HIAS-E-98

Reserves and Risk: Evidence from China
Rasmus Fatum(a) , Takahiro Hattori(b) and Yohei Yamamoto(b)(a) School of Business, University of Alberta, Edmonton, Alberta, Canada T6G 2R6(b) Department of Economics, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8601 Japan

May, 2020

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Reserves and Risk: Evidence from China

This version: 15 May 2020

Rasmus Fatum
School of Business
University of Alberta
Edmonton, Alberta
Canada, T6G 2R6
Email: rasmus.fatum@ualberta.ca

Takahiro Hattori
Department of Economics
Hitotsubashi University
2-1 Naka, Kunitachi
Tokyo 186-8601, Japan
Email: hattori0819@gmail.com

Yohei Yamamoto
Department of Economics
Hitotsubashi University
2-1 Naka, Kunitachi
Tokyo 186-8601, Japan
Email: yohei.yamamoto@econ.hit-u.ac.jp
Abstract:
China holds more international reserves than any other country. We consider if theChinese accumulation of reserves is associated with unintended consequences in the form of
increased private sector risk taking. Using sovereign credit default swap spreads and stock index
prices as indicators of risk taking we provide evidence to suggest that as reserve holdings increase,so does the willingness of the private sector to take on more risk. This is an important finding that
adds credence to the suggestion that insurance through costly reserves, to be used in the event of acrisis, may lead to private sector actions that in and of themselves make it more likely that thisinsurance will be used.
Key words: International reserves; risk taking; China.
JEL Classifications: F31, G15.
* Corresponding author. Fatum is also Research Associate at the Federal Reserve Bank of Dallas
and member of the Economic Policy Research Unit (EPRU) at the University of Copenhagen. This
research was carried out in part while Fatum was visiting Hitotsubashi Institute for Advanced Study(HIAS). Fatum greatly appreciates the hospitality of HIAS. Fatum gratefully acknowledges
financial support from a Foote Professorship in International Business and Yamamoto gratefully
acknowledges financial support from MEXT Grant-in-Aid for Scientific Research No. 17H00985.
We thank James Yetman for help with the reserves data.

1.

Introduction

We consider if the Chinese accumulation of international reserves is associated with
unintended consequences in the form of increased private sector risk taking. Usingsovereign credit default swap spreads (CDS) and stock index prices as indicators of risktaking we provide evidence to suggest that as reserve holdings increase, so does thewillingness of the private sector to take on more risk. This is an important finding that adds
credence to the suggestion that costly insurance in the form of holding reserves to be usedin the event of a crisis may lead to private sector actions that in and of themselves make it
more likely that this insurance will be used.
The build-up of international reserves across several emerging economies,
including and most noticeably in the case of China, took off after the Asian Financial Crisis(AFC) and has further accelerated since the Global Financial Crisis (GFC). Generally, anessential rationale underlying accumulation of reserves is the precautionary savings
motive, i.e. the desire to hold liquid insurance, in the form of foreign currency, against
adverse financial market shocks and sudden capital outflows.1 Other key drivers are exportdriven growth in conjunction with varying degrees of managed exchange rate systems
pushing up the level of reserves if foreign exchange intervention operations aimed at
mitigating pressure for the domestic currency to appreciate are undertaken, as well as lessdeveloped domestic financial systems where, for example, capital controls interfere withprivate investments.2

1

See Jeanne and Sandri (2020) for a recent contribution on optimal reserves and precautionary savings.
See ECB (2006) for a useful overview of traditional drivers of reserve accumulation. Cheung and Qian
(2009) suggest keeping up with the Joneses as an alternative driver of reserve accumulation.
22

