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: shulin@cuhk.edu.hk. b Research Institute of Industrial Securities Co., LTD.,15/F Industrial Securities Building, No. 36

Changliu Road, Shanghai, P. R. China. Email: jcxiao@think-macro.net. 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: haichunye@gmail.com.

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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

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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

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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)

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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

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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

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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.

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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

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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

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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.

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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

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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.

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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.

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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.

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(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)