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Disguised Carry Trade and the Transmission of Global Liquidity Shocks:
Evidence from China’s Goods Trade Data
Shu Lina, Jinchuan Xiaob, and Haichun Yec
Abstract
Currency carry trade disguised as goods trade can potentially channel external financial
shocks to domestic economic environment, despite capital controls. We identify this channel in
the context of post-GFC China using variations in product characteristics and a policy shock. We
show that trade volumes of cost-efficient products responded significantly more to carry returns.
However, such differential responses to carry returns vanished after the government’s
clampdown on illicit capital flows. At the aggregate level, we demonstrate further that the surge
in disguised carry trades led to a significant expansion of China’s shadow banking credit but not
its traditional bank lending credit.
Keywords: carry trade; goods trade; global liquidity shock; capital control; shadow banking
JEL classification: F38; F14
____________________________________ a Department of Economics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
Email: [email protected]. b Research Institute of Industrial Securities Co., LTD.,15/F Industrial Securities Building, No. 36
Changliu Road, Shanghai, P. R. China. Email: [email protected]. b Corresponding author. School of Management and Economics, CUHK Business School,
Chinese University of Hong Kong (Shenzhen), 2001 Longxiang Boulevard, Longgang District,
Shenzhen, Guangdong Province, P. R. China. Email: [email protected].
1
1. Introduction
A widely discussed issue among academic researchers and policymakers is how global
liquidity shocks are transmitted across borders, especially in the aftermath of global financial
crisis (GFC). Existing contributions in the literature have examined various transmission
mechanisms, including the trilemma channel (e.g., Mundell, 1963; Obstfeld et al., 2004, 2005;
Frankel, et al., 2004; Aizenman et al., 2016), the global bank lending channel (e.g., Cetorelli and
Goldberg, 2012a, 2012b; Bruno and Shin, 2015; Morais et al., 2019), and the foreign direct
investment (FDI) channel (e.g., Lin and Ye, 2018). In this study, we aim to contribute to this
growing literature by exploring a new channel – disguised carry trade via goods trade – through
which global liquidity shocks can be propagated to emerging market (EM) economies despite
tight capital controls.
To identify such a channel, we focus on China’s experience during the post-GFC period.
Several key features make this environment particularly suitable for detecting disguised carry
trades via goods trade. First, as we will discuss in more details in Section 2, monetary easing
conducted by major advanced economies following the GFC created ample global liquidity and
ultra-low interest rates in global financial markets, resulting in a large RMB-USD interest rate
differential. The widened interest rate differential together with a steady appreciation of the RMB
against the USD make the “China carry” (i.e., long RMB and short USD) particularly attractive.
The second feature is China’s tight capital control policy which prohibits regular cross-border
financial arbitrage activities. Investors thus need to find alternative strategies to circumvent
capital controls. Finally, while China poses tight controls on cross-border financial transactions,
it is highly open to international trade, which makes goods trade an ideal candidate to be used for
facilitating carry trades.
Uncovering carry trade activities in the goods trade data, however, is a challenging task.
One needs to find a way to disentangle goods trade used to cover up carry trades from genuine
2
goods trade activities. We overcome this empirical challenge by making use of China’s
disaggregate product-level trade data and exploiting variations in product characteristics as well
as a unique policy shock. Our main identification strategy is motivated by the observation that
cost-efficient products are deliberately chosen as investment vehicles so as to save logistics costs
incurred in the process of disguised carry trade. Hence, we expect that trade volumes of cost-
efficient products to respond significantly more to changes in carry returns as compared to those
of cost-inefficient products. Moreover, we also utilize Chinese regulatory authorities’ joint
crackdown on illicit capital inflows in May 2013 as a natural experiment to further verify the
presence of disguised carry trade and to examine how disguised carry trades respond to this
government crackdown campaign. Finally, we also make efforts to track the macroeconomic
impact of such disguised carry trades on China’s domestic credit conditions, particularly the
growth of shadow banking credit.
Based on China’s monthly product-level trade data over the period between September
2008 and December 2014, we find strong evidence for disguised carry trade activities via goods
trade. A rise in the return to the “China carry” is associated with significantly larger increases in
export volumes of products with higher value-to-weight ratios (i.e., more cost-efficient). Such
differentials, however, narrowed significantly following Chinese government agencies’ joint
crackdown campaign on illicit capital inflows launched in May 2013. Similar patterns are also
observed in China’s imports data, confirming that firms indeed use redundant goods trade to
conduct currency carry trade repeatedly. Having established the existence of disguised carry
trade via goods trade, we then conduct a vector autoregression (VAR) analysis using aggregate-
level data. We show that a positive shock to carry returns leads to an increase in disguised carry
trade via goods trade, which, in turn, exerts a sizable positive impact on the expansion of China’s
shadow banking credit but not the traditional bank lending credit. Taken together, our findings
suggest that disguised carry trade via goods trade can channel external liquidity shocks to
3
domestic economy and have an impact on local credit conditions.
Our study contributes to the relevant literature in the following aspects. First, we
contribute to the growing literature on the transmission of global liquidity shocks. While
previous studies have explored the roles of exchange rate regimes (e.g., Mundell, 1963; Obstfeld
et al., 2004, 2005; Frankel, et al., 2004; Aizenman et al., 2016), cross-border bank lending flows
(e.g., Cetorelli and Goldberg, 2012a, 2012b; Bruno and Shin, 2015; Morais et al., 2019) and FDI
flows (e.g., Avdjiev et al., 2014; Lin and Ye, 2018) in the international transmission of liquidity
shocks, we add to this literature by identifying a novel goods-trade-based channel that can
propagate global liquidity shocks to domestic economy despite tight capital controls.
Secondly, our work complements recent studies on how non-financial firm’s offshore
financing behavior affects domestic economic conditions. For example, Caballero et al. (2015)
and Bruno and Shin (2016) find that non-financial firms in EM economies have issued large
amount of dollar bonds to accumulate financial assets for carry trades. In the context of China,
Huang and Portes (2016) show that such dollar bond issuances by nonfinancial firms have
significant real and financial effects on local economic environment. Besides foreign-currency
bond issuances, there are also studies (e.g., Avdjiev et al., 2014) suggesting that EM non-
financial firms typically employ foreign direct investment (FDI) to circumvent capital controls. A
recent study by Lin and Ye (2018) provides evidence that, despite the imposition of capital
controls in China, the presence of FDI firms can import global liquidity shocks to domestic
economy through a trade credit channel. This paper departs from previous studies by focusing on
non-financial firms' engagement in carry trades under the cover of goods trade and its potential
impact on domestic financial conditions.
Thirdly, our work is also related to studies on anomalies in cross-border trade and financial
flows. Various explanations for such anomalies have been proposed so far, chief among which
are tax/tariff evasions and trafficking of restricted items. For example, Fisman and Wei (2004)
4
find strong evidence of tariff evasion as the discrepancy between Hong Kong’s reported exports
to China and China’s reported imports from Hong Kong increases in tariff rates. This is later
confirmed by Yang (2008) with data from the Philippines and Ferrantino et al. (2012) with the
US-China bilateral trade data. Prasad and Wei (2007) show that a large portion of FDI inflows
from Hong Kong to China are the “round-tripping” of funds originating in China for tax evasion
purposes to take advantage of preferential tax treatment of foreign investment relative to
domestic investment. Also, Fisman and Wei (2009) reveal that trafficking of cultural property
and antiques gives rise to the irregularities in the trade data. In this study, we provide addition
context to the anomalies puzzle by pointing to the evasion of capital controls as a plausible
explanation.