While the precautionary savings motive may explain part of the observed
accumulation of reserves, several Asian emerging economies hold reserves in excess oflevels typically dictated by models of optimal reserve holdings as well as by rule of thumbguidelines. 3 This may seem surprising, considering that holding reserves is costly. 4
However, the willingness to incur the cost of holding high levels of reserves can berationalized by the expectation that holding substantial reserves may reduce the severity ofa financial crisis, expedite subsequent recovery, and make a financial crisis itself lesslikely.5,6Traditionally, the cost of holding reserves is measured as the spread between the
return on reserves held versus the opportunity cost of reserves held, i.e. the foregone returnon investment in physical or human capital or, simply, the cost of external borrowing.7
However, the cost of holding reserves goes beyond opportunity costs from foregone
returns, or incurred borrowing costs, and includes indirect costs such as sterilization costs,

3

For example, Jeanne (2007) uses a model of optimal reserve levels with an insurance motive to show thatthe build-up of reserves in Asian emerging economies after the AFC can only be explained if the expected
output cost associated with a sudden stop capital account crisis is unrealistically large. Similarly, Jeanne andRancière (2011) show that the build-up of reserves in Asian emerging economies can only be explained
within the confines of their model when expected output cost of a sudden stop crisis is combined with a highdegree of risk aversion. As for rule of thumb guidelines for reserve accumulation, these typically pertain toreserves relative to imports or reserves relative to external short-term debt ratios. While the former suggeststhat the level of reserves should amount to the value of three months of imports, and the latter, known as the
Guidotti-Greenspan rule, suggests that the level of reserves should cover (at least) the external short-term
debt of a country, reserve levels in Asian emerging economies are often in excess also of these rule of thumbguidelines.
4
See Aizenman and Jinjarak (2020) for a recent study of optimal management of reserves aimed at mitigating
the cost of holding reserves. People’s Bank of China (PBC) (2019) addresses reserves management practices
aimed at reducing the cost of holding reserves in the context of China.
5See, for example, Feldstein (1999).
6
Interestingly, there is no consensus on whether a high level of reserves helped facilitate faster post-GFC
recovery. For example, Blanchard, Faruqee, and Klyuev (2009) do not report that more reserves lowered
output declines whereas Dominguez, Hashimoto and Ito (2012) find that higher levels of reserves prior to the
GFC are associated with higher post-GFC output growth.
7
See Baker and Walentin (2001) and Rodrik (2006). Rodrik (2006) refers to the opportunity cost of holding
reserves as the social cost of self-insurance.

3interest rate costs and difficulties in implementing monetary policy.8 Importantly, and thefocus of our study, reserve accumulation may also be associated with an additional andperhaps particularly concerning counterproductive cost from moral-hazard induced private
sector risk taking. If reserves correlate with the ability of the government to provide
insurance in times of financial stress, private sector agents may be willing to take on morerisk knowing that as reserve holdings increase, so does the ability of providing insurance
and private sector bailouts. In doing so, private sector risk taking can increase theprobability of overinvestment and asset bubbles which, in turn, can make a financial crisis
more likely. In other words, while emerging economies accumulate reserves to provide
insurance in the event of a financial crisis, this may have the unintended counterproductive
effect of increasing the likelihood of a financial crisis. If this is the case, rather than theinsurance premium paying for itself, as suggested by Rodrik (2006), the insurance premiumincludes an increase in the probability of a financial crisis and thus that the insurance will
be put to use.
There is some limited evidence to suggest that reserve accumulation may indeed
lead to an increase in private sector risk taking. In an innovative paper, Tong and Wei(2019) use firm-level data on corporate leverage spanning 6610 non-financial companies
across 23 emerging economies to show that higher levels of reserves is systematically andsignificantly related to higher corporate leverage.9 Since their data ends in 2006 they do
not consider the acceleration of the emerging market reserve accumulation that has
8

See ECB (2006) for a detailed discussion of indirect costs associated with holding reserves and Yun (2020)
for an interesting study of a crowding-out effect in the form of reduced commercial bank credit expansion asa result of reserve accumulation. By contrast, see also Yeyati (2008) for a study suggesting that the social
cost of self-insurance via reserves are overstated if reserves reduce default risk.
9
In line with Tong and Wei (2019), Sengupta (2010) uses firm-level data spanning 1500 firms across six
Latin American emerging economies to show that higher levels of reserves are associated with higher levelsof USD denominated corporate debt.

4

occurred since the GFC. In another recent paper, Fatum and Yetman (2020) carry out acountry-specific daily data event study analysis of whether official announcements ofreserves influence risk taking. They use implied volatility of currency options as their focal
measure of risk taking and they consider a sample of 10 economies, emerging as well as
advanced, over the 1999 to 2017 period. Generally, they find little evidence to suggest that
reserve accumulation influences risk taking. However, when they consider other measuresof risk -taking they can no longer consistently reject their null hypothesis of no link
between reserves and risk taking.
We attempt to contribute to the empirical literature on reserves by considering if
reserve accumulation after the GFC is associated with counterproductive costs in the formof increased private sector risk taking. We focus our efforts on China as China isparticularly interesting in this regard for two reasons. First, China holds more reserves thanany other country and has done so since before the GFC.10 The reserve levels of China havebeen consistently excessive well before the GFC and beyond according to most models of
optimal reserve levels as well as according to various rules of thumb guidelines, thereby
making China particularly at risk for adverse and unintended consequences of reserve
accumulation.11 Second, to our knowledge, China is the only emerging economy for which

10

At the end of 2015, China overtook Japan as the country holding the largest amount of reserves in theworld.
11
For example, when Obstfeld, Shambaugh, and Taylor (2010) compare actual levels of reserves topredictions from models of optimal reserves augmented to account for changes in financial openness andfinancial development, they only find systematic and substantial deviations between actual and predicted
reserves for the case of China (and only after 2002 when the pace of reserve accumulation in China increased
markedly). Similarly, reserve levels in China deviate dramatically from what rule of thumb guidelines would
suggest, e.g. since the GFC reserves have consistently been in excess of at least six times the value of threemonths of imports, and reserves have consistently been in excess of more than double the value of external
short term debt. It is noteworthy, however, that Bianchi, Hatchondo, and Martinez (2018), in a study ofreserve accumulation and roll-over risk, find that simulated optimal reserve levels can reach 40% of GDP,
corresponding to roughly double the current Chinese reserves to GDP ratio, indicating that as per this metric
Chinese reserve holdings are not necessarily excessive.

5

survey data on market expectations of reserve announcements exists. Specifically, survey
expectations of Chinese reserve announcements are available from 2008 and onwards. Thisis important as it enables us to consider the surprise component of reserve announcements
rather than having to rely only on either actual announcements or on model-based measuresof expectations to identify the surprise component of a given reserve announcement.12
To address whether reserve accumulation in China is associated with increased risktaking we first carry out monthly frequency time-series estimations to assess the influenceof GDP-normalized reserves separately on two risk taking indicators, Chinese sovereign
credit default (CDS) spreads and Chinese stock market (index) prices. CDS spreads provide
a risk taking measure specifically pertaining to extreme events, i.e. default or restructuringof sovereign debt while stock prices provide a broad measure of risk taking reflectingchanges in market sentiment that are not necessarily related to changes in perception of thelikelihood of extreme events.13 Our full sample spans the July 2001 to October 2019 timeperiod. We pay particular attention to the post-GFC sub-sample. We first estimate our timeseries models using OLS and, subsequently, we consider non-linearities by estimating
threshold models with endogenously defined thresholds according to reserve levels as wellas according to global market uncertainty. Next, we carry out daily data event study
regressions of the effects of reserve announcement surprises on our two risk taking
indicators. Third, we extend our analysis to incorporate tick-level prices from three Chinese
stock indices to perform an intraday analysis of whether reserve announcement surprises
12

It is well-known that failure to disentangle the expected component of an announcement may lead to anunderestimation of the impact of the announcement. See, for example, Fatum and Scholnick (2008).
13
An increase in CDS spreads suggests an increase in demand for, and willingness to pay more for, insurance
and is thus consistent with a decrease in risk taking (and vice versa). Similarly, an increase in stock prices
suggests an increase in demand for, and willingness to pay more for, assets and is thus consistent with anincrease in risk taking (and vice versa). See also Fatum and Yetman (2020) who discuss and employ CDS
spreads and stock market indices in their study of reserve accumulation and risk-taking.

6

influence risk taking as indicated by stock prices over 1-, 2-, and 5-minute windows. Thisis an important extension as it addresses endogeneity stemming from possible omitted
variable concerns pertaining to the lower frequency estimations. Finally, to complement,
and provide context to, our China-focused investigation we also carry out monthly
frequency time-series estimations of the influence of reserve accumulation on risk taking
for five other Asian emerging economies (Indonesia, South Korea, Malaysia, Philippines,
and Thailand).
Overall, our results suggest that as reserve holdings increase, CDS prices decrease
while stock prices increase. This is consistent with the suggestion that an increase in reserve
holdings is associated with an increase in risk taking. When we consider the effects ofreserve accumulation separately across before and after GFC sub-samples, we show that
our full sample results are driven by post-GFC effects. Our threshold estimations further
confirm that reserves matter more for risk taking after the GFC. Daily data as well as
intraday estimations provide additional evidence consistent with the suggestion that reserve
accumulation is systematically and significantly associated with increased risk taking after
the GFC. These are important findings that add credence to the suggestion that additional
insurance in the form of costly reserve accumulation prompts private sector actions thatmake it more likely that the insurance will be used.
The rest of the paper is organized as follows. Section 2 describes the data. Sections
3 and 4 present and discuss the empirical framework and the results, respectively. Section
5 provides extensions and robustness checks. Section 6 concludes.

7

2.

Data

Our data on Chinese reserves consists of date- and time-stamped PBC announcements andspans the July 2001 to October 2019 time-period. The reserve announcements occur at amonthly frequency. Following rapid growth rates from the onset of our sample period
Chinese reserve holdings peaked at almost USD 4,000 billion in June 2014. Subsequently,
reserve holdings gradually declined before stabilizing at around USD 3,000 billion. Figure
1 shows the evolution of Chinese reserves and Table 1 provides descriptive statistics.14
Our data on survey expectations of Chinese reserve announcements is obtained
from Money Market Services (MMS) and Bloomberg News Service. Quarterly survey data
is available from January 2008 and monthly after September 2015. Following Andersen etal. (2003) and others we construct for each reserve announcement the standardized
announcement surprise as the unexpected component of the announcement divided by the
sample standard deviation.15 Figure 2 shows the reserve announcement surprises.16
Our stock market risk taking indicator is the Morgan Stanley Capital International(MSCI) China index and our sovereign CDS risk taking indicator is the five-year Chinese
USD-denominated sovereign CDS spread obtained from Markit Inc. The two risk-indicator
series are depicted at the top of Figure 3 and descriptive statistics provided in Table 1.
We employ as control variables the CNY/USD exchange rate and the MSCI US
stock index. The control variables are displayed at the bottom of Figure 3 and descriptive
14

For the purpose of our empirical analysis we follow Fatum and Yetman (2020) and others in normalizing
reserves by domestic GDP.
15
Let 𝐴𝑡 denote the value of a given reserve announcement on day 𝑡. Let 𝐸𝑡 refer to the median value of thepreceding survey expectations, and let 𝜎̂ denote the sample standard deviation of all reserve announcement
surprise components across the entire sample period. The standardized reserve announcement surprise on
𝐴 −𝐸
day 𝑡 is then defined as 𝑡 𝑡.
𝜎̂
16Note that Figure 2 depicts pre-scaled (i.e. 𝐴𝑡 − 𝐸𝑡 ) surprises. Note also that announcements are typicallymade outside of market hours (59 out of 68) and that the expectation errors are smaller after 2014, the lattersuggesting that Chinese reserve announcements are increasingly predictable.

8statistics provided in Table 1. Additionally, as an indicator of global market uncertainty wemake use of the VIX series.17,18
For the purpose of our intraday assessment of whether reserve accumulationinfluences risk taking we employ tick-level traded prices for three major Chinese stock
market indices, the CSI 300 Index, the Shanghai SE Composite Index, and the Shenzhen
SE Composite Index. The tick-level data series are provided by Tick Data Inc.
To provide a comparison point for our results on China, we extend our analysis toconsider other Asian emerging market economies. Specifically, we consider reserve
accumulation and risk taking in Indonesia, Malaysia, Philippines, South Korea, andThailand.19 Our data on normalized reserves for these countries also span the July 2001 to
October 2019 time-period except for Indonesia where the reserve series start April 2004.
Risk indicator series for the five comparison countries are also constructed from countryspecific MSCI stock price indices and country-specific five-year sovereign CDS spreads
obtained from Markit Inc.20

3.

Econometric Methodology

To assess whether reserve accumulation influences risk taking we first estimate(separately) the following monthly frequency baseline models:17

The Chicago Board Options Exchange (CBOE) Volatility Index (VIX) is an oft-used indicator of global
risk. The VIX is a forward-looking, model-free measure of the near-term (30-day) implied volatility of S&P
500 index options.
18
Risk indicators as well as VIX and control variables are obtained as daily data series. For the purpose of
our monthly frequency estimations (baseline and threshold), monthly series data points are generated fromthe daily data as the difference between previous end of month and current end of month observations.
19
The reserve accumulation of these five economies is also considered in Jeanne (2007).
20
Descriptive statistics on intraday data and comparison country series are available upon request.

9∆𝑆𝑡 = 𝛽0 + 𝛽1 ∆𝑅𝐸𝑆𝑡 + 𝛽2 ∆𝐶𝑁𝑌𝑈𝑆𝐷𝑡 + 𝛽3 ∆𝑈𝑆𝑆𝑡 + 𝑒𝑡

(1)

∆𝐶𝐷𝑆𝑡 = 𝛽0 + 𝛽1 ∆𝑅𝐸𝑆𝑡 + 𝛽2 ∆𝐶𝑁𝑌𝑈𝑆𝐷𝑡 + 𝛽3 ∆𝑈𝑆𝑆𝑡 + 𝑒𝑡(2)

where ∆𝑆𝑡 and ∆𝐶𝐷𝑆𝑡 denote, respectively, the change in the Chinese stock market index
and the CDS spread, i.e. these are our two risk indicators, ∆𝑅𝐸𝑆𝑡 is the change in percent
of normalized Chinese reserve holdings, i.e. this is our focal explanatory variable,∆𝐶𝑁𝑌𝑈𝑆𝐷𝑡 and ∆𝑈𝑆𝑆𝑡 are, respectively, the change in the CNY/USD exchange rate andthe US stock market index, i.e. these are our control variables, and 𝑒𝑡 is the zero-mean error
term. We include control variables to reduce omitted variable endogeneity bias in ourestimates and choose as our controls US stock prices and the CNY/USD exchange rate forboth models.21 The models are estimated using ordinary least squares (OLS) with White(1980) heteroskedasticity-robust standard errors.
To allow for the possibility that reserve accumulation influences risk taking
differently depending on whether reserve levels are relatively high or relatively low, we
extend our analysis to incorporate the non-temporal testing model originally developed byHansen (2000) and estimate the following

𝛽0𝐿 + 𝛽1𝐿 ∆𝑅𝐸𝑆𝑡 + 𝛽2𝐿 ∆𝐶𝑁𝑌𝑈𝑆𝐷𝑡 + 𝛽3𝐿 ∆𝑈𝑆𝑆𝑡 + 𝑒𝑡
∆𝑆𝑡 = { 𝐻
𝛽0 + 𝛽1𝐻 ∆𝑅𝐸𝑆𝑡 + 𝛽2𝐻 ∆𝐶𝑁𝑌𝑈𝑆𝐷𝑡 + 𝛽3𝐻 ∆𝑈𝑆𝑆𝑡 + 𝑒𝑡

21

𝑖𝑓 𝑅𝐸𝑆𝑡 < 𝑞
𝑖𝑓 𝑅𝐸𝑆𝑡 ≥ 𝑞(3)See, for example, Phylaktis and Ravazzolo (2005) and Longstaff, Pan, Pedersen, and Singleton (2011),
respectively, for studies showing the relevance of exchange rates and US stock prices when modeling nonUS stock prices and sovereign CDS spreads, respectively. Moreover, since reserve accumulation is in part areflection of pursuing an active exchange rate management policy, including the exchange rate in ourestimations ensures that the effects of reserve accumulation are not exaggerated from the reserve variableinadvertently serving as an exchange rate proxy variable.

10𝛽 𝐿 + 𝛽1𝐿 ∆𝑅𝐸𝑆𝑡 + 𝛽2𝐿 ∆𝐶𝑁𝑌𝑈𝑆𝐷𝑡 + 𝛽3𝐿 ∆𝑈𝑆𝑆𝑡 + 𝑒𝑡
∆𝐶𝐷𝑆𝑡 = { 0𝐻
𝛽0 + 𝛽1𝐻 ∆𝑅𝐸𝑆𝑡 + 𝛽2𝐻 ∆𝐶𝑁𝑌𝑈𝑆𝐷𝑡 + 𝛽3𝐻 ∆𝑈𝑆𝑆𝑡 + 𝑒𝑡

𝑖𝑓 𝑅𝐸𝑆𝑡 < 𝑞
𝑖𝑓 𝑅𝐸𝑆𝑡 ≥ 𝑞(4)where q is the reserve level threshold value to be estimated by the maximand of thelikelihood ratio statistics over all permissible values. 22 Superscripts L and H denote low
and high reserve levels, respectively. The non-temporal test of Hansen (2000) is similar toa standard temporal parameter change test for a single unknown breakpoint (e.g. Andrews
1993). However, instead of analyzing a temporally-ordered data set, the Hansen (2000)
procedure dictates that we sort our data in a non-temporal fashion according to, in ourcontext, levels of reserve holdings. Doing so allows us to endogenously identify in a nontemporal modeling framework the reserve level, if any, around which the influence ofreserve accumulation on risk taking changes.23
We subsequently extend the threshold analysis by also considering if the influenceof reserve accumulation on risk taking depends on the level of global market uncertainty.
To do so we re-estimate the models described in Equations (3) and (4) with sorting
according to market uncertainty as measured by VIX levels.24
Next, we estimate daily frequency event study versions of the models described in
Equations (1) and (2) with the surprise component of the reserve announcement, denoted
𝑅𝐸𝑆𝑆𝑈𝑅𝑃𝑡 , in place of ∆𝑅𝐸𝑆𝑡 , and all other variables describing the corresponding dailychange on the day of the announcement. We include in these estimations only

22

The permissible threshold values exclude the first and last 1% of the ordered sample.
For completeness, we also employ a non-temporal version of the Bai and Perron (1998) sequential test to
consider a null hypothesis of one threshold against an alternative of two thresholds.
24
See Fatum and Yamamoto (2016) for a recent application of the non-temporal threshold procedure alongwith additional details.
23

11announcement day observations. Importantly, since the PBC does not announce reserve
holdings for a given month until the beginning of the following month (typically on day 7of the following month), and survey expectations pertaining to the announcement of agiven month are formed and released prior to the day of the announcement, theannouncement surprises are pre-determined relative to the risk taking measures. Therefore,
the daily frequency estimations are effectively addressing reverse causality endogeneity
bias in the reserve variable coefficient estimates.
Lastly, we incorporate the intraday tick-level stock price data into our analysis. We
do so by estimating 1-minute, 2-minute, and 5-minute intraday event study versions ofEquation (1) for each of the three Chinese stock market indices considered (the CSI 300
Index, the Shanghai SE Composite Index, and the Shenzhen SE Composite Index). As inour daily frequency event study estimations we use the surprise component of reserveannouncements in place of ∆𝑅𝐸𝑆𝑡 .25 As noted earlier, most Chinese reserveannouncements occur outside of market hours, leaving us with only 9 announcements forwhich intraday windows can be constructed as described. Nevertheless, by assessing theinfluence of announcement surprises pertaining to pre-determined reserve levels acrosswindow lengths sufficiently short that we can reasonably assume that no other relevant
news or events occur, we are effectively addressing both omitted variable and reverse
causality endogeneity concerns.

25

For each of the three stock price indices employed, the event window is set to start at the open price of theminute of the announcement and end at the closing price of the minute corresponding to the given window
length. For example, if the announcement is made at 10:05, the windows start at the open price of 10:05 andthe 1-, 2-, and 5-minute windows end at the closing prices of 10:05, 10:06, and 10:09, respectively.

12

4.

Results

Table 2 shows the results of our full sample baseline regressions. The top panel pertains toestimations of the model described in Equation (1), where the Chinese stock price index is
the risk indicator, and the bottom panel pertains to estimations of the model described in
Equation (2), where the Chinese sovereign CDS is the risk indicator. To provide a nuanced
picture of the influence of reserve accumulation on the risk indicators, we estimate allmodels without control variables included, with only the exchange rate control variable
included, and with both exchange rate and US stock price control variables included. As
the top panel results show, the reserve variable coefficient estimate with respect to the stock
price risk indicator is consistently positive and significant. The addition of the exchange
rate control variable does little to change the magnitude and significance of the reserve
variable coefficient estimate, and while controlling for US stock prices noticeably does,
the reserve variable remains positive and significant at conventional levels, thereby
implying that an increase in Chinese reserve holdings is systematically associated with anincrease in domestic stock prices and thus consistent with more willingness to assume risk.
Similarly, the bottom panel results show that the reserve variable coefficient estimate withrespect to the CDS spread indicator is consistently negative and significant at the 5% levelor higher in all three estimations. This conforms to the suggestion that as reserve levels
increase, investors become less inclined to obtain insurance against sovereign default or
restructuring in reflection of more willingness to assume risk.
Tables 3 and 4 report the results of re-estimating Equations (1) and (2) separately
across three sub-samples spanning, respectively, the pre-GFC period (August 2001 to July2007), the GFC period (August 2007 to December 2008), and the post-GFC period

13

(January 2009 to October 2019). 26 The results pertaining to both risk indicators are
remarkably consistent. Both sets of estimations strongly indicate that reserve accumulation
prior to the GFC did not systematically influence neither Chinese stock prices (first panelof Table 3) nor CDS spreads (first panel of Table 4). Similarly, even though somesignificant effects of reserve accumulation are reported across the reserves only and thereserves and exchange rates only specifications, once we control for US stock prices weagain find no effects of reserves (second panel of Tables 3 and 4). However, when we
consider the post-GFC sample, our full sample results for both risk indicators are repeated(third panel of Tables 3 and 4). Clearly, the sub-sample results suggest that the influenceof reserves on risk is stronger after the GFC. This is an interesting finding that, since reserve
levels at any point before and during the GFC are markedly lower than at any point after
the GFC, might suggest that discernable risk taking effects of reserve accumulation do not
manifest until reserve holdings have reached a given level.
To further consider whether this is the case we turn to threshold estimations. Tables
5A and 5B display the results of estimating Equation (3), i.e. the threshold model where
Chinese stock prices serve as the risk indicator, with reserve levels and VIX levels,
respectively, as the sorting variables. All threshold estimations are carried out separately
across the full sample and across the post-GFC sample. Comparing significance of thereserve variable coefficient estimates across the full and the post-GFC sample, Table 5A
confirms that reserves are particularly influential after the GFC. Noticeably, the table also
shows that for the post-GFC sample reserves matter regardless of whether prevailingreserve levels are high or low. By contrast, this is not the case when considering the full

26

GFC dates are set in accordance with Melvin and Taylor (2009).

14

sample results where the effects of reserve accumulation when prevailing reