available at
low frequency but we need high-frequency data to assess the impact of the pandemic
on RMB (2020–), common features are extracted from influential exchange rates and3

https://data.imf.org/?sk=E6A5F467-C14B-4AA8-9F6D-5A09EC4E62A4
https://www.swift.com/our-solutions/compliance-and-shared-services/businessintelligence/renminbi/rmb-tracker
45are used to measure the internationalization level by comparing their common featureswith RMB. A high correlation between the common factors and RMB implies a highinternationalization level. Therefore, our approach does not directly utilize the conceptof key currencies but employs currencies and exchange rates that are believed to meetthe condition of key currencies. Clearly, if our proxy data positively correlate with
SWIFT and COFER, then it is effective and reliable to evaluate the internationalization
degree of currencies.
In the following part, we will explain how exploratory factor analysis works withmathematical details and show how the extracted factors serve a role in influencing
currency internationalization. The study’s variable grouping process will also convey
meaningful information about potential influencing factors.

3.1

Mathematical Principles and Conditions

Exploratory Factor Analysis (EFA) is a data dimension reduction technique used to
discover the latent structure and complex pattern of a set of variables (Bai et al.,2015; Yong and Pearce, 2013). In this paper, interpreting factor analysis results is a
critical mission. The group clustering results, the factor scores, and the linear functions
representing the relationship between underlying factors and variables varying by time
can help complete this task. Researchers such as Öcal et al. (2007) and Bai et al. (2015)
acquire valuable information on the data through the grouping process. According to
the previous works (Yong and Pearce, 2013), two essential conditions must be fulfilled
before conducting the EFA. First, the criterion of sample size should be sufficiently
large. Second, the correlations among variables in the data set should not be larger
than 0.9 because it indicates there might have a multicollinearity problem in the data.
The classic mathematical expression of EFA is:
Xj = aj1 F1 + aj2 F2 + . . . . . . ajm Fm + ej

(1)

where j = 1, 2, ..., p, p represents the number of variable, and m represents thenumber of underlying common factors F. The letter a denotes factor loadings, where
aj1 means the factor loading of j th variable on the first factor. The term e is the error
term and represents idiosyncratic factors.
Before conducting the EFA, several conditions should be fulfilled. The sample size
should be larger than 300, and the correlations among variables should not be larger
than 0.9 because this means a multicollinearity problem might exist in the data (Yongand Pearce, 2013). Additionally, the data for conducting EFA should pass the KaiserMeyer-Olkin (KMO) Test and the Bartlett’s Test of Sphericity, with a KMO value
6larger than 0.5 and a p-value smaller than 0.01 (Bartlett, 1954; Kaiser, 1958). The
number of factors is determined by drawing a scree plot, and those factors extractedwith an eigenvalue larger than one can be used as common factors (Kaiser, 1960).
Communality is another concept that can be used to judge whether a variable should
be kept after conducting the factor analysis. Equation (2) denotes the mathematicalexpression of calculating commonality, and any variable with a commonality before
rotation smaller than 0.4 should be deleted.
h2j = a2j1 + a2j2 . . . · · · + a2jm

(2)

As a data dimension reduction technique, the primary purpose of using factor analysis in this research is to find common features behind internationalized and key currencies and then extract these features as influential factors of currency internationalization. One of the main tasks of the research is to find out the gaps between the
RMB and other internationalized currencies; thus, it is necessary to acquire influential
factors, which are the factors extracted among internationalized and key currencies,that influence currency internationalization and guide the development of RMB internationalization. Besides, exchange rate data can provide information about a country’s
economic conditions, international competitiveness, and even domestic consumer market situations. Extracted common factors can generalize the information wanted, but
the disadvantage is that interpreting the factors is always challenging in the factor
analysis, even when most of the information has been extracted. Therefore, we shallconduct factor analysis with several groups of exchange rates believed to be relatively
important in the global market.

3.2

Data Description, Eligibility & Results

Eleven influential currencies are selected for EFA, divided into internationalized andinternationalizing currencies. According to the turnover report of currency foreignexchange instruments from the BIS5 , internationalized currencies include US Dollar(USD), Euro (EURO), Japanese Yen (JPY), Pound Sterling (GPB), Canadian Dollar(CAD) and Australian Dollar (AUD). Another internationalized currency, the Swiss
Franc (CHF), is performed as a benchmark of the nominal exchange rate data. Other
five internationalizing currencies come from BRICS countries, including the Chinese
Yuan (RMB), Russian Ruble (RUB), Brazilian Real (BRL), Indian Rupee (INR), andSouth Africa Rand (ZAR). The frequency of the data is monthly, and the time range
is from January 1999 to December 2021, acquired from FxTop historical exchange rate5

https://stats.bis.org/statx/srs/table/d11.3

7

database.6 All exchange rates are in natural logarithmic form and first difference. Datadescription details and graphs are shown in Table 1 and Figures 1 and 2. In total, there
are 276 observations under each currency.
Moreover, Table 2 presents a correlation matrix. The correlations of INR, BRL, andZAR with other currencies are mostly low. Thus, they can be deleted for conducting
EFA without losing vital information required for the accuracy of the factor extraction.
After deleting these currencies, the new data set should pass the KMO and Bartlett’sTest. The rotation technique is called “Varimax ”, meaning the number of variables
with high loadings on each factor has been minimized, and the number of variables
with smaller loadings is smaller (Yong and Pearce, 2013). Before rotation, all variables
were clustered under one factor. After rotation, variables should be clustered under
several factors, which will help to interpret the results easily.
Then, factor analysis is conducted three times with different variables and purposes.
The first-factor analysis is performed with eight currencies. The second-factor analysis
is conducted just among the six internationalized currencies, including USD, EURO,JPY, GPB, CAD, and AUD. This is to examine how these key currencies are grouped.
These results will detect some potential influencing factors of currency internationalization. The third-factor analysis is conducted among the three strongest currencies(USD, EURO, and GPB), extracting only one factor that will include the most information on internationalized currencies. The standard of picking the strongest currencies
is based on the turnover report of currency foreign exchange instruments from the BIS
mentioned previously. Although it shows that JPY surpassed GPB around 2016 to be
the third strongest currency, overall, GPB has a longer period to be the third place.
Thus, the three strongest currencies in the paper are USD, EURO, and GPB. Afteracquiring this single factor, an estimated exchange rate time series data can be formed
for the next step. Table 3 indicates that all data with different variable combinations
have passed the KMO and Bartlett’s tests.
Table 4 and the left part of Figure 6 show factor analysis results with eight currencies. The USD, JPY, and RMB are clustered under Factor 2, and the rest of thecurrencies are under Factor 1. In total, the two factors explain 60% of the whole data
set. After deleting the data of RMB and RUB, Table 5 and the right part of Figure
6 indicate the results of 6 currencies, where CAD and AUD are under Factor 1, USD
and JPY are under Factor 2, and EURO and GPB are under Factor 3. In total, thethree factors explain 70% of the whole data.
The correlation matrix in Table 2 shows that the correlation values among USD,
6

https://fxtop.com

8

RMB, and JPY are high, relatively, 0.6034, 0.7914, and 0.5006. This is probably thereason for their clustering with each other in the grouping process. The correlation
value between GPB and USD is 0.6110, and almost 0.5 between GPB and RMB.
However, GPB does not cluster with USD and RMB but with EURO and RUB, eventhough the correlation between RUB and GPB is slightly lower. According to theWorld Bank’s GDP ranking, the economic size of the United Kingdom is closer tothat of Russia, and the United States, China, and Japan are the three top economies
in the world. Thus, economic size can be another reason for this grouping results.
Also, in Table 2, it is evident that RMB has relatively low correlations with countries
that are far away from it, such as EURO (0.3474), CAD (0.4625), AUD (0.2437),
and RUB (0.3620). Not just RMB, JPY has a similar situation (EURO 0.3565, CAD
0.3640, AUD 0.2536, RUB 0.2138). These results might be evidence that, except foreconomic size, geographical location could be another important, influential factor
for the determination of particular currencies used in the payment system, which isconsistent with previous studies (He et al., 2016; Korhonen et al., 2015; Xueyan, 2017)and provides indirect evidence that the Belt and Road Initiative project has become adriving force of RMB internationalization.
As Factor 1 extracted most information from the data, significant tests for Factor
1 from factor analysis with six currencies and three world’s strongest currencies are
conducted to see whether the results are significant to all included currencies and the
RMB. For the factor analysis with three currencies, since only one factor was extracted,
there is no need to rotate. The factor loadings under the sole factor are 0.64, 0.57, and0.96 relative to USD, Euro, and GPB. According to Table 6, no matter which Factor
1 is, the results are all significant, meaning the factor analysis results are meaningful
and reliable. Thus, according to the linear function of factor analysis, estimated timeseries exchange rate data can be acquired, representing the exchange rates influenced
by the factor extracted. The rolling correlations with a moving window size of 3 are
calculated between the original exchange rate data and the estimated data.
Since RMB internationalization progress is a dynamic process changing over time,
the rolling correlation data of RMB should expect to be significant to the time change.
Here, natural numbers starting from 0 represent the time change. Results can beseen in Table 7. In the left part, the rolling correlations are calculated with Factor
1 from all six internationalizing currencies, where only USD and RMB results are
significant. Results in the right part show that Factor 1 extracted from 3 currencies’
factor analysis is most significant to RMB but less significant to USD and EURO andnot significant to GPB. Since Factor 1 from 6 currencies includes more information on

9internationalization and the results of time trend are significant, the rolling correlation
calculated with this Factor 1 of RMB can represent the internationalization process
and will be used as the data set for the causal impact analysis. Figure 4 and Figure 5
plot the rolling correlation of all internationalized currencies and RMB. Comparing thefigures indicates that for internationalized currencies, dots are mostly clustered near
the top of the graph with values closer to 1. That makes the trend line flatter. The
dots in the RMB graph spread over the panel, and the trend line slope is much steeper.
Thus, RMB has not been completely internationalized yet. Still, since the trend line
slope is positive and the dots start to get close to value 1, this ongoing process is
positive, and finally, the RMB will be an internationalized currency.
The reason for selecting the exchange rate as a concrete measurement of currencyinternationalization is mainly based on related economic theories and previous research(Ding et al., 2020; Wang et al., 2022; Xun et al., 2019). As a key currency, the currency must serve as a medium of exchange, a unit of account, and a store of value.
The exchange rate is an important standard for conveying these functions. According
to Norrlof (2009) and Hartmann and Issing (2002), a key currency, or an internationalized currency, should have the ability to maintain price stability, and the price ofthe currency refers to the exchange rate. Thus, examining currency exchange rates isreasonable for measuring whether a currency is internationalized.
Finally, to demonstrate their similarity with our proxy for currency internationalization, we show their correlation in Table 8. We use three-month averages as quarterly
data for SWIFT and rolling correlation data and find that our proxy for currency internationalization positively correlates with COFER and SWIFT (0.4537 and 0.2282). A
positive correlation means that our proxy of currency internationalization shares similar information with COFER and SWIFT in some aspects, and since the correlation
with COFER is higher than that with SWIFT, our proxy for currency internationalization is more in line with COFER that records the currency composition of holdingsof foreign exchange reserves.

4

COVID-19 Impact on RMB Internationalization

On January 31st, 2020, the World Health Organization (WHO) Emergency Committee announced the COVID-19 pandemic had become a worldwide health emergency
based on the growing cases in different places (Velavan and Meyer, 2020). From thenews on TV and the Internet, it is claimed that this worldwide health emergency isthe cause of the economic downturn, but it has no academic evidence. Thus, it is10necessary to explore the impact of the COVID-19 pandemic. The world’s economy andfinancial market have been hugely influenced since the COVID-19 pandemic. During
the COVID-19 pandemic in China, companies and factories stopped working; people
were locked down at home in many cities. The crash risk of the stock and bond market
in China was more strongly affected by the pandemic (Liu et al., 2021); the expected
results of the COVID-19 pandemic causal analysis should be negative.
Previously, pandemics such as Severe Acute Respiratory Syndrome (SARS) and
Swine Flu (H1N1) have been proven by researchers that such worldwide pandemics
have a negative impact in the short term. For example, Fan (2003) concludes that theappearance of the short-term negative impact of SARS on the economy is reducing
demand. She also considers that the so-called long-term effect of SARS is on thestrategy of preventing the pandemic. Hai et al. (2004) stress that SARS negatively
influenced the Chinese economy in the short term and hit the tourism sector the most.
Beutels et al. (2009) has similar conclusions, but the research object is Beijing City.
Research related to H1N1 is mainly conducted with single-country samples, but thefindings also have a short-term negative impact on the economy and financial sectors.
Tracht et al. (2012) confirm that the H1N1 pandemic has caused short-term economic
loss due to the wide use of face masks. Rassy and Smith (2013) conclude that thenegative impact of H1N1 on Mexico’s pork and tourism sector lasts around five months.
Another reason for the hypothesis is that existing researches and facts prove that
the COVID-19 pandemic can negatively influence RMB internationalization throughdifferent channels shortly. According to researchers such as Wei et al. (2020) andGunay (2021), the COVID-19 pandemic has induced disturbances and shocks in financial markets. Besides, according to Batten and Szilagyi (2016) and Ming and Zhang(2017), international trade or cross-border trade is an important channel to promote
RMB internationalization. Researchers such as Gruszczynski (2020) and Hayakawa andMukunoki (2021) have proved with empirical methods that COVID-19 has a short-term
negative impact on international trade in most industries. During the pandemic, theChinese government has closed many factories and shops, canceled multiple transportation lines, and stopped international trade to prevent the virus from infecting
more people. Thus, it is not surprising to see that the pandemic has influenced RMB
internationalization in some aspects, and the negative impact lasted for a while due tothe timeline of restrictions from the government.

4.1

Causal Impact Methodology

Brodersen et al. (2015) developed the causal impact technique to analyze the impact of11

an intervention with time series data. They defined the causal impact as the difference
between the reality (observed data) and the counterfactual. The counterfactual data
can be acquired through direct estimation with the observed data, data with similar
behavior, and models with prior knowledge. The data before and after the event time
point is relatively called pre-period data and post-period data.
The most commonly used approach to infer a causal relationship before and after
an intervention is the Difference-in-Differences (DID) method with a cross-sectional
data set. However, the standard DID is often static and only considers two-time points(before and after the intervention). Thus, the Bayesian Structural Time Series (BSTS)
model has been developed to address these limitations. In causal impact analysis,
the relationship before the event is modeled with BSTS models. In short, the BSTS
models illustrate that the observed data, in reality, is determined by the latent statethat evolves with a Markov Chain Monte Carlo (MCMC) process.
A basic structural model contains a local linear trend component, a seasonality
component, and a regression component, and the function could be written as:
yt = µt + τt + β T xt + εt

(3)

In the above equation, yt represents the observed data, and εt represents the error
term of the linear equation. µt , and τt represent the local linear trend and seasonality.
Term β T xt is the regression component, including regression coefficients and a setof trend verticals. Since each component has its way of calculating, writing themseparately can be tedious and complex. Thus, α, the unobserved latent state, is used
to represent these components in a state space model to include them and can be
written as:
yt = ZtT αt + εt εt ∼ N 0, σt2
αt+1 = Tt αt + Rt ηt(ηt ∼ N (0, Qt ))

(4)
(5)

In the above equation pair, Equation (4) is called the observation equation, whichlinks the observed data yt with the unobserved latent state α, and Equation (5) iscalled the transition equation, which defines the latent state with the change of time.
The capital letters, Zt , Tt , and Rt are matrices that contain known values and unknown
parameters. Term Rt ηt is another format of the error term in transition equation (Scottand Varian, 2014).
The parameters of the linear function of Bayesian linear regression, unlike those ofregression based on the regular Ordinary Least Squares (OLS), come from distribution
sampled from thousands of times of simulations. The spike and slab selection method12is helpful to make use of the prior knowledge of the regression model, where “spike”
refers to the probability of a particular coefficient of the model to be zero, and “slab”
refers to the prior distribution of regression coefficient values. A spike and slab priorcan be expressed as the following:p β, γ, σ −2 = p(γ)p σ 2 | γ p βγ | γ, σ 2where:γ∼KY

πkγk (1 − πk )1−γk

(6)

(7)

k=1

and:
2



2

−1
Σ−1
γ



(8)
βγ | σ ∼ N bγ , σν sσ −2 ∼ G,(9)
2 2
In the above expressions, Equation (7) is a Bernoulli distribution representing the
spike part, and Equation (8) and (9) is a conditionally conjugate slab prior. The small
π in Equation (7) is the prior probability of the regressor in the model, and its value
can be determined by examining the expected model size M , where π equals M divided
by K. Vector b in Equation (8) is usually set as 0, and Σ−1 is the prior precision over
β in the model. Σ is calculated as:
Σ−1 =


g
wX T X + (1 − w) diag X T X
n(10)

where g is the number of observations that contain the worth of information, andw is the probability. X is the covariates. Prior parameters in Equation (9) follow
a Gamma Distribution and can be elicited by examining the expected R2 value andthe number of observations worth of weight v. Thus, prior sum of squares s can becalculated as:s = v 1 − R2 s2y(11)

In this research, θ represents a set of the model parameters, and the prior distribution can be expressed as p(θ), indicating all possible model parameters based on theinitial guess on the situation of the data set. The prior distribution also describes thebeliefs about the researcher’s expectations of the model and likelihood relationship.
The model will be estimated by sampling the posterior distribution of the parameterswith the Markov Chain Monte Carlo (MCMC) algorithm. The posterior distribution
is the proportion of the product of prior and likelihood, written as:p(θ | D) ∝ p(D | θ) · p(θ)
13

(12)

where D is the data set used for analysis. The causal impact analysis in this research
will use defaulted values in programming software R, where R2 is set as 0.5, π equals
0.5 for each regressor, v equals 0.01 and w when calculating Σ−1 is 0.5. The inference
will be performed following these steps: First, a draw of θ and latent state α will besimulated given the observed data y within the historical data before the event time
point. With the MCMC technique, this goal can be easily reached. The algorithm of
MCMC is as follows. Let E be the set of latent state α and other model parameters
including θ, β and σ 2 . Relatively, simulate latent state α and model parameter θ given
data y within the training period. Then, simulate other parameters from the Markov
Chain with a stationary distribution p(E | y). After repeating simulations thousandsof times (3000 times in this research), a sequence of drawing E1 , E2 ,... forms. Then,the posterior simulations will be used to simulate the posterior predictive distribution
over the counterfactual data. Finally, these posterior predictive samples will be used
for calculating the differences to get the cumulative impact of the distribution.

4.2

COVID-19 Pandemic and RMB Internationalization

Due to the particularity of the COVID-19 pandemic data, the only reliable way to findits counterfactual data is to estimate based on the data itself. To examine whether
the COVID-19 pandemic effect is a robust short-term negative one, two methods,
including a simple naive forecasting method and a moving average forecasting method,
are conducted to simulate different counterfactual data. Besides, a comparison of
results between a longer counterfactual period (from February 2020 to December 2021)
and a shorter counterfactual period (from February 2020 to December 2020) is madeto confirm whether the impact is a long-term one or a short-term one.
The mathematical expression for the simple naive method is as follows: the asteriskmeans the estimated value, and t is time.
yt∗ = yt−1

(13)

The mathematical expression of the moving average method is:yn =yn−12 + yn−11 + · · · + yn−1
12

(14)

The moving size selected is 12 because of the monthly data. The forecast value
can be acquired from the average of the previous 12 values. With these two methods,
simulated counterfactual data are plotted in Figure 7 and Figure 8.
Based on the causal impact methodology described in the previous part, each part14

of Equation 6 can be clearly explained. The prior distribution is the guess of the trainedmodel parameters and comes from the simulations with the actual historical data before
the event. The likelihood is the response features given the trained model, which isexpressed as the probability of the data analyzed (both reality and counterfactual)
given the trained model. Thus, the product of these two is the posterior of the model
parameters used for the causal impact analysis. The causal impact results can be
numerical (Tables 9 and 10) and graphical (Figures 9 and 10). According to theresults in Table 9, under the naive method, the absolute average effect is -82%, andthe absolute cumulative effect is -165%. The relative effect for both methods is -179%.
The moving average method shows that the absolute effect is -47% and the cumulative
effect is -435%. However, the relative effect for both average and cumulative is -47%.
Both methods’ p-value is smaller than 0.01, meaning the magnitude of the negative
impacts observed is statistically significant. According to the results in Table 10, under
the naive method, the absolute average effect is -4%, and the absolute cumulative
effect is -44%. The relative effect for both methods is -253%. The moving averagemethod shows that the absolute effect is -15% and the cumulative impact is -41.6%.
However, the relative effect for both average and cumulative is -14%. Both methods’
p-value is smaller than 0.01, meaning the magnitude of the negative impacts observed
is statistically significant.
In Figure 9, the shapes of a point-wise part are similar. Although line shapes areslightly different in the cumulative panel, the total trend is still negative. Also, theyboth show a relatively short trend of increase of the negative effect but then a long trendof decrease, meaning the COVID-19 pandemic’s negative impact only lasts shortly. As
in Figure 9, line shapes in point-wise panels of Figure 10 are similar. In the cumulative
panel, the total trend is negative, and there is a short-term increase in the negative
impact, but then the impact decreases. Therefore, the COVID-19 pandemic impact isnegative but only lasts in the short term. All these results mean that the COVID-19
pandemic presents a much stronger negative effect in a shorter period, and it keeps
decreasing over time. Though the relative effect of the naive forecasting method of2020 is smaller than that of 2021, the result is still negative. All four parts have ap-value smaller than 0.01, meaning the negative effect is statistically significant, andthe results are robust.
Literature previously mentioned the relationship between exchange rate and the
COVID-19 pandemic in Section 4 concludes that COVID-19 influences exchange rate
spillover differently. Wei et al. (2020) claim that the pandemic triggers the changes of
exchange rate spillover, while Gunay (2021) considers the influence on spillover varies

15

from country to country. However, they only confirm that the COVID-19 pandemic
can affect exchange rates from some channels but do not conclude any causal or anothertype of relationship. But similarly, when comparing with Wei et al. (2020)’s results,
when the pandemic hits, there is an apparent sudden change in the graph, meaning
the COVID-19 pandemic significantly affects the exchange rate market. However, theconclusion from Wei et al. (2020)’s paper is only to confirm the influence, not define
its nature. But in this paper, a clear causal relationship conclusion is drawn basedon empirical results from the causal impact analysis, and it should be considered an
improvement in this area.

5

Conclusions

We have examined the dynamic process of currency internationalization and the relationship between the COVID-19 pandemic and RMB internationalization. Using rollingcorrelation, our proxy for currency internationalization shows that RMB is approachingcomplete internationalization; the trend is positive as an internationalizing currency.
Moreover, the results of the causal analysis indicate that the COVID-19 pandemic cansignificantly and negatively affect the process of RMB internationalization, confirming a close relationship between international trade and currency internationalization.
However, this effect lasted just a short time. A short-term impact of COVID-19 implies that the pandemic has not resulted in institutional changes, such as alternationin settlement currencies. As a result, traders still view RMB as an important payment
currency in the global market.

16

Mean
USD 1.1443
EURO 1.3469
GPB 1.8027
JPY
1.0708
CAD 0.9175
AUD 0.8534
RMB 0.1576
RUB 0.0339
INR
0.0225
BRL 0.4755
ZAR 0.1331

Original
Median Std.
1.02180.25391.36470.2096
1.58790.47981.06090.19840.93740.14230.87990.11390.15190.01940.03280.01570.02190.00810.46530.19720.13140.0588UR p-value
0.7876
0.63540.62790.27090.17380.37110.48550.51160.61030.43220.4027

1st Difference
Median Std. UR p-value-0.0003
0.0125 0.0100-0.0002
0.0077 0.0100-0.0006
0.0119 0.0100-0.00120.0134 0.0100
0.00070.0141 0.0100-0.0001
0.0270 0.0100
0.00030.0124 0.0100
0.00000.0176 0.0100
0.00000.0104 0.0100-0.00110.0195 0.0100-0.0006
0.0160 0.0100

Mean-0.0007-0.0007-0.0010-0.0007-0.0004-0.0008-0.0002-0.0025-0.0015-0.0028-0.0022

Note: USD - US Dollar; GPB - Pound Sterling; JPY - Japanese Yen; CAD - Canadian Dollar; AUD- Australian Dollar; RMB - Chinese Yuan; RUB - Russian Ruble; INR - Indian Rupee; BRL Brazilian Real; ZAR - South African Rand.

Table 1: Data Description

USD
EURO
GPB
JPY
CAD
AUD
RMB
RUB
INR
BRL
ZAR

USD
1.00000.36080.61100.60340.61450.3022
0.79140.4396
0.42680.18950.1287

EURO

GPB

JPY

CAD

AUD

RMB

RUB

INR

BRL

1.00000.54490.35650.5292
0.5222
0.34740.41290.23610.18630.14081.00000.3786 1.0000
0.5982 0.3640 1.0000
0.4383 0.2536 0.6811 1.0000
0.4988 0.5006 0.4625 0.2437 1.0000
0.4778 0.2183 0.5560 0.4784 0.3620 1.0000
0.2348 0.1857 0.3264 0.2760 0.5108 0.2913 1.0000
0.2296 0.0542 0.2965 0.4029 0.2451 0.2878 0.4858 1.0000
0.1729 0.0655 0.3426 0.3426 0.2074 0.2709 0.4852 0.4064 1.0000

Table 2: Correlation Matrix

1st Factor Analysis
KMO Value
Overall
0.83
USD
0.77
EURO
0.86
GPB
0.89
JPY
0.83
CAD
0.84
AUD
0.78
RMB
0.80
RUB
0.94

2nd Factor Analysis
KMO Value
Overall
0.76
USD
0.69
EURO
0.85
GPB
0.81
JPY
0.69
CAD
0.77
AUD
0.72

3rd Factor Analysis
KMO Value
Overall
0.63
USD
0.64
EURO
0.69
GPB
0.59

Bartlett’s Test
Chi-square 1250.029
p-value
3.55E-239
DF
36

Bartlett’s Test
Chi-square 747.851
p-value
1.16E-149
DF
15

Bartlett’s Test
Chi-square 223.542
p-value
3.44E-48
DF
3

Table 3: KMO and Bartlett’s Test Results

17

ZAR

Before
FA 1
0.60
0.73
0.83
0.62
0.61
0.87
0.49
0.71

EURO
GPB
CAD
AUD
RUB
USD
JPY
RMB

Eigenvalue
4.80
Explained %48%
Cumulative % 48%

FA 2
0.30
0.08
0.24
0.50
0.20
-0.48
-0.36
-0.38

Communality
0.46
0.54
0.74
0.64
0.41
0.99
0.47
0.65

1.20
12%
60%

After
FA 1
0.65
0.59
0.77
0.80
0.58
0.32
0.12
0.26

FA 2
0.19
0.43
0.38
0.05
0.27
0.94
0.60
0.76

3.20
32%
32%

2.80
28%
60%

Communality
0.46
0.54
0.74
0.64
0.41
0.99
0.47
0.65

Table 4: Factor Analysis Results of 8 Currencies

CAD
AUD
USD
JPY
EURO
GPB

FA 1
0.84
0.69
0.80
0.46
0.62
0.79

Before Rotation
FA 2 FA 3 Communality FA 1
-0.13 0.14 0.74
0.67
-0.52 0.25 0.81
0.87
0.52 0.06 0.92
0.18
0.42 0.20 0.43
0.10
-0.25 -0.13 0.46
0.29
0.00 -0.44 0.82
0.30

After Rotation
FA 2 FA 3 Communality
0.24 0.34 0.74
0.08 0.19 0.81
0.86 0.37 0.92
0.65 0.07 0.43
0.12 0.45 0.46
0.30 0.80 0.82

Eigenvalue
Explained %Cumulative %

5.10
51%
51%

1.30
13%
64%

2.40
24%
51%

0.06
6%
70%

2.60
26%
26%

1.90
19%
70%

Table 5: Factor Analysis Results of 6 Currencies

6 Internationalized Currencies
US Dollar Est.
Std.
t-value p-value
Intercept-0.0007 0.0004 -1.7090 0.0885
Factor 1
0.0106 0.0004 24.2310 2e-16 ***EuroIntercept-0.0007 0.0004 -1.9840 0.0482 *Factor 1
0.0050 0.0004 13.6500 2e-16 ***Pound SterlingIntercept-0.0010 0.0004 -2.4850 0.0135 *Factor 1
0.0100 0.0004 23.2250 2e-16 ***Japanese Yen
Intercept-0.0007 0.0007 -0.9800 0.3280
Factor 1
0.0065 0.0007 8.8750 2e-16 ***Canadian Dollar
Intercept-0.0004 0.0003 -1.0400 0.2990
Factor 1
0.0125 0.0004 28.0500 2e-16 ***Australian Dollar
Intercept-0.0009 0.0011 -0.7700 0.4420
Factor 1
0.0198 0.0012 16.7700 2e-16 ***Chinese RMBIntercept-0.0003 0.0006 -0.4750 0.6350
Factor 1
0.0084 0.0006 14.5150 2e-16 ***3 Key Currencies
Est.
Std.
t-value
Intercept -0.0007 0.0006 -1.2270
Factor 1 0.0085 0.0006 14.5110

p-value0.22102e-16 ***

Intercept -0.0007
Factor 1 0.0047

0.0004 -1.8530
0.0004 12.0260

0.06502e-16 ***

Intercept -0.0010
Factor 1 0.0123

0.0001 -16.9800
0.0001 199.6400

2e-16 ***
2e-16 ***

Intercept -0.0002
Factor 1 0.0069

0.0006 -0.3850
0.0007 14.6100

0.70002e-16 ***

Table 6: The Significance Between Factor and Currency18

6 Internationalized Currencies
US Dollar Est.
Std.
t-value p-value
Intercept
9.22e-01 6.75e-03 136.67
2e-16 ***
Time
1.90e-04 4.29e-05
4.43 1.37e-05 ***EuroIntercept
0.17500.08472.06500.0399 *Time
0.00110.0005
1.9510 0.0521
Pound SterlingIntercept
0.75400.0550 13.7040
2e-16 ***
Time
-0.0004
0.0004 -1.2220
0.2230Japanese Yen
Intercept
5.48e-01 6.68e-02
8.207 9.27e-15 ***Time
2.84e-05 4.25e-04
0.067
0.9470
Canadian Dollar
Intercept
0.80000.0548 14.6050
2e-16 ***
Time
-0.0005
0.0003 -1.4840
0.1390Australian Dollar
Intercept
0.67990.0659 10.3220
2e-16 ***
Time
-0.00070.0004 -1.6570
0.0986Chinese RMBIntercept
0.40670.04109.9280 2e-16 ***Time
0.00240.00039.2210 2e-16 ***Intercept
Time

3 Key Currencies
Est.
Std.
t-valuep-value
0.7097 0.0661 10.7410
2e-16 ***
-0.0012 0.0004-2.83800.0049 **Intercept
Time

0.2359 0.0813
0.0011 0.0005

2.90102.21700.0040 **0.0275 *Intercept
Time

0.9787 0.0096 101.4330
0.0000 0.0001
0.52402e-16 ***
0.6010Intercept
Time

0.0042 0.0776
0.0026 0.0005

0.05400.95705.1800 4.34e-07 ***Table 7: The Significance Between Rolling Correlations and Time Trend

COFER SWIFT
COFER
1
SWIFT
0.4612
1
ROLLING 0.4537
0.2282ROLLING

1

Table 8: Correlation Matrix (COFER, SWIFT, and Rolling Correlation)

Naive Method
Average
Cumulative
Actual-0.36-7.31
Prediction 0.46 (0.042)
9.2 (0.861)
95% CI[0.37, 0.54][7.48, 10.89]

Moving Average
Average
Cumulative
0.24
4.85
0.46 (0.042) 9.20 (0.848)[0.38, 0.54][7.59, 10.86]

Absolute
95% CI-0.82 (0.043)[-0.91, -0.74]-16.5 (0.861)[-18.19, -14.78]-0.22 (0.042)[-0.3, -0.14]

Relative
95% CI-179% (9.4%)[-198%, -161%]-179% (9.4%)-47% (9.2%) -47% (9.2%)[-198%, -161%] [-65%, -30%] [-65%, -30%]

p-valueProb.

0.00101
0.999

p-valueProb.

-4.35 (0.848)[-6.0, -2.74]

0.00201
0.999

Note: In the above table, ”Average” and ”Cumulative” are the average across time during thetraining period and the sum of individual time points. ”Actual” means the actual value examined.
”Prediction” means the predicted value. Values in the brackets are standard deviations. CI is shortfor Credible Interval, referring to the interval that the real parameters of estimation could be in.

Table 9: Causal Impact Analysis Results (Till Dec. 2021)

19

Naive Method
Average
Cumulative
Actual0.33
3.64
Prediction -0.22 (0.049)-2.38 (0.543)
95% CI[-0.32, -0.12][-3.47, -1.36]

Moving Average
Average
Cumulative
0.33
3.64
0.18 (0.049) 2.02 (0.536)[0.087, 0.28] [0.955, 3.08]

Absolute
95% CI-0.04 (0.049)[-0.14, 0.052]-0.44 (0.531)[-1.50, 0.574]-0.15 (0.049) -0.416 (0.55)[-0.051, 0.24] [-0.574, 1.53]

Relative
95% CI-253% (-23%)[-210%, -298%]-253% (-23%)-14% (-26%)[-210%, -298%] [-37%, 63%]-14% (-26%)[-37%, 63%]0.00101
0.999

0.00201
0.998

p-valueProb.

p-value
Prob.

Table 10: Causal Impact Analysis Results (Till Dec. 2020)20

Figure 1: Original Exchange Rates

21

Figure 2: Stationary Exchange Rates

22

Figure 3: Scree Plots23

Figure 4: USD and RMB Rolling Correlations

Figure 5: Other Internationalized Currencies Rolling Correlations

24

Figure 6: Factor Analysis Grouping

Figure 7: Counterfactual Simulation Till December 2021
25

Figure 8: Counterfactual Simulation Till December 202026

Note: The original panel shows the reality data (broken line) and the counterfactual data (blackline). The broken line in the point-wise panel denotes the difference between reality and thecounterfactual. The cumulative panel yields the comprehensive influence of the COVID-19 pandemic.

Figure 9: Causal Impact Results (Dec 2021)

27

Figure 10: Causal Impact Results (Dec 2020)28

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