Finally, our study also connects with the literature on the linkage between financial
openness and trade openness. For example, Aizenman (2004, 2008) show that trade openness in
emerging countries erodes the effectiveness of capital controls. The cross-country study by
Aizenman and Noy (2009) shows that increasing openness to international trade can foster
openness to international capital flows. Compared to these macro-level studies, our work
complements this literature by providing micro-level evidence that trade openness promotes de
facto financial openness.
From a policy perspective, while our results are based on China’s experience, they have
broader implications for other emerging economies that impose tight controls on cross-border
financial transactions but are highly open to trade. Our findings suggest that, as EM countries are
increasingly involved in international trade of goods and services, it becomes more and more
difficult for them to rely on capital controls alone to insulate their domestic economies from
external financial shocks. Moreover, as shown in our study, the illicit capital inflows are often
associated with risky shadow banking activities at home. To better manage risks and achieve
financial stability, policymakers should step up their efforts to strengthen prudential regulations
5
and supervisions and push forward warranted macroeconomic adjustments to accommodate large
external liquidity shocks.
The remainder of this paper is organized as follows. Section 2 provides the institutional
background for the disguised carry trade and the subsequent government crackdown in China.
Section 3 develops testable hypotheses and discusses our empirical strategy along with the data
used. Section 4 details empirical findings. Concluding remarks are offered in Section 5.
2. Background
2.1. The “China Carry” Trade
In an effort to reinvigorate growth in the wake of the global financial crisis (GFC
hereafter), most advanced economies cut their domestic interest rates to near zero and injected
large sums of liquidity to the global financial system via unconventional monetary policies. This
ultra-loose global monetary environment has encouraged investors to actively search for higher
yields (e.g., Hau and Lai, 2016; Di Maggio and Kacperczyk, 2017; Choi and Kronlund, 2018;
Morais, et al., 2019). One prevalent investment vehicle during the post-GFC period was the
Chinese Renminbi (RMB), whose widening positive interest rate differential relative to the US
dollar (USD), together with its steady appreciation against the USD, created a lucrative
opportunity for the “China carry” trade (i.e., borrowing the USD and investing in the RMB).
Figure 1 plots the time paths of the onshore spot exchange rate of RMB per USD (i.e., a
decline in the spot exchange rate value indicates an appreciation of the RMB against the USD),
the RMB interest rate proxied by the 3-month Shanghai Interbank Offered Rate (Shibor), the
USD interest rate proxied by the 3-month USD London Interbank Offered Rate (Libor) and
Bloomberg’s RMB carry return index (in natural logarithm) over the period from September
2008 to December 2014. Two striking observations stand out. One is the sizable positive interest
rate differential between the RMB and the USD. As the USD interest rates approached zero
6
following the GFC, the 3-month Shibor-Libor interest rate differential widened sharply,
averaging 3.8 percentage points between September 2008 and December 2014. The other is the
steady appreciation path of the RMB exchange rate against the USD during the sample period,
with the RMB exchange rate against the USD appreciating over 10%. The combination of
widening positive interest rate differential and RMB’s steady appreciation creates a mentality
that RMB is a one-way bet. As such, the carry return from the “long RMB and short USD”
strategy has climbed up substantially during the post-GFC period. The Bloomberg’s carry return
index soared by around 14.3% between September 2008 and December 2014.
2.2. The Disguised Carry Trade and the Crackdown
Despite the attractiveness of the “China carry” trade opportunity, it is not easy to reap
profits from such a currency carry trade due to China’s strict capital controls. One popular
approach to get around capital controls so as to effectively benefit from the “China carry” trade
is to disguise the RMB/USD carry trade as a flow of goods trade. To better understand the core
mechanics of this disguised carry trade, we provide an illustrative example in Figure 2 based on
the actual market information between June and September 2011:
1. In June 2011, a Mainland company exported a certain product (e.g., electronic integrated
circuit) for USD 1million (mn hereafter) to its Hong Kong partner.
2. The Hong Kong partner applied for a 3-month USD loan of 1mn to pay for its imports and
was granted such a loan by a Hong Kong bank with an interest rate of 2.25% per annum.1
The Hong Kong partner then made a payment of USD 1mn to the Mainland company. In
doing so, the USD funds was legitimately moved into Mainland China.
3. Upon receiving the export proceeds of USD 1mn, the Mainland company converted it into
RMB 6.475mn at the spot exchange rate of 6.475 and then invested in RMB assets for three
1 The cost of borrowing the USD is assumed to be 2.25%, 200 basis points above the 3-month USD Libor (0.25%) in
June 2011.
7
months with a fixed return of 5.48% per annum.
4. Three months later, the Mainland company collected a total sum of RMB 6.5637mn from its
RMB investment and converted it back into USD1.0272mn at the spot exchange rate of
6.390.
5. The Mainland company then conducted a reverse purchase of the same goods from its Hong
Kong partner at slightly higher price and paid USD1.0056mn to cover its Hong Kong
partner’s dollar loan (principal plus interest payment). In the end, this disguised carry trade
yielded a profit of around USD 21,559.
In short, rather than engaging in genuine goods trade, firms have turned to conducting the
“China carry” trade dressed up as goods trade instead. It is worth noting that, for illustration
purpose, here we simply use the 3-month Shanghai interbank-offer rate (Shibor) as a proxy for
the investment return to RMB assets, while, in reality, investors usually invested in shadow
saving instruments (e.g. wealth management products and trust products) to earn higher return.
Moreover, although the Mainland company in the above example is assumed to carry out its
reverse purchase in three months, in practice, quite a few companies initiate their reverse
purchases right after exporting so that the same goods can be traded more frequently to extract as
much carry return as possible.
The surge in such disguised carry trade activities and the subsequent anomalies in trade
data eventually caught Chinese regulatory authorities’ attention as they recognized that
companies had been secretly channeling capital inflows in to China under the cover of goods
trade. Amid rising concerns that hot money inflows would fuel shadow banking activities and
pose a threat to China’s financial stability, the Ministry of Finance (MoF), the General
Administration of Customs (GAC), the China Banking Regulatory Commission (CBRC), and the
State Administration of Foreign Exchange (SAFE) launched a joint campaign in May 2013 to
crack down speculative funds entering the country. New rules were released to tighten limits on
8
long RMB positions that banks could hold for their own accounts and to discourage companies
from engaging in the “China carry” trade. In particular, Chinese regulators increased scrutiny on
exporters who channeled money into the country disguised as trade payments. For example, the
SAFE issued warnings to companies involved in suspicious disguised carry trade activities. If
these companies were unable to provide satisfactory explanations for their activities, they would
be placed on the SAFE’s blacklist and subject to strict supervision until they demonstrated
compliance with regulations. Overall, this clampdown made it harder for companies to evade
capital controls and thus acted as a deterrent to disguised carry trade via goods trade.
3. Empirical Design and Data
3.1. Hypothesis Development
To empirically identify disguised carry trade in goods trade data, it is crucial to recognize
that not all goods are suitable candidates for such a disguised carry trade. Given that logistics and
transportation expenses constitute the bulk of transaction costs incurred in these disguised carry
trades, firms would deliberately employ cost-efficient goods to trade, largely products with high
value-to-weight ratios. Motivated by this observation, our identification strategy exploits cross-
product variation in value-to-weight ratios and the resulting differential trade responses to
changes in carry returns across products. Specifically, we conjecture that export volumes of
higher value-to-weight-ratio products would expand significantly more as returns to carry trade
rise.
