Global Liquidity, Offshore Bond Issuance andShadow Banking in China
Shugo Yamamoto
July, 2020
Discussion Paper No.2011

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANGlobal Liquidity, Offshore Bond Issuance and Shadow Banking
in China

Shugo Yamamoto * #Research Fellow, Graduate School of Economics, Kobe University
Associate professor, Graduate School of East Asian Studies, Yamaguchi University

This version: March 1, 2020

Abstract
In China, to benefit from abundant global liquidity, offshore affiliates of non-financial
companies have been increasingly used as financing vehicles for accumulating lowyield US dollar liabilities. To elucidate this issue and its implications, we specificallyexamine offshore bond issuance, within-company flows, and shadow banking. Results
indicate that a global liquidity shock will increase shadow banking, as represented by
entrusted loans in China. In spite of strict financial restrictions, about 20% of thevariance of shadow banking is explained by global liquidity shocks.

JEL classification: F3, F32, F34
Keywords: entrusted loan, global liquidity shock, non-financial company, offshorebond issuance, shadow banking, within-company flows*

This work was supported by JSPSKAKENHI Grant Numbers 18K01689.

# E-mail: shugo-y@yamaguchi-u.ac.jp1

1.

Introduction
The United States Federal Reserve Bank (FRB) implemented a monetary easing

policy after the global financial crisis of 2008. That policy triggered and then sustained
a considerable financial boom in economically developing countries through bondissuance. Shin (2013a) regards this as a “second phase of global liquidity.” In emerging
economies, to circumvent capital restrictions in the home country, the use of offshore
affiliates as financing vehicles for accumulating low-yield US dollar liabilities has
become widespread. In spite of strict financial restrictions, the outstanding amounts ofoffshore bond debt of Chinese non-financial companies are overwhelming.
The sudden sharp increase in shadow banking in China in recent years represents animportant difficulty that might affect financial stability 1. To mitigate the financial
vulnerability, monetary authorities in China such as the People’s Bank of China andChina Banking Regulatory Commission are undertaking efforts to cut leverage in thefinancial system by issuing a flurry of regulations. These policies have served to push
up onshore corporate funding costs. By contrast, although the FRB is set to raise interest
rates, offshore funding costs of US dollars will remain cheaper. Therefore, Chinese nonfinancial companies face strong demand to substitute low-yielding US dollar debt forhigher-yielding yuan assets.
An accumulation of both offshore and onshore debt is occurring simultaneously. Do
they have any mutual relation? Do global liquidity shocks representing a “second phase1

Shadow banking is credit intermediation involving activities outside the traditional banking
system. According to Ehlers et al. (2018), in China, the access to low-interest rate bank loans is
restricted to large state-owned companies. In contrast, many small and medium-sized privatecompanies have difficulty getting any bank loan. Therefore, by charging a high lending rate, shadowbanking fills the gap. Using various statistics and indicators, Ehlers et al. (2018) analyzed thevolume and dynamics of shadow banking in China. For example, their total lending is estimated as$6.4 – $9.1 trillion dollars.

2

of global liquidity” have some effect on shadow banking in China? This report explores
answers to these questions. At the core of the questions is regulatory arbitrage that arises
from regulatory differences between offshore and onshore financial markets. Because
strict financial restrictions exist in China, access to cheap US dollars is difficult forbanks and the financial sector. Therefore, non-financial companies behave as financial
intermediaries. An important reason is that capital account transactions through banks
can be regulated tightly, but the transactions of thousands of non-financial companies
generated through international trade are much more difficult to monitor and regulate.
To assess this issue more deeply, we specifically examine within-company flows by
non-financial companies. The findings indicate that non-financial companies act assurrogate intermediaries by issuing bonds at offshore affiliates and by transferring thefunds to the headquarters in the home country 2 (Bruno and Shin, 2017; Huang et al.,2018; Kim and Shin, 2017; McCauley et al., 2015; Shin, 2013a; Shin and Zhao, 2013).
Based on the characteristics presented above, we analyze whether the increase ofoffshore bond issuance and within-company flow by non-financial companies can serve
as a transmission channel of global liquidity shocks to shadow banking in China. A
close study was made by Huang et al. (2018), who reported that non-financial
companies that issue offshore bonds are more likely to become lenders in the shadowbanking system. Similarly, Bruno and Shin (2017) described that non-financial
companies in emerging economies issue bonds denominated in US dollars and conduct
financial intermediation activities similar to a carry trade.

2

Loopholes of various kinds exist for transfer of funds from offshore affiliates to a headquarters in
the home country. For example, according to Chui et al. (2014), the difference between China’sreported copper imports and partners’ reported copper exports to China became extremely wide after
2010. This over-invoicing by Chinese copper corporations represents one instance of capital inflow
by non-financial corporations.

3However, because estimations in those studies were conducted statically andbecause they were based on micro-data, they are unsuitable for assessing the dynamic
interrelation of the variables. It can be expected that the effects of global liquidity
shocks on shadow banking are time-variant and that they might depend strongly onmacroeconomic conditions. For example, after the global financial crisis of 2008, global
liquidity shocks became negative, perhaps exerting negative effects on shadow banking.
Alternatively, if China adopts strict financial restrictions on the shadow banking sector,
then what will happen to the dynamic relation? By applying dynamic methods of avector autoregression (VAR) analysis, one can analyze the time-varying effect of global
liquidity shocks on shadow banking through narratives of macroeconomic conditions
and macroeconomic policies. Then it is possible to complement the results of thepreceding static micro-data studies. Results are expected to engender more fruitfulmacroeconomic policy implications.
Especially, the VAR model analytical framework is based on that presented by
Bruno and Shin (2015), who analyze the propagation of US monetary policy shocks oncross-border bank flows. For their sample period of 1995–2007, they specificallyexamine the global banking sectors of the United States and European countries.
Different from their study, we specifically examine the era of a “second phase of global
liquidity” and economically developing countries. To model the circumvention ofcapital restrictions, our VAR model includes offshore bonds and within-company flows.
Similarly to our motivation, Kim and Shin (2017) use the VAR model and analyze theimportance of offshore bond issuance as a channel for transmission of global liquidity.
However, different from their study, our study emphasizes the role of within-company
flows as a transmission channel of global liquidity shocks. Additionally, different from4

their study, we specifically examine shadow banking.
Particularly, a Bayesian structural VAR model with sign restriction was used for thisstudy, as explained by Uhlig (2005). This approach requires the imposition of only afew sign restrictions that have economically meaningful interpretations and which can
avoid some identification difficulties in identification arising from traditional models
such as those of Cholesky decomposition and the long-run restriction of Blanchard–
Quah decomposition. In line with this, studies using a sign-restricted VAR approach toanalyze the effect of capital flow shock include the following. Sá et al. (2014) reportthat capital-inflow shocks positively affect real credit and real house prices. Tillmann(2013) demonstrates that capital inflow shocks have a strong effect on the increase ofhouse prices and equity prices in Asian countries. Although causality between asset
prices and the capital inflows remains controversial, Fratzscher et al. (2010)
demonstrate that equity market shocks and housing price shocks are the salient
determinants of the current account imbalance.
Finally, our sign-restricted VAR exercises complement firm-level microdata of
Huang et al. (2018) and the VAR analysis of Kim and Shin (2017), indicating how
offshore bond issuance, within-company flows, and shadow banking are influenced by
global liquidity shocks. The remainder of the paper is organized as follows. Section 2
presents a description of the trend behavior of the variables used for empirical analyses.
Section 3 presents the methodology of sign restriction VAR and a data description.
Section 4 presents empirical results. Based on those empirical results, we analyze thetime-varying effect of global liquidity shock on shadow banking in section 5. Finally,
we present conclusions in section 6.

52.

Facts of Offshore Bond Issuance, Within-company Flows, and Shadow
Banking
Because of low interest rate policies imposed by the FRB after 2008, dollar

borrowing costs became extremely low. Therefore, offshore bond issuance by nonfinancial companies in emerging countries increased dramatically after the 2008
financial crisis. The stream of offshore bond issuance by non-financial companies canbe portrayed as Figure 1 (Chung et al., 2015; Shin, 2013a). First, the offshore affiliate ofa non-financial company issues US-dollar-denominated bonds. Then, through withincompany flows, the offshore affiliates transfer funds to their headquarters in the homecountry. Different from Shin (2013b), who specifically examines noncore liability ofcorporate deposits from non-financial companies, we specifically assess entrusted loans:an important component of shadow banking 3. Entrusted loans are inter-company loans
between non-financial companies. Direct borrowing and lending between non-financial
companies is restricted. Therefore, it is facilitated by a financial institution.
Subsequently, the headquarters in the home country will engaged in entrusted loans andlend to the other non-financial companies.

<<insert Figure 1 here>>No available data directly indicate offshore bond issuance by foreign affiliates.
Therefore, one must conduct indirect estimation using existing data. As describedherein, by referring to reports of earlier studies (Gruic and Wooldridge, 2015; McCauley
3

Financing of bank and nonbank financial institutions through noncore liabilities constitutes
shadow banking (Harutyunyan et al., 2014; Harutyunyan et al., 2015). Therefore, we can associate
our findings with noncore liability.

6

et al., 2015; Shin, 2013a), we use statistics of two types reflecting the net issuance ofinternational debt securities: nationality-based measures and the residence-based
measures. The issuer residence is the country in which the issuer is incorporated,
whereas the issuer nationality is the country in which the issuer's parent isheadquartered 4. We can assume that the amount of international debt issued byforeigners within the borders of emerging countries is small. Therefore, we can assume
that the nationality-based measure is the sum of onshore and offshore measures.
Furthermore, we use the residence-based measure as the proxy for the onshore measure.
Consequently, the difference between the nationality-based and the residence-based
measures represents a proxy for the offshore measure. In the following, we describe
offshore US-dollar-denominated bond issuance by international debt securities of nonfinancial companies using data from the Bank for International Settlements (BIS) 5.
Figure 2 presents the stock of offshore bond issuance of non-financial companies in
China (CHN_OFF) and the sum of remaining 3 BRICs countries (Brazil, Russia, India)(WITHOUT_OFF). As this figure shows, the outstanding amounts of China are
overwhelming.

<<insert Figure 2 here>>

After offshore affiliates issue offshore bonds, the funds must be repatriated to theheadquarters in the home country. To circumvent capital restrictions, within-company
4

For example, debts of a Hong Kong subsidiary of a Chinese company might be guaranteed by theparent company. Therefore, debt securities issued by the Hong Kong subsidiary of a Chinese company
would be allocated to Hong Kong on a residence basis and to China on a nationality basis.
5
Hereinafter, unless otherwise noted, all data of offshore bond issuance used for these analyses aredenominated in US dollars. The ratios of US-dollar-denominated bonds to total currency in 2017Q4
were 89.6% for China, 93.7% for Brazil, 72.7% for Russia, and 77.3% for India.

7

flows are used to transfer funds from offshore affiliates to a headquarters in the homecountry. By doing so, even if capital account transactions through banks are regulated
tightly, the transactions of thousands of non-financial companies generated throughinternational trade would be much more difficult to monitor and regulate. Following the
explanation offered by Avdjiev et al. (2014), data of within-company flows can be
described as presented below 6. An accounting convention in the balance of payments(BOP) used by the International Monetary Fund (IMF) deems borrowing and lending
between affiliated entities of the same non-financial companies as “direct investment.”
Specifically, such transactions are classified as “debt instruments”: a sub-category of
direct investment.
According to Avdjiev et al. (2014) and Hardy and Saffie (2019), non-financial
companies have other means to repatriate funds to the home country. In this case, thelender and the borrower non-financial companies differ; they conduct so-called
between-company flows 7. Such flows are captured through trade credit in BOP
statistics. According to the BOP manual, trade credit is a direct extension of credit bysuppliers for transactions in goods and services. Therefore, our data do not include tradefinancing more broadly such as guarantees through banks and letters of credit. As
described in this paper, using trade credit in BOP statistics, we construct data of
between-company flows, and make comparisons with results that use within-company
flows.
6

According to Avdjiev et al. (2014), three fund flow channels exist between the offshore affiliate
and the home country: (1) direct lending to the headquarters; (2) direct extension of credit bysuppliers for transaction in goods and services; (3) providing cross-border loans and deposits toheadquarters in home country by a non-financial company. For this paper, we define the first as awithin-company flow and the second as a between-company flow. Because of data limitations, dataof the third channel are not obtainable.
7
In the terminology of Avdjiev et al. (2014), this is between-company trade credit.

8Here, we describe the trend behavior of three previously described variables: The
differences among offshore bond issuance, within-company flows, and betweencompany flows 8. These three variables are abbreviated as DIF_OFFSHORE, WITHIN,
and BETWEEN as shown in Figure 3, which depicts that offshore bond and withincompany flow data exhibit very similar trend behavior throughout the sample periods.
However, between-company flows do not exhibit similar trend behavior. Offshore bondissuance and within-company flows show similar trend behavior.

<<insert Figure 3 here>>

Finally, we describe the shadow banking of China. Among several shadow banking
types, we specifically examine entrusted loans. According to Huang et al. (2018), byconducting entrusted loan activity, non-financial companies eliminate interest rate
differences between offshore and onshore markets through arbitrage. The loans haveattracted much attention. Recently, in addition to Huang et al. (2018), entrusted loans
have been studied by many researchers such as Allen et al. (2019) and Chen et al.
(2018). Hereinafter, with comparison of the offshore bond issuance of China, one canfind a trend for behavior of entrusted loans from the earlier Figure 2. Data are takenfrom the CEIC database 9. As the figure shows, shadow banking (SHADOW), andoffshore bond issuance of China exhibit similar trend behavior 10. One can infer that the8

It is noteworthy that, because within-company flows and between-company flows are flows of new
investment, to unify the terms used, we calculate the backward difference of stock variables of offshorebond. All data are expressed in US dollars.
9
Because entrusted loans are flows of new investment, we convert to stock variables to unify theterms used in Figure 2.
10
On January 2018, the China Banking Regulatory Commission put new curbs on the entrusted
lending business. Therefore, especially after 2018, shadow banking is showing decreasing trend

9boom in shadow banking in China is fueled to some degree by offshore bond issuanceby offshore affiliates. Empirical analyses must be conducted to analyze linkages among
these variables more deeply.

3.

Empirical Methodology and Data

3-1. Sign restriction VAR
If a positive shock arises in global financial markets, then offshore bond issuance
will increase. Consequently, offshore affiliates will repatriate funds to headquarters in
the home country through within-company flows. Finally, shadow banking will increase
because of accumulation of short-term assets denominated in the domestic currency. As
described in this paper, five-variable Bayesian VAR is estimated. This approach requires
the imposition of only a few sign restrictions that have an economically meaningfulinterpretation. For example, monetary easing shocks are identifiable by imposing
negative responses of interest rates in the first several periods and a positive response ofmoney over the same period. In the following, we briefly describe the methodology ofused for the study by Uhlig (2005).
The procedure can be summarized as the following seven steps: (1) Estimate
unrestricted VAR and obtain the parameters using OLS. (2) Extract the orthogonal
innovations from the model using Cholesky decomposition 11. (3) Calculate the resulting
impulse responses from step 2. (4) Randomly draw an orthogonal impulse vector α. (5)Multiply the responses from step 3 times α and confirm that they match the imposed

behavior. Associated with empirical results presented in section 4, we address this topic again in
section 5.
11
Cholesky decomposition is merely a means of orthogonalizing shocks. Different ordering of thevariables does not alter the results.

10

sign. (6) Check whether it meets the sign restrictions. If so, keep it. If not, reject it. (7)Repeat steps 2–6. It is noteworthy that these steps are based on a joint draw from a flat
Normal Inverted-Wishart posterior for the VAR parameters and uniform distribution for
α.

3-2. Data and identification methodology
According to BIS (2011), the definition of global liquidity is “ease of financing” inglobal financial markets. Specifically, several proxy variables exist as follows: Rey(2015) reports that uncertainty and risk aversion captured by VIX, which is a volatility
index implied by S&P 500 index options, represents global liquidity. Gourinchas andObstfeld (2012) specifically examine the rapid increase in leverage and appreciation of
the domestic currency against the US dollar. Bekaert et al. (2013) describe that the cut
in the Fed Funds rate and VIX index are major sources of global liquidity. Eichenbaumand Evans (1995) find that a contractionary shock to the Fed Funds rate engenders US
dollar appreciation. Bruno and Shin (2015) use the Fed Funds rate, US broker dealer
leverage, VIX, and the real exchange rate to analyze global liquidity shocks. Kim andShin (2017) use domestic credit of the US as a source of global liquidity shock. Finally,
BIS publishes a database of global liquidity indicators that consist of total credit to nonbank borrowers.
According to BIS (2011), these variables are classifiable into price indicators andquantity measures. In fact, BIS (2011) states that “Price indicators tend to provide
information about the conditions at which liquidity is provided, while quantity measures
capture how far such conditions translate into the build-up of potential risks.” Actually,
VIX, the exchange rate, and the Fed Funds rate are classifiable as price indicators.
11

Similarly, we can classify the leverage of US broker dealers, US domestic credit, andglobal liquidity indicators of BIS as quantity measures. Because our sample period is
2005Q1 – 2019Q2, not many sample periods are available. Therefore, we must use theminimized variables to identify the global liquidity shock. As described herein, we use
VIX as a price indicator and global liquidity indicators of BIS as a quantity indicator.
The reason is the following. We have no interest in the origin of a global liquidity
shock, such as US monetary policy shock. Therefore, we did not use the Fed Funds rateor US domestic credit. A consensus exists for using VIX as a price indicator. For
quantity measures, we adopt this global liquidity indicator published by BIS as aquantity measure because it directly indicates global liquidity. It is noteworthy that we
did not use this variable because US broker dealer leverage is a source of funds forwholesale funding for economically developed countries such as the United States andUnited Kingdom.
Including the robustness check test and the additional analysis, we estimate fourmodels. Table 1 presents the imposed sign restriction and estimated models. The main
empirical model is Model 1, which consists of five variables. To identify the positive
global liquidity shock, we impose a negative sign on VIX. The decrease of VIX
indicates global financial market stabilization. Additionally, we impose a positive signon global liquidity indicators of BIS (GLOBAL LIQUIDITY). We set the period of therestriction to 12 quarters. The reason for this long-term restriction is the following.
Because of self-reinforcing interaction of global liquidity shock, it has persistence
characteristics. For example, Bruno and Shin (2015) show for nearly three years that
global shocks affect their own components such as VIX and US broker dealer leverage.
Eichenbaum and Evans (1995) show that global liquidity shock effects persist for five12

years or more. Bekaert et al. (2013) later demonstrate that the effect persists for two
years or more. After identifying global liquidity shock, we will analyze the response ofoffshore bond issuance (OFFSHORE BOND), within-company flows (WITHINCOMPANY FLOW), and shadow banking (SHADOW BANKING). The values of VIX
are referred from the Chicago Board Options Exchange. GLOBAL LIQUIDITY data arereferred from BIS 12. The sources of the remaining three variables were described in an
earlier section. Finally, we take logarithms of all variables 13.

<<insert Table 1 here>>

Next, to confirm our empirical results, we conduct two robustness check tests. First,
based the variables used in Model 1, we impose an additional positive sign restriction
on OFFSHORE BOND because we want to identify a global liquidity shock that is also
originated from OFFSHORE BOND. Second, because most offshore bonds of China aredenominated in US dollars, the exchange rate fluctuation severely affects the behaviorof non-financial companies; we therefore add the US dollar exchange rate to that of theChinese yuan (EXCHANGE RATE) in Model 1. This is emphasized especially by
Bruno and Shin (2015, 2019). Therefore, Model 3 consists of six variables: VIX,
GLOBAL LIQUIDITY, EXCHANGE RATE, OFFSHORE BOND, WITHIN-

12

Relevant details are the following: The currency of denomination is the US dollar. Borrowers'
countries and sectors are all countries excluding residents and non-financial sectors. Lending sectors
are all sectors. Position types are cross-border and local in US dollars.
13
Because the original data of within-company flows, between-company flows, and shadow banking
are flows of new investment, negative values exist. To take log linearization, we accumulate thesevariables and convert them to stock variables. Therefore, the stock variables are used in VAR
estimation. It is noteworthy that the variables used in Figure 2 are stock variables. Those in Figure 3
are flow variables. In addition, we did not take logarithms to these variables in Figures 2 and 3. Finally,
GLOBAL LIQUIDITY and OFFSHORE BOND are stock variables from the beginning.

13

COMPANY FLOW, and SHADOW BANKING. The rise of EXCHANGE RATE
represents Chinese yuan devaluation. The data are taken from International Financial
Statistics of IMF. However, one must keep in mind that because our sample period is
not long, inclusion of the additional variables in the model might decrease theestimation accuracy. Finally, the sign restriction is the same as that of Model 1.
To conduct additional analysis, as an alternative to WITHIN-COMPANY FLOW,
we use between-company flows (BETWEEN-COMPANY FLOW) and analyze thetransmission channel through BETWEEN-COMPANY FLOW. This is designated as
Model 4, for which the remaining variables and sign restrictions are the same as thoseof Model 1. Therefore, Model 4 consists of five variables: VIX, GLOBAL LIQUIDITY,OFFSHORE BOND, BETWEEN-COMPANY FLOW, and SHADOW BANKING. The
sources of BETWEEN-COMPANY FLOW are described in an earlier section.

4.

Empirical Results

4-1. Main results
This subsection presents a description of the main results. Model 1 has fivevariables: VIX, GLOBAL LIQUIDITY, OFFSHORE BOND, WITHIN-COMPANY
FLOW, and SHADOW BANKING. According to the Schwarz Criterion, we set the lag
length to 1.
Results of impulse-response functions of global liquidity shock to the five variables
are presented in Figure 4 14. From the figure, by imposed sign restriction, VIX andGLOBAL LIQUIDITY immediately respond negatively and positively. This response is14

The solid line is the median of the posterior distribution and the dashed lines respectively
represent the 16% and 84% quantiles.

14

a natural consequence. Actually, we are interested in the remaining three variables,
which are not restricted by sign restriction. OFFSHORE BOND significantly andpositively responded against global liquidity shock after three quarters. In response to it,
WITHIN-COMPANY FLOW and SHADOW BANKING responded significantly andpositively after 9 and 4 quarters. Although Bruno and Shin (2015) and Kim and Shin(2017) did not consider WITHIN-COMPANY FLOW and SHADOW BANKING, ourresults are consistent with theirs in that global liquidity shocks have a positive effect on
capital inflows.

<<insert Figure 4 here>>

From a dynamic perspective, the hypothesis of the timing of the transmission
channel is the following: First, the positive global liquidity shock has a positive effecton OFFSHORE BOND; then via the response of WITHIN-COMPANY FLOW, a global
liquidity shock has a positive effect on SHADOW BANKING. However, from Figure 4,
the response of SHADOW BANKING apparently precedes that of WITHINCOMPANY FLOW. Onshore headquarters and offshore affiliates of non-financial
companies are the same companies. For that reason, they need not repatriate funds
immediately. For example, because non-financial companies are conducting merger andacquisition activity in international markets, the headquarters will consider the timing offund repatriation according to international investing activities. Therefore, if prospectsof receiving funds from offshore affiliates rise, the headquarters in the home country
will conduct financial activities and increase SHADOW BANKING without receiving
funds.
15

As the results demonstrate, shocks occurring in global financial markets strongly
influence shadow banking in China. How much of an effect does a global liquidity
shock have on shadow banking in China? To analyze this point, we calculate theforecast error variance decomposition (FEVD) of the variables against a global liquidity
shock. The variables constituting FEVD are presented in Figure 5. At the initial stage,
about 18% of the FEVD of SHADOW BANKING is explained by a global liquidity
shock. As time passes, the number increases. After 20 quarters, it becomes about 21%.
For OFFSHORE BOND, after 20 quarters, it will become about 20%. Therefore, inspite of strict financial restrictions, a non-negligible share of FEVD of SHADOWBANKING is explained by a global liquidity shock. In the case of WITHINCOMPANY FLOW, at the initial stage, about 17% of the FEVD of WITHINCOMPANY FLOW is explained by a global liquidity shock. As time passes, the number
increases. Especially, six quarters later, the effect of global liquidity shock increases,
which is in line with the lagging response of WITHIN-COMPANY FLOW of thepreceding Figure 4.

<<insert Figure 5 here>>In section 5, using this Model 1, we conduct historical decomposition analysis.
Finally, one must reconsider the characteristics of foreign direct investment (FDI).
WITHIN-COMPANY FLOW is classified as FDI. Generally, compared to portfolio
investment, earlier studies have relied on the assumption that FDI is more stable: it isless prone to financial booms and sudden stops. For example, Caballero (2016) andGhosh et al. (2016) report that FDI flows are less likely to trigger a crisis. As a result,
16

FDI flows into economically developing countries are often viewed as stable “cold”
funding that is generated by long-term considerations. By contrast, portfolio flows areoften regarded as unstable “hot” money, with movements triggered by short-term
considerations. Contrary to that conventional view, in economies of emerging countries,
where capital flows are often restricted, the offshore affiliate of a non-financial
company can act as a surrogate intermediary by repatriating funds. Therefore, FDI canturn out to be “hot” money, which transmits the prevailing global financial conditions tothe segregated domestic shadow banking sector.

4-2. Robustness check
To check the robustness of our result, we conduct two robustness check tests. First,
the impulse-response of Model 2 is shown in Figure 6. As described before, we
identified a global liquidity shock originating from OFFSHORE BOND. Therefore, we
add an additional positive sign restriction on OFFSHORE BOND. As the figure shows,
the result is consistent with the earlier presented Figure 4 of Model 1.

<<insert Figure 6 here>>

Next, to control the exchange rate channel, we include the additional variable of
EXCHANGE RATE into Model 1. Consequently, our model consists of VIX, GLOBALLIQUIDITY, EXCHANGE RATE, OFFSHORE BOND, WITHIN-COMPANY FLOW,and SHADOW BANKING. According to the Schwarz Criterion, we set the lag length to1. The results of impulse-response functions of global liquidity shock to six variables
are presented in Figure 7. As shown in this figure, by the imposed sign restriction, VIX
17

and GLOBAL LIQUIDITY respectively respond immediately as negatively andpositively. This is a natural consequence. Then OFFSHORE BOND significantly andpositively responds against global liquidity shock after five quarters.

<<insert Figure 7 here>>

The response of EXCHANGE RATE is specially described. The rise of the value
indicates devaluation of the Chinese yuan. As the figure shows, against positive global
liquidity shock, the response of EXCHANGE RATE is significantly negative, whichindicates appreciation of yuan against the positive global liquidity shock. That result is
in line with that reported by Bruno and Shin (2015) who used a VAR model andanalyzed the effect of a US monetary shock on the exchange rate. Therefore,
appreciation of yuan against the US dollar can be expected to mitigate the burden of US
dollar denominated debt of non-financial companies. In response to it, WITHINCOMPANY FLOW significantly and positively responded after seven quarters. Finally,
SHADOW BANKING significantly and positively responded after one quarter.
However, the response of SHADOW BANKING precedes both WITHINCOMPANY FLOW and OFFSHORE BOND. Therefore, as described before, when
global liquidity shocks erupted, before receiving funds through WITHIN-COMPANY
FLOW, the domestic headquarters in China will foresee fund repatriation and willconduct shadow banking activity before receiving funds. Furthermore, because
EXCHANGE RATE is included in Model 3, because of yuan appreciation, theexpectation of improvements in the balance sheet will also accelerate an immediate
response of SHADOW BANKING.
18

4-3. Additional analysis
To confirm the channel of repatriation of offshore funds to headquarters in the homecountry, instead of WITHIN-COMPANY FLOW, we use BETWEEN-COMPANY
FLOW. Therefore, our model consists of VIX, GLOBAL LIQUIDITY, OFFSHORE
BOND, BETWEEN-COMPANY FLOW and SHADOW BANKING. According to the
Schwarz Criterion, we set the lag length to 1. The results of impulse-response functionsof global liquidity shock for five variables are shown in Figure 8.
As shown in this figure, by the imposed sign restriction, VIX and GLOBALLIQUIDITY respectively respond immediately negatively and positively. This is anatural consequence. Then OFFSHORE BOND significantly and positively responded
against global liquidity shock after two quarters. This result is the same as thoseobtained with Models 1, 2, and 3. However, the response of BETWEEN-COMPANYFLOW is not significant. Therefore, the response of SHADOW BANKING is also notsignificant. Therefore, offshore affiliates of Chinese non-financial companies usually do
not use BETWEEN-COMPANY FLOW channels for repatriation of offshore funds tothe home country.

<<insert Figure 8 here>>5.

Shadow Banking and Macro-prudential Policies in China
How important were global liquidity shocks on shadow banking during our sample

period of 2005–2019? It can be expected that the effects of global liquidity shock on
shadow banking are time-variant and that they might depend strongly on external and19internal macroeconomic conditions. Usually, macroeconomic conditions are not thesame throughout the period. After the global financial crisis of 2008, and the continuing
European debt crisis, global liquidity became persistently negative. Alternatively, after
2016, because of attempts to contain financial risks and to slow an explosive build-up in
debt without stunting economic growth, the monetary authorities of China, including thePeople's Bank of China, have pursued a deleveraging policy.
Therefore, assessment of the relative importance among structural shocks and how
this evolved over time necessitates the use of historical decomposition (HIST)
analysis. 15 The analysis provides an interpretation of historical fluctuations in themodelled time series through the lens of the identified structural shocks. Results
obtained from HIST reveal what portion of the deviation of the variables from its
unconditional mean is attributable to the shocks. In this section, by applying a HIST
approach to Model 1, we can analyze the time-varying effect of global liquidity shock
through narratives of particular historical episodes of macroeconomic conditions. The
response of WITHIN-COMPANY FLOW provides valuable information. Therefore, wewill also specifically examine this variable.
The results from a HIST examination of global liquidity shocks on WITHINCOMPANY FLOW and SHADOW BANKING are shown in Figures 9 and 10 16. As theglobal financial crisis in 2008 began, the negative global liquidity shock (GL_SHOCK),
exerted a negative effect on both WITHIN-COMPANY FLOW (DT_WITHIN), andSHADOW BANKING (DT_SHADOW), as the figure shows. The negative effect15

Historical decomposition is obtained using the structural moving average representation to
separate series in the components (mutually orthogonal) attributable to the different structural
shocks. Doan (2009) presents related details.
16
Both variables are detrended using a Hodrick–Prescott filter.

20

persisted until 2013. During this period, the European debt crisis also made global
financial conditions worse and persistent. Therefore, in spite of rising debt amounts ofthe shadow banking sector in China, the contribution of global liquidity shocks was
negative during this period.

<<insert Figure 9 here>><<insert Figure 10 here>>

Nevertheless, to recover economic growth and employment after the global financial
crisis of 2008, the Chinese government deployed a stimulus package. Therefore, thegrowth rates of M2 of China in 2009 and 2010 were, respectively, 26.4% and 20.8%.
According to Chen et al. (2020), excess liquidity and the stimulus package contributed
to the rise of the shadow banking sector.
After 2014, as global financial conditions improved, the global liquidity shock
became positive. In fact, it turned out to have a positive effect on both WITHINCOMPANY FLOW and SHADOW BANKING, as the figure shows. At the same time,
as shown in Figure 2 earlier, offshore bond issuance of China rose steeply. The amount
has come to exceed the sum of the other three BRICs countries. Therefore, one can say
that, after 2014, the global liquidity shock has contributed to the rise of the shadowbanking sector.
By contrast, the growth rate of M2 of China decreased from 12.7% in 2014 to 8.8%
in 2018 because, to prevent overheating of the credit boom and to mitigate theaccumulation of potential financial distress, the Macro Prudential Assessment
framework was launched on 2016. The financial deleveraging policy has pushed up
21

money market rates, bond yields, and corporate funding costs as the broad M2 money
supply growth has decreased. Therefore, the domestic credit shortage will engender arise in the importance of repatriating cheap offshore funds to headquarters in the homecountry through entrusted loans. This result roughly matches that produced by Allen etal. (2019) who argue that entrusted loans are fundamentally a market reaction to credit
shortages. Furthermore, according to Chen et al. (2018), contractionary monetary policy
caused shadow banking loans to rise rapidly, offsetting the expected decline of
traditional bank loans and hampering the effectiveness of monetary policy for total
credit in the banking system.
Finally, we specifically examine the year of 2018 when the China Banking
Regulatory Commission put new curbs on entrusted lending. During this period, Figure
2 shows that although offshore bond issuance still has an upward trend, a sudden
decrease exists in shadow banking. From Figure 10, the effect of global liquidity shocks
on SHADOW BANKING vanished after 2018. As shown in Figure 9, the effect of
global liquidity shock on WITHIN-COMPANY FLOW persists. Moreover it has an
upward trend. Therefore, Chinese non-financial companies are still issuing bonds inoffshore markets and are transferring the funds to the home country through somethingother than entrusted loans. Therefore, one can reasonably infer that the restriction policyon entrusted loans can not fully prevent the effects of global liquidity shocks on thedomestic economy. Although we are interested in the whereabouts of the funds, thatissue is beyond the scope of this paper.

6.

Conclusions
In China, offshore affiliates are increasingly used as financing vehicles to22

circumvent capital restrictions by accumulating low-yield US dollar liabilities.
Furthermore, funds are repatriated from offshore affiliates to headquarters in the homecountry via within-company flows. To analyze this issue more deeply and dynamically,we specifically examine shadow banking. We analyze whether the increase of offshorebond issuance and within-company flows are important transmission channels of global
liquidity shocks. From the impulse-response function of VAR analysis, we ascertainedthat global liquidity shocks have a positive effect on offshore bond issuance.
Furthermore, through within-company flows, the funds will be transmitted to shadowbanking.
Our study is based on that of Huang et al. (2018). Using firm-level micro data, theyfind that non-financial companies that issue offshore bonds are more likely to become
shadow banking system lenders. In our study, we analyze the time-varying effect of
global liquidity shock on shadow banking through narratives of macroeconomic
conditions. According to our results, global liquidity shocks on shadow banking dependon external and internal macroeconomic conditions. For example, as described by Chenet al. (2018), because of offshore funds, contractionary monetary policy causes shadowbanking loans to rise rapidly, consequently hampering the effectiveness of monetary
policy. Alternatively, when the China Banking Regulatory Commission put new curbs
on the entrusted lending in 2018, although effects of global liquidity shocks on entrusted
loans vanished, offshore funds are still inflowing to the home country through somemode other than entrusted loans.
In addition, using the VAR model, Kim and Shin (2017) identified global liquidity
shocks and analyzed the respective responses of offshore bond issuance and domestic
interest. Particularly addressing within-company flows as a surrogate intermediary23

through fund repatriation, our VAR exercises complement their interesting work.
Generally, compared to portfolio investment, those earlier studies assumed that FDI
is more stable and less prone to financial booms and sudden stops. Therefore, FDI flows
into economically developing countries are often viewed as stable “cold” money
generated by long-term considerations. Contrary to that conventional view, in
economies such as those of emerging countries where capital flows are restricted, theoffshore affiliate of a non-financial company can act as a surrogate intermediarythrough fund repatriation. As described in Avdjiev et al. (2014), via within-company
flows, FDI can turn out to be “hot” money, thereby transmitting global financial
conditions to a segregated domestic economy.

24

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Figure 1: Non-financial company as a surrogate intermediary.

Note: We make a change to figures presented by Shin (2013a) and Chung et al. (2015).

29

Figure 2: Offshore bond issuance and shadow banking.

Note: CHN_OFF and WITHOUT_OFF denote the offshore bond issuance of China and the sum of
the remaining three BRICs countries (Brazil, Russia, India). SHADOW is entrusted loans ofChina, for which the axis is at the right-hand side. It is noteworthy that, because entrusted loans
represent a flow of a new investment, we convert to stock variable to unify the terms.
Source: CHN_OFF and WITHOUT_OFF are from BIS; SHADOW is from CEIC.

30

Figure 3: Differences of offshore bond issuance, within-company flows and between-company
flows.

Note: WITHIN denotes within-company flows; BETWEEN denotes between-company flows.
Because these variables are flows of new investment, to unify the terms used, we calculate
DIF_OFFSHORE by the backward difference of stock variables of offshore bond issuance.
Sources: WITHIN and BETWEEN are referred from BOP of the IMF; DIF_OFFSHORE is referredfrom BIS securities statistics.

31

Figure 4: Impulse–response function of Model 1 (per cent).

Note: To generate the impulse vectors, 200 draws from the posterior and 200 subdraws for eachposterior draw were conducted.

32

Figure 5: Variance decomposition of Model 1 (per cent).

33

Figure 6: Impulse–response function of Model 2 (per cent).

Note: To generate the impulse vectors, 200 draws from the posterior and 200 subdraws for eachposterior draw were conducted.

34

Figure 7: Impulse–response function of Model 3 (per cent).

Note: To generate the impulse vectors, 200 draws from the posterior and 200 subdraws for eachposterior draw were conducted.

35

Figure 8: Impulse–response function of Model 4 (per cent).

Note: To generate the impulse vectors, 200 draws from the posterior and 200 subdraws for eachposterior draw were conducted.

36

Figure 9: Historical decomposition of WITHIN-COMPANY flow.

Note: GL_SHOCK is global liquidity shock on WITHIN-COMPANY FLOW. DT_WITHIN is
detrended using a Hodrick–Prescott filter to WITHIN-COMPANY FLOW. The axis is at theright-hand side.

37

Figure 10: Historical decomposition of SHADOW BANKING.

Note: GL_SHOCK is the global liquidity shock on SHADOW BANKING. Also, DT_SHADOW isdetrended using a Hodrick–Prescott filter to SHADOW BANKING. The axis is at the right-hand
side.

38

Table 1: Used variables and sign restrictions

Note: Model 1 denotes the model used in section 4-1. Model 2 and Model 3 are models used in
section 4-2. Model 4 denotes the model used in section 4-3. “<0” denotes negative sign restriction;“>0” denotes a positive sign restriction. NO REST denotes no sign restriction. “-” represents
variables that are not used.

39