Next, we employ the Chinese regulatory authorities’ joint crackdown on disguised carry
trade since May 2013 as a policy shock to shed more light on disguised carry trades. Given that
Chinese regulatory authorities aim to deter non-financial firms from engaging in disguised carry
trade, their joint crackdown targeted mainly high value-to-weight products used for facilitating
carry trades. We thus expect that the joint crackdown campaign weigh more on the trade of high
9
value-to-weight ratio products in response to carry return changes, leading to attenuated
difference in trade responses to carry returns across products in the post-crackdown era.
Moreover, if the joint crackdown is indeed effective in curbing illicit capital inflows through
disguised carry trade, the differential trade responses to carry returns may disappear completely.
Last, at the aggregate level, we also make attempts to assess the potential impact of such
disguised carry trade on China’s domestic credit conditions. The disguised carry trade has served
as an effective means of circumventing capital controls and funneling cheap foreign funds to
domestic financial system. As such, we expect that fund inflows through the disguised carry
trade may have helped to fuel the rapid credit growth in China over the last decade. Particularly,
we suspect that the disguised carry trade may have contributed more to the strong expansion of
shadow banking credit than traditional bank lending in China because shadow banking funding
instruments usually offer higher yields than traditional deposits2 and are typically perceived as
“safe” assets by investors due to their implicit guarantee feature (Claessens and Ratnovski, 2014;
Dang et al., 2014; Ehlers, et al., 2018).
3.2. Empirical Specifications
To test for differential trade responses across products to changes in the carry return, we
specify a panel regression model with product and time fixed effects as follows:
lnyit = βlogcrt × vwratioi + γ'X + μi + τt + εit, (1)
where the dependent variable is log real export volumes of product i in month t, logcrt is logged
return to the “China carry” trade in month t, vwratioi is product i's value-to-weight ratio, and X
represents a set of control variables. μi is product fixed effects that capture time-invariant
2 While China has taken important steps to liberalize its interest rates over the past two decades, an official ceiling on
deposit rates, set by the People’s Bank of China (PBoC), has not been scrapped until October 2015. Even after the
removal of the official ceiling, the deposit rates are still largely constrained by PBoC’s informal guidance on the upper
limit of commercial banks’ deposit rates as well as the market interest rate pricing self-regulation mechanism, which prevents commercial banks from attracting deposits by offering interest rates higher than those agreed on by the self-
disciplinary mechanism.
10
product-level unobserved heterogeneity, and τt is time fixed effects that control for the time trend
common to all products.
Our variable of interest here is the interaction term between log carry trade return and
product’s value-to-weight ratio, logcrt × vwratioi. A positive and significant coefficient on this
interaction term (β > 0) suggests that higher return to the carry trade is associated with a larger
increase in export volumes of higher value-to-weight ratio products. This would be considered as
supportive evidence for the presence of disguised carry trade via goods trade. Now that the
value-to-weight ratio (vwratio) does not vary over time for each product, its level effect is thus
absorbed by product fixed effects. Likewise, the level effect of logcr is submerged with time
fixed effects as it is common to all products but varies over time.
As for the set of control variables (X), we include an interaction term between the VIX
index (in natural logarithm) and product’s value-to-weight ratio to control for the impact of
global risks on the disguised carry trade. While genuine goods trade is generally not expected to
be affected by global risks differently across products, disguised carry trade may generate
product-level heterogeneity in export volumes as a response to changing global risks. According
to Brunnermeier et al. (2008), an increase in the VIX index (i.e., rising global risks) often
coincides with carry trade losses. This should apply not only to outright carry trade transactions
but also to disguised carry trades. Particularly, more disguised carry trade would arise amid
subdued global risks, causing stronger growth in the trade volumes of higher value-to-weight
ratio products. Thus, a significantly negative coefficient on this interaction term would lend
further support for the presence of disguised carry trade through goods trade.
In addition, to purge off potential confounding effects of global economic factors on the
differential trade responses across products, we also include the interaction of value-to-weight
ratio with world real GDP growth (wrgdpg) and the interaction of value-to-weight ratio with
international commodity price inflation (infcom) as additional controls. We also include export
11
tax rebate rate for each product (rebate) to control for the impact of tax incentives on exports.
To examine the impact of Chinese regulatory authorities’ joint crackdown on disguised
carry trade, we further add a triple interaction term to the model specification:
lnyit = βlogcrt × vwratioi + δlogcrt × vwratioi × postcrackt
+ λvwratioi × postcrackt + γ'X + μi + τt + εit, (2)
where postcrackt is a dummy variable for the post-crackdown period that takes on the value of
unity for the period after May 2013, and zero otherwise.3 Our main focus here is the triple
interaction term, logcrt × vwratioi × postcrackt, which compares the differential trade response to
carry returns before and after the joint crackdown campaign. A negative and significant
coefficient on the triple interaction term (δ < 0) is expected to be consistent with our hypothesis
that the crackdown has largely discouraged disguised carry trade via goods trade. Moreover, if
the crackdown were indeed effective in curbing disguised carry trade, no significant cross-
product difference in trade response to carry returns should be observed in the post-crackdown
period, implying that β + δ = 0.
Finally, to further investigate how illicit fund inflows through the channel of disguised carry
trade affects China’s domestic credit conditions on an aggregate basis, we consider a VAR model
based on monthly data for zt = (logcrt, logexpgapt, logcreditt)’, where logexpgapt is an empirical
proxy for the scale of disguised carry trade activities measured as the log difference in trade
volumes between high and low value-to-weight ratio products4, and logcreditt refers to the
3 Since the interaction between carry return and the post-crackdown dummy (logcrt × postcrackt) only varies over
time, it is absorbed by time fixed effects. 4 Since the data on the scale of disguised carry trade is not available, we use the difference in export volume between
high and low value-to-weight ratio products as an empirical proxy. This is motivated by our empirical findings in
Section 4.1 that product-level exports respond quite differently to carry returns, with high value-to-weight product
being more responsive than the low value-to-weight ones. As such, the gap in export volumes between high and low
value-to-weight ratio products, especially its response to carry return shocks, contain useful information about the
magnitude of disguised carry trade.
12
natural logarithm of newly increased domestic shadow banking credit (or traditional bank
lending credit). The trivariate VAR representation is specified as follows:
𝐴0𝑧𝑡 = 𝛼 + ∑ 𝐴𝑖𝑧𝑡−𝑖𝑝𝑖=1 + 𝜀𝑡, (3)
where 𝜀𝑡 denotes the vector of serially uncorrelated orthogonal shocks. Specifically, the matrix
𝐴0−1 has a recursive structure such that reduced-form errors 𝑒𝑡 can be decomposed as a linear
combination of the orthogonal shocks according to 𝑒𝑡 = 𝐴0−1𝜀𝑡 and E(𝑒𝑡𝑒𝑡
′) = Σ = 𝐴0−1(𝐴0
−1)′:
𝑒𝑡 ≡ (
𝑒𝑡𝑙𝑜𝑔𝑐𝑟
𝑒𝑡𝑙𝑜𝑔𝑒𝑥𝑝𝑔𝑎𝑝
𝑒𝑡𝑙𝑜𝑔𝑐𝑟𝑒𝑑𝑖𝑡
) = [𝑎11 0 0𝑎21 𝑎22 0𝑎31 𝑎32 𝑎33
] (
𝜀𝑡𝑐𝑎𝑟𝑟𝑦 𝑟𝑒𝑡𝑢𝑟𝑛
𝜀𝑡𝑑𝑖𝑠𝑔𝑢𝑖𝑠𝑒𝑑 𝑐𝑎𝑟𝑟𝑦 𝑡𝑟𝑎𝑑𝑒
𝜀𝑡𝑑𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑐𝑟𝑒𝑑𝑖𝑡
).
The above exclusion restrictions on contemporaneous responses in the matrix 𝐴0−1 imply that
log carry return is affected by its own contemporaneous shock only, log difference in real exports
between high and low value-to-weight ratio products is affected by its own contemporaneous
shocks as well as the contemporaneous shock to carry return, while China’s domestic credit (in
natural log) is affected by all three types of contemporaneous shocks. Based on these
identification schemes, we then perform impulse responses and variance decomposition to
examine the dynamic relation among carry returns, disguised carry trades and China’s domestic
credit conditions.
3.3. Data
Our sample consists of monthly data from September 2008 to December 2014. The chosen
sample period coincides with the post-GFC period when the interest rate differential between
RMB and USD widened sharply and RMB strengthened steadily against USD, leading to a surge
in disguised carry trade via goods trade in China. Moreover, this period also witnessed the strong
growth of domestic credit, especially shadow banking credit, in China. Thus, it suits our purpose
to examine the workings of disguised carry trades in China and to assess its potential impact on
China’s domestic credit conditions.
13
Monthly Product-Level Trade Volume. – China’ monthly goods trade data at the HS 4-digit
product disaggregation is extracted from China's General Administration of Customs (CGAC).
The monthly values of exports and imports are then seasonally adjusted and deflated by China’s
Consumer Price Index (CPI).
Product-Level Value-to-Weight Ratio. – Our measure of value-to-weight ratio for each
product is obtained from the BACI world trade database developed by the CEPII. Specifically,
we convert unit values into value-to-weight ratios by adopting a common measure of unit value,
thousand dollars per ton, for each product.5 Given that products’ cost-efficiency characteristics
are fairly persistent and that most time variations in value-to-weight ratios are likely to be driven
by price changes, we use the value-to-weight ratio in 2005 for each product to reflect cross-
product heterogeneity in cost efficiency. To facilitate interpretation, we standardize value-to-
weight ratio (vwratio) so that it has a zero mean and standard deviation of unity when used in our
regression analysis.
Return to the “China Carry”. – To measure the carry returns to the “long RMB and short
USD” strategy, following Bruno and Shin (2016), we use the carry return index (in natural
logarithm) from Bloomberg, which captures the combined returns from both interest rate
differential and exchange rate movements. As evident from Figure 1, the carry return index has
been on a rising trend during the post-GFC period. Meanwhile, China’s export structure has been
upgrading towards higher value-added products during the same period of time. This may raise
concerns that the relationship between rising carry returns and stronger export growth for
products with higher value-to-weight ratios would have nothing to do with disguised carry trade
activities but simply driven by a common upward trend in both series. To address this concern,
we remove the trend component from the log carry return index by applying the Hodrik-Prescott
5 A high unit-value product does not necessarily have a high value-to-weight ratio. For example, automobiles have high unit
values but low value-to-weight ratios.
14
(HP) filter and simply use its cyclical component as our measure of carry trade return throughout
the paper unless stated otherwise.
For the sake of robustness, we also construct two Sharpe ratio-based measures for returns
to the long RMB and short USD carry. Follow Brunnermeier et al. (2008), we first compute the
excess return to this strategy as iCN - iUS - (f - s), where iCN and iUS are three-month interest rates
(in natural log) in China and the U.S., f is the three-month forward rate of RMB per US dollar (in
natural log), and s is the spot rate of RMB per US dollar (in natural log). In addition to interbank
interest rates (i.e., three-month Shibor and Libor), we also consider short-term government bond
yields, China’s three-month government bond yield and the U.S. three-month Treasury Bill rate,
as proxies for interest rates in the computation of excess returns. We then scale the excess return
by the implied volatility derived from the three-month at-the-money exchange rate options and
use this implied Sharpe ratio to measure the ex ante attractiveness of the “China carry” trade.
Alternatively, we also construct a historical Sharpe ratio as the average excess return divided by
its standard deviation. The data on the carry return index, implied volatility and exchange rates
are extracted from Bloomberg, while those on interest rates are obtained from the CEIC and
FRED II databases.
Domestic Credit Condition in China. – Our aggregate-level analysis on China’s domestic
credit condition focus on two distinctive elements: shadow banking credit and traditional bank
lending credit. Following the conventional practice in the literature (e.g., Ehlers, Kong and Zhu,
2018), we measure the provision of shadow banking credit in China as the total amount of newly
extended entrusted and trust loans. As such, the extension of traditional bank lending credit is
proxied by the total amount of newly increased aggregate financing after subtracting the amount
of shadow banking credit. The monthly data on newly increased entrusted loans, trust loans and
aggregate financing are obtained from the CEIC dataset. Similar to the log carry return variable,
we apply the HP filter to log domestic credit to remove its trend component and use its cyclical
15
component in our VAR analysis below.
Control Variables. – One group of covariates included in the regression consists of trade-
related taxes at the HS 4-digit product level, including export tax rebate rates, consumption tax
rates, value added tax (VAT) rates and import tariff rates. We obtain the first three types of tax
rates from China’s State Administration of Taxation (CAST) and the import tariff rates from the
World Integrated Trade Solution (WITS).
As for other control variables, we obtain the VIX implied volatility index from the FRED
II database, Real GDP growth rate for G7 economies and international commodity price inflation
from the IMF’s International Financial Statistics (IFS). Detailed variable definitions and data
sources as well as summary statistics are provided in the appendix.
4. Empirical Results
Prior to formal regression analyses, we provide some suggestive evidence for the disguised
carry trade in China’s goods trade by eyeballing the relationship between export volumes and
carry return. In Panel (a) of Figure 3, we plot the real export volume for electronic integrated
circuit, a high value-to-weight product typically used in the disguised carry trade, alongside the
time path of log carry return index. The time path of the real export volume for corn, a low
value-to-weight product, is graphed in panel (b), together with that of log carry return. A salient
feature is that the export volume of electronic integrated circuit exhibits a strong positive
comovement with the carry return, with a correlation coefficient of 0.82, while that of corn
seems unrelated to the carry return at all. Another interesting observation is that, despite
continuous rise in the carry return, there was a sharp decline in the export volume of electronic
integrated circuit following the launch of the joint crackdown campaign as highlighted by gray-
shaded areas in both charts. In what follows, we shall formally test for the disguised carry trade
16
with China’s goods trade data and investigate its potential macro implication for China’s
domestic credit conditions.
4.1. Uncovering Disguised Carry Trade
Table 1 reports estimation results from Equation (1).6 As shown in Column (1), consistent
with our conjecture, the estimated coefficient on the interaction term between log carry return
and value-to-weight ratio is positive and statistically significant at the 1% level. That is, a rise in
carry trade return is associated with a significantly larger increase in exports for products with
higher value-to-weight ratios. Furthermore, this differential export response to carry return is
also economically sizable. Consider again the two products examined in Figure 3 – electronic
integrated circuit and corn. The former has a (standardized) value-to-weight ratio of 3.744 while
the latter has a (standardized) value-to-weight ratio of -0.209. Given a one-standard-deviation
increase in log carry return over a one-month period, the monthly exports of electronic integrated
circuit would grow faster than that of corn by 3.91 percentage points.
As for other control variables, the estimated coefficient on the interaction term between
log VIX and value-to-weight ratio is significantly negative, suggesting that subdued risks in
global markets tend to boost exports of higher value-to-weight products more. This is consistent
with our hypothesis of carry trade under the cover of exporting high value-to-weight products. In
addition, an increase in international commodity price inflation tends to reduce the export growth
of higher value-to-weight-ratio products significantly more, while exports rebate rate and the
interaction between world economic growth and product’s value-to-weight ratio are not
statistically significant.
One potential concern about the value-to-weight ratio variable is that it may reflect the
6 Pretests for stationary were conducted for key variables in our baseline models, such as log real export volumes, log
real import volumes, and the interaction between the HP filtered log carry return index and value-to-weight ratio.
Given the unbalanced panel structure of our data, we applied Fisher-type panel unit root tests and found all these series
to be stationary. Unit root test results are not reported for the sake of brevity but available upon request.
17
degree of financial constraints associated with products’ manufacturing process rather than its
cost effectiveness in trading. To address this issue, in the next three columns of Table 1, we
further control for the interaction of log carry return with proxies for sector-level financial
constraints commonly used in the literature, including dependence on external financing, asset
tangibility and physical capital intensity. Adding these extra interaction terms does not affect our
main results. We still find significantly positive interaction effect between log carry return and
product’s value-to-weight ratio in all three columns.
Next, we conduct a battery of robustness checks in Table 2. First, we use producer price
index (PPI) adjusted real export volumes as the dependent variable and check if our results
would be sensitive to this alternative measure of real export volumes. As shown in Column (1),
the estimated coefficient on the interaction between log carry return and value-to-weight ratio
remains significantly positive and quantitatively similar to the one from the baseline regression.
Our second set of robustness checks is to see how our results hold when the carry return is
measured in different ways. Columns (2) and (3) measure the carry return by the implied Sharpe
ratios constructed using short-term interbank rates and government bond yields, respectively,
while Columns (4) and (5) use their respective historical Sharpe ratios as proxies for the carry
return. No matter which carry return measure is used, we always find positive and significant
coefficients on the interaction terms with product’s value-to-weight ratio. Overall, our results
deliver a consistent message – increase in carry returns are associated with faster export growth
of higher value-to-weight products.
Finally, we further explore potential heterogeneity in our results by splitting the entire
sample into respectively high and low value-to-weight ratio product subsamples, according to
whether a product’s value-to-weight ratio exceeds the sample median or not. The first two
columns of Table 3 re-estimate Equation (1) separately for the low value-to-weight ratio (i.e.,
cost-inefficient) products and high value-to-weight ratio (i.e., cost-efficient) products. As
18
expected, the estimated coefficient on the interaction term between value-to-weight ratio and the
carry return is statistically insignificant in the cost-inefficient (i.e., low value-to-weight ratio)
product subsample (Column (1)), but statistically significant, at the 5% level, and positive in the
subsample of cost-efficient (i.e., high value-to-weight ratio) products (Column (2)).
4.2. The Impact of Government Crackdown Campaign
In this subsection, to provide further evidence for the disguised carry trade via goods trade,
we use the crackdown campaign jointly launched by Chinese regulatory authorities in May 2013
as a natural experiment and assess its impact on the cross-product difference in trade response to
carry return changes. If there were indeed disguised carry trade at work, we would expect
exports of higher value-to-weight ratio products to be more adversely affected by this crackdown
campaign for they are more cost efficient and thus more likely to be utilized in the disguised
carry trade. Consequently, the trade differential as a response to variations in the carry return
would narrow following this joint crackdown.
Column (3) of Table 3 examines the impact of this joint crackdown campaign using the
entire pool of trading goods, with the specification laid out in Equation (2). While the estimated
coefficient on the interaction term, logcr×vwratio, remains statistically significant and positive,
the estimated coefficient on the triple interaction term, logcr×vwratio×postcrack, is significantly
negative at the 1% level. That is, compared to the pre-crackdown period, the real exports of
products with higher value-to-weight ratios have become significantly less responsive to changes
in the carry return during the post-crackdown period.
Furthermore, we also conduct a simple test to gauge the effectiveness of the joint
crackdown campaign in curbing speculative fund inflows through the channel of disguised carry
trade. If the joint crackdown indeed discourages disguised carry trade forcefully, we would
expect the differential export response to vanish in the post-crackdown era. This can be verified
in the context of Equation (2) by testing the null hypothesis that the coefficients on the first two
19
interaction terms add up to zero. We report the test statistics, along with their p-values, in the
bottom two rows of Table 3. It turns out that there is no significant cross-product difference in
export responses to changes in carry return during the post-crackdown period, which leads us to
conclude that this government crackdown campaign has been an effective deterrent against
disguised carry trade.
In the last two columns of Table 3, we further examine the impact of joint crackdown on
disguised carry trade in the subsamples of low and high value-to-weight ratio products,
respectively. Similarly, for the group of high value-to-weight ratio (i.e., cost-efficient) products,
we find significantly smaller export differential in the post-crackdown era than in the pre-
crackdown ear. As a matter of fact, there is not much significant difference in export responses
(to carry return changes) across products after the onset of the joint crackdown.
4.3. Additional Evidence from Imports
In this subsection, we offer additional evidence for the disguised carry trade by analyzing
the product-level imports data. As noted in Section 2.2, firms often conduct disguised carry
trades repeatedly by repurchasing (i.e., importing) the same products they exported earlier. If that
is the case, as the carry return fluctuates, we should expect import responses across products to
exhibit similar patterns to those observed in exports data.
Table 4 presents the estimation results from imports regressions where the dependent
variables are log real imports. Given that firms’ importing decisions are potentially subject to
various tax-related considerations, we also control for a tax variable, defined as the sum of
import tariff rate, consumption tax rate and value-added tax rate, in the imports regressions.
Panel A estimates the differential import response to carry return changes (i.e., Equation (1)), and
Panel B examines the impact of the joint crackdown campaign on import differential (i.e.,
Equation (2)). For each panel, we start our estimation with the sample of all products in Column
(1) and then re-estimate our model specifications for the low value-to-weight ratio (i.e., cost-
20
inefficient) and high value-to-weight ratio (i.e., cost-efficient) subgroups separately in Columns
(2) and (3).
The evidence from the imports data is generally in line with our hypothesis of disguised
carry trade. In Panel A, the coefficient on the interaction term between log carry return index and
value-to-weight ratio is significantly positive in the full sample of all products (Column (A1)).
After splitting the full sample into the low and high value-to-weight product subsamples
(Columns (A2) and (A3) respectively), only among those cost-efficient (i.e., high value-to-
weight ratio) products, can we find significantly positive response in import volumes to carry
return increases.
In Panel B, while there is significant evidence for the dampening effect of the joint
crackdown campaign on disguised carry trade for imported goods overall (Column (B1)), this
result is mainly driven by goods with high value-to-weight ratios as the estimated coefficient on
the triple interaction terms in only statistically significant and negative in the subsample of high
value-to-weight products (Column (B3)) but not so in the low value-to-weight product
subsample (Column (B2)). Moreover, we also report the F-statistics and their p-values in the last
two rows of Panel B, which suggests that the relationship between import volumes of high value-
to-weight products and carry returns also disappeared in the post-crackdown period.
In a nutshell, our results from the imports data provide further supportive evidence that
there has been disguised carry trade via good trade at work in China and that the subsequent
crackdown campaign jointly launched by several government agencies indeed restrained firms
from engaging in such disguised carry trade activities.
4.4. Macro Implications for Domestic Credit Conditions
Having discovered the trace of disguised carry trade via goods trade in Chinese micro-
level trade data, we then proceed to ask how such disguised carry trades would affect the
domestic credit conditions in China. Based on China’s macro-level data, here we conduct VAR
21
exercises to case some light on this question.
Prior to the VAR model estimation, formal lag selection procedures, including Akaike's
information criterion (AIC), final prediction error (FPE), Hannan and Quinn information
criterion (HQ) and Schwarz information criterion (SIC), all suggest two lags. Further, the
Lagrange multiplier test for autocorrelation in the residuals of the VAR also confirms that a
model with two lags eliminates all serial correlation in the residuals. Therefore, we specify the
VAR model with two lags. In addition, a stable VAR model requires the eigenvalues to be less
than one and the formal test confirms that all the eigenvalues lie inside the unit circle.
Next, we estimate the reduced-form VAR model with the least-squares method and then
use the resulting estimates to construct the structural VAR representation of the model. Figure 4
presents the impulse response functions (IRF) from our trivariate recursive VAR model over a
horizon of 12 months, along with the bootstrapped 95% confidence intervals based on 1000
replications. To save space, we present key IRF graphs related to the potential transmission
mechanism. Panel (a) considers the impact of a shock to the “China carry” return and examines
how it influences the shadow banking credit condition in China through the channel of disguised
carry trade. The left graph of Panel (a) shows that a positive carry return shock leads to a
widening gap in export volumes between high and low value-to-weight ratio products between
months 2 and 4, with this impact being statistically significant between months 2 and 3. This
corroborates our previous findings based on micro-level trade data that export volumes of high
value-to-weight ratio products are more responsive to carry return variations, relative to those of
low value-to-weight ratio products. This impact reaches a maximum response of 0.039 at month
3. When measured against the sample average of 0.0013 for the HP filtered log export
differential (logexpgap), a one standard deviation positive shock to the carry return entails an
increase in logexpgap to around 0.0403 (i.e., rising by 0.039). That is, given a one-standard-
deviation carry return shock, the export volume of high value-to-weight ratio products, relative
22
to that of low value-to-weight ratio products, rises above its trend by over 4%, as compared to
0.1% in absence of such a positive carry return shock.
The right graph of Panel (a) shows that an increase in logexpgap, interpreted as an
expansion of the scale of disguised carry trade, raises the shadow banking credit within the first
four months, with a maximum and statistically significant response reached at month 2. Consider
an increase in logexpgap by the same magnitude of 0.039 as before. This would trigger a
temporary yet sizable expansion in shadow banking credit relative to its trend by around 6.1%.
Thus, the conjunction of the two graphs in Panel (a) tells the story of how an increase in the
return to the “long RMB, short USD” carry strategy can potentially move more illicit capital
inflows into Mainland China through the channel of disguised carry trade via goods trade,
eventually fueling the rapid expansion of shadow banking credit in China.
For comparison purpose, in Panel (b), we also explore the potential impact of the same
positive carry shock on the traditional bank lending credit condition in China. While we continue
to find significantly positive responses in logexpgap to a positive carry return shock, the impulse
responses of traditional bank lending credit to an increase in logexpgap are statistically
insignificant and quantitatively smaller than those of shadow banking credit.
In Panel (a) of Figure 5 we also graph the fraction of the structural variance of logexpgap
due to carry return shock as well as the fraction of the structural variance of log shadow banking
credit due to disguised carry trade shock. Carry return shocks account for 15%-18% of the
variance of logexpgap at horizons longer than 2 months. Shocks to logexpgap account for about
15% of the variance of the shadow banking credit condition at horizons longer than 2 months. In
contrast, as shown in Panel (b) of Figure 5, while carry return shocks still account for 15-16% of
the variance of logexpgap at horizons longer than 2 months, shocks to logexpgap can account for
less than 5% of the variance of the traditional bank lending credit at horizons longer than 2
months. These results further highlight the importance of disguised carry trade as a key
23
transmitter of carry return shocks to China’s domestic credit environment and confirm that fund
flows through disguised carry trades tend to have a more pronounced boosting effect on shadow
banking credit than on traditional bank lending credit.
To sum up, our macro-level evidence from the VAR exercises shows that, during the post-
GFC period, the rise in the “China carry” return induces a surge in disguised carry trade via
goods trade, which smuggled speculative funds into China’s financial system, fueling the rapid
expansion of shadow banking credit in China.
5. Conclusions
In this paper, we examine a new channel – disguised carry trade via goods trade – through
which global liquidity shocks can be transmitted to an emerging economy, despite its tight
controls on cross-border financial flows. To empirically identify such a channel, we resort to
China’s experience during the post-GFC era and exploit product-level variations in cost
efficiency and a unique policy shock. Based on China’s monthly product-level trade data over
the period of 2008.9 - 2014.12, we show that export volumes of high value-to-weight ratio
products respond significantly more positively to rising carry trade returns as compared to low
value-to-weight ratio products. However, this differential export responses across products are
significantly reduced following Chinese regulatory authorities’ joint crackdown campaign on
illicit capital inflows. Similar results are also obtained from the product-level imports data for the
same period. These findings thus point to the existence of disguised carry trade via goods trade in
China. To analyze further the macro implications of such transmission channel, we also conduct
a VAR analysis using Chinese macro-level trade and financial data. Our results suggest that, in
the face of the increasing return to the “China carry”, capital inflows through disguised carry
trade leads to a significant credit expansion in China’s shadow banking sector but not much in its
traditional bank lending credit.
24
Our study contributes to the growing literature on the international transmission of
liquidity shocks by identifying a new channel that is particularly relevant to countries with tight
capital controls but highly open to trade. We show that, notwithstanding capital controls, global
liquidity shocks can still be propagated to domestic economy through disguised carry trade via
goods trade. Our work is also a complement to the literature on anomalies in international trade
and financial flows as we provide new evidence on how goods trade is utilized as a means of
capital control evasion. Finally, our findings underline the role of trade openness in creating de
facto financial openness.
From the policy perspective, our findings indicate that, despite the imposition of tight
capital controls, openness to international trade has made it increasingly difficult for emerging
market economies to shield themselves from global financial shocks. Moreover, without proper
financial supervision and regulation, capital inflows from this channel often feed risky shadow
banking activities. To better safeguard domestic financial stability, EM economies should put
more effort in increasing the resilience of their financial systems to the ebb and flow of global
financial conditions. In particular, instead of relying solely on restrictions of cross-border capital
flows, policymakers can implement appropriate macroprudential measures, strengthen financial
regulations, and build sufficient policy buffers to guard against volatile capital flows.
25
Appendices
Table A1. Variable Definitions and Sources
Variable Definition Source
logexp ln (exports deflated by the CPI) CGAC, CEIC
logexp2 ln (exports deflated by the PPI) CGAC, CEIC
logimp ln (imports deflated by the CPI) CGAC, CEIC
logcr ln(CNY carry return index), HP filtered Bloomberg
isr_ibr Implied Sharpe ratio, computed as the interbank-rate-
based excess return, scaled by the implied volatility
derived from 3-month at-the-money exchange rate
options
Bloomberg, CEIC
hsr_ibr Historical Sharpe ratio, computed as the mean of the
interbank-rate-based excess return, scaled by its
standard deviation
Bloomberg, CEIC
isr_gby Implied Sharpe ratio, computed as the government-
bond-yield-based excess return, scaled by the implied
volatility derived from 3-month at-the-money
exchange rate options
Bloomberg, CEIC
hsr_gby Historical Sharpe ratio, computed as the mean of
excess return, scaled by its standard deviation
Bloomberg, CEIC
logvix ln(VIX implied volatility index) FRED II
vwratio Standardized value-to-weight ratio in 2005 BACI
rebate Export rebate rate CAST
tax Tariff rate + consumption tax rate + VAT rate CAST, WITS
efd Sector’s external finance dependence Manova (2013)
asstan Sector’s asset tangibility Manova (2013)
phycap Sector’s physical capital intensity Manova (2013)
postcrack Dummy variable for the post-crackdown period Authors’ own calculation
infcom Percentage change in commodity price IFS
wrgdpg Real GDP growth of G7 economies IFS
logexpgap The natural log of difference in real exports between
high and low value-to-weight products, HP filtered
CGAC, CEIC
shadow The flow of shadow credit, measured as the sum of
newly increased entrusted and trust loans, HP filtered
CEIC
nonshadow The flow of non-shadow credit, measured as the
difference between the total amount of newly
increased aggregate finance and the flow of non-
shadow credit, HP filtered
CEIC
26
Table A2. Summary Statistics
Variable Mean S.D. Min. Max.
logexp 4.152 1.954 -1.190 9.344
logexp2 4.071 1.940 -1.164 9.215
logimp 4.758 2.146 -1.241 10.985
logcr 0.004 0.015 -0.022 0.041
isr_ior 9.111 5.370 -0.034 21.905
hsr_ior 32.941 28.857 0.479 133.217
isr_tb 13.849 7.386 2.027 32.032
isr_tb 20.946 14.243 1.186 57.837
logvix 3.019 0.397 2.446 4.137
vwratio 0.025 0.119 0.00005 1.636
rebate 9.993 5.838 0 17
tax 26.011 17.032 13 252
efd 0.253 0.330 -0.451 1.140
asstan 0.304 0.137 0.075 0.671
phycap 0.071 0.037 0.018 0.196
infcom -0.531 5.818 -23.682 11.007
wrgdpg 0.752 2.128 -3.918 2.818
logexpgap -0.012 0.142 -0.476 0.221
shadow -0.883 105.832 -197.987 371.811
nonshadow 16.098 448.119 -877.785 1332.050
27
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Figure 1. Evolution of Interest Rates, Spot Exchange Rate and Carry Return Index
Notes: This sample period used in this figure spans from September 2008 to December 2014.
The left axis indicates the ranges of CNY/USD spot exchange rate, 3-month Shanghai interbank
offer-rate (Shibor), 3-month USD London interbank offer-rate (Libor). The right axis indicates
the range of Bloomberg’s CNY carry return index (in natural log).
4.55
4.6
4.65
4.7
4.75
4.8
4.85
0
1
2
3
4
5
6
7
8
200
8m
9
200
8m
12
200
9m
3
200
9m
6
200
9m
9
200
9m
12
201
0m
3
201
0m
6
201
0m
9
201
0m
12
201
1m
3
201
1m
6
201
1m
9
201
1m
12
201
2m
3
201
2m
6
201
2m
9
201
2m
12
201
3m
3
201
3m
6
201
3m
9
201
3m
12
201
4m
3
201
4m
6
201
4m
9
201
4m
12
CNY/USD Spot Shibor3m Libor3m log(carry)
31
Figure 2. An Illustrative Example for Disguising Carry Trade via Goods Trade
Notes: This illustrative example is constructed using market information for the 3-month period
between June and September 2011. The RMB/USD spot exchange rates in June 2011 and
September 2011 were 6.475 and 6.390, respectively. The cost of borrowing the USD is assumed
to be 2.25%, 200 basis points above the 3-month USD Libor (0.25%) in June 2011. The interest
rate of holding the 3-month RMB deposits is assumed to be the 3-month Shibor (5.48%) in June
2011. Ignoring transaction costs involved, the gross profit from this activity is about US$ 21,559
= US$ 1,000,000*[6.475*(1+5.48%/4)/6.39 – (1+2.25%/4)].
Mainland
Bank
Mainland
Firm
Hong Kong
Partner
Hong Kong
Bank
Make a payment of US$ 1mn to Mainland
firm for those exported goods
Co
nvert th
e pro
ceeds o
f
US
$ 1
mn
to R
MB
6.4
75
mn
@ sp
ot exch
ang
e rate o
f
6.4
75
and
dep
osit it fo
r 3
mo
nth
s @ 5
.48
%
Convert the RMB 6.5637mn into
US$ 1.0272 mn @ spot exchange rate
of 6.39 and make payment of
US$ 1.0056mn for re-importing goods
Exten
d a
3-m
onth
loan
of
US
$ 1
mn
@ 2
.25%
Rep
ay
the
loa
n o
f
US
$ 1
.00
56
mn
Rec
eive
pri
nci
pa
l p
lus
inte
rest
of
RM
B 6
.56
37
mn
and
con
vert
it b
ack
to
US$
1.0
272
mn
32
Figure 3. Exports and Carry Return
(a) Electric Integrated Circuit
(b) Corn
Notes: The solid line denotes log carry return index from Bloomberg. The dashed line denotes
the real export volumes. The shaded area indicates the post-crackdown period since May 2013.
33
Figure 4. Macroeconomic Implications: Impulse Responses
(a) Transmission of Shocks to Shadow Banking Credit
Response of logexpgap to logcr Response of logshadow to logexpgap
-.10
-.05
.00
.05
.10
.15
1 2 3 4 5 6 7 8 9 10 11 12
Response of LOGEXPRATIO to LOGCARRY
-.4
-.2
.0
.2
.4
.6
.8
1 2 3 4 5 6 7 8 9 10 11 12
Response of LOGSHADOW to LOGEXPRATIO
(b) Transmission of Shocks to Traditional Bank Lending Credit
Response of logexpgap to logcr Response of lognonshadow to logexpgap
-.10
-.05
.00
.05
.10
.15
1 2 3 4 5 6 7 8 9 10 11 12
Response of LOGEXPRATIO to LOGCARRY
-.4
-.2
.0
.2
.4
.6
1 2 3 4 5 6 7 8 9 10 11 12
Response of LOGNONSHADOW to LOGEXPRATIO
Notes: This figure presents key panels from the impulse response functions of the trivariate VAR
illustrating the transmission of a shock to logged carry return to shadow banking (traditional bank
lending) credit, via the change in log real exports of cost-efficient products relative to those of
cost-inefficient ones. The red dashed lines represent the bootstrapped 95% confidence intervals,
based on 1000 replications.
34
Figure 5. Macroeconomic Implications: Variance Decomposition
(a) Transmission of Shocks to Shadow Banking Credit
(b) Transmission of Shocks to Traditional Bank Lending Credit
Notes: This figure presents key panels from the variance decomposition of the trivariate VAR
illustrating the transmission of a shock to logged carry return to shadow banking (traditional
bank lending) credit, via the change in log real exports of cost-efficient products relative to
those of cost-inefficient ones.
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12
Percent LOGEXPGAP variance due to LOGCR
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12
Percent LOGSHADOW variance due to LOGEXPGAP
Variance Decomposition using Cholesky (d.f. adjusted) Factors
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12
Percent LOGEXPGAP variance due to LOGCR
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12
Percent LOGNONSHADOW variance due to LOGEXPGAP
Variance Decomposition using Cholesky (d.f. adjusted) Factors
35
Table 1. Baseline Regression Results
This table reports the results from baseline panel regressions over the period from September
2008 to December 2014. The dependent variable in each column is the natural log of monthly
real exports (deflated by CPI) for products at the 4-digit HS level. logcr is the HP filtered log
carry return index, vwratio is the value-to-weight ratio for each product in 2005, rebate is the
export rebate rate for each product, logvix is the natural log of VIX index, wrgdpg is the real
GDP growth rate of G7 countries, and infcom is the international commodity price inflation. All
regressions include a constant term, product and time fixed effects. Standard errors clustered at
the product level are reported in parentheses. ***, **, and * indicate the significance at the 1%,
5%, and 10% level, respectively.
Dependent variable:
ln(real exports) (1) (2) (3) (4)
logcr × vwratio 0.659*** 0.548*** 0.539** 0.529**
(0.188) (0.210) (0.225) (0.212)
rebate 0.003 0.001 0.002 0.002
(0.005) (0.006) (0.006) (0.006)
logvix × vwratio -0.156*** -0.151*** -0.151*** -0.151***
(0.029) (0.027) (0.027) (0.027)
wrgdpg × vwratio 0.000 -0.001 -0.001 -0.001
(0.004) (0.004) (0.004) (0.004)
infcom × vwratio -0.003*** -0.003*** -0.003*** -0.003***
(0.001) (0.001) (0.001) (0.001)
logcr × efd 0.628
(2.123)
logcr × asstan -2.295
(5.806)
logcr × phycap -17.828
(19.414)
Observations 17,536 14,337 14,337 14,337
R-squared 0.956 0.960 0.960 0.960
Product FE yes yes yes yes
Month FE yes yes yes yes
36
Table 2. Robustness Checks: Alternative Measures
This table reports the results from robustness checks with alternative measures. The dependent
variable in each column is the natural log of monthly real exports of products at the 4-digit HS
level. Column (1) uses PPI-deflated real exports as the dependent variable and measures carry
return (carry) with HP filtered log carry return index (logcr). Columns (2) and (3) measure carry
return (carry) with the implied Sharpe ratios based on interbank rates (isr_ibr) and government
bond yields (isr_gby), respectively. Columns (4) and (5) measure carry return (carry) with the
historical Sharpe ratios based on interbank rates (hsr_ibr) and government bond yield (hsr_gby),
respectively. All regressions include a constant term, product and time fixed effects. Standard
errors clustered at product level are reported in parentheses. ***, **, and * indicate the
significance at the 1%, 5%, and 10% level, respectively.
Dependent variable:
ln(real exports) (1) (2) (3) (4) (5)
carry × vwratio 0.646*** 0.008*** 0.005*** 0.001*** 0.001***
(0.188) (0.003) (0.001) (0.000) (0.000)
rebate 0.003 0.003 0.003 0.003 0.003
(0.005) (0.005) (0.005) (0.005) (0.005)
logvix × vwratio -0.156*** -0.073*** -0.085*** -0.100*** -0.142***
(0.029) (0.008) (0.011) (0.028) (0.031)
wrgdpg × vwratio 0.000 -0.003 -0.002 -0.002 -0.003
(0.004) (0.004) (0.003) (0.004) (0.004)
infcom × vwratio -0.003*** -0.002*** -0.002*** -0.002*** -0.003***
(0.001) (0.001) (0.000) (0.001) (0.001)
Observations 17,536 17,536 17,536 17,536 17,536
R-squared 0.956 0.956 0.956 0.956 0.956
Product FE yes yes yes yes yes
Month FE yes yes yes yes yes
37
Table 3. Heterogeneity and the Impact of Joint Crackdown Campaign
This table further explores the heterogeneity in trade responses to carry return and assesses the
impact of the joint crackdown campaign on the disguised carry trade activities. The dependent
variable in each column is the natural log of monthly real exports (deflated by CPI) of products
at the 4-digit HS level. Column (1) is estimated using the cost-inefficient products whose value-
to-weight ratios are below the sample median. Column (2) is estimated using the cost-efficient
products whose value-to-weight ratios are above the sample median. Column (3) further includes
a triple interaction term (logcr×vwratio×postcrack) to examine the impact of the joint
crackdown campaign. Columns (4) and (5) assess the impact of the joint crackdown campaign
for cost-inefficient and cost-efficient product groups, respectively. In Columns (3) to (5), F-
statistics and p-values are provided for the null of no differential response in real exports to carry
return across products in the post-crackdown era. All regressions include a constant term,
product and time fixed effects. Standard errors clustered at the product level are reported in
parentheses. ***, **, and * indicate the significance at the 1%, 5%, and 10% level, respectively.
Dependent variable:
ln(real exports)
(1)
cost-
inefficient
(2)
cost-
efficient
(3)
full
sample
(4)
cost-
inefficient
(5)
cost-
efficient
logcr × vwratio 0.676 0.738** 1.612* -0.516 2.193**
(0.736) (0.298) (0.821) (0.807) (1.104)
logcr × vwratio × postcrack -1.393*** 0.239 -1.960***
(0.378) (1.553) (0.509)
rebate 0.004 -0.001 0.003 0.004 -0.001
(0.007) (0.007) (0.005) (0.007) (0.007)
logvix × vwratio -0.105*** -0.208*** -0.161*** -0.088** -0.218***
(0.039) (0.038) (0.038) (0.034) (0.052)
wrgdpg × vwratio -0.004 0.001 0.003* -0.008* 0.005**
(0.005) (0.005) (0.002) (0.004) (0.002)
infcom × vwratio -0.001 -0.005*** -0.003*** -0.000 -0.005***
(0.001) (0.001) (0.001) (0.001) (0.001)
vwratio × postcrack -0.002 0.049 -0.009
(0.031) (0.056) (0.045)
Observations 8,699 8,837 17,536 8,699 8,837
R-squared 0.932 0.967 0.956 0.932 0.967
Product FE yes yes yes yes yes
Month FE yes yes yes yes yes
H0: β + δ = 0 0.13 0.04 0.09
(p-value) (0.718) (0.836) (0.763)
38
Table 4. Additional Evidence from Imports
This table reports the estimates from imports regressions. The dependent variable is log monthly real import volume, and the HP
filtered log carry return index is used to measure carry return. Panel A tests the effect of carry returns. Panel B examines the impact of
the crackdown. In each panel, Column (1) uses the full sample, and Columns (2) and (3) use the cost-inefficient and cost-efficient
subsamples. All regressions include constant terms, product and time fixed effects. Standard errors clustered at product level are in
parentheses. In Panel B, F-statistics and p-values are provided for the null of no differential response in real imports to carry return
across products in the post-crackdown era. ***, **, and * indicate the significance at the 1%, 5%, and 10% level, respectively.
Dependent variable: Panel A: The Response to Carry Return Panel B: The Impact of Crackdown
ln(real imports) (1)
full sample
(2)
cost-inefficient
(3)
cost-efficient
(1)
full sample
(2)
cost-inefficient
(3)
cost-efficient
logcr × vwratio 1.339* -0.837 2.248** 1.313** 0.427 1.962**
(0.780) (1.473) (0.934) (0.659) (1.048) (0.940)
logcr × vwratio × postcrack -2.036** 1.100 -4.215**
(0.914) (1.151) (1.674)
tax -0.008*** -0.008*** 0.037 -0.008*** -0.008*** 0.038
(0.001) (0.001) (0.097) (0.001) (0.001) (0.097)
logvix × vwratio 0.091** -0.065 0.034 0.105** -0.092 0.067
(0.046) (0.093) (0.046) (0.042) (0.087) (0.043)
wrgdpg × vwratio -0.006 -0.016 -0.012* -0.005 -0.013 -0.011*
(0.005) (0.015) (0.006) (0.005) (0.016) (0.006)
infcom × vwratio 0.002 -0.003 -0.000 0.002* -0.004 0.001
(0.001) (0.004) (0.002) (0.001) (0.004) (0.001)
vwratio × postcrack 0.063* -0.093* 0.141***
(0.033) (0.049) (0.048)
Observations 10,916 6,317 4,599 10,916 6,317 4,599
R-squared 0.939 0.919 0.963 0.939 0.920 0.963
Product FE yes yes yes yes yes yes
Month FE yes yes yes yes yes yes
H0: β + δ = 0 0.73 1.08 1.91
(p-value) (0.395) (0.302) (0.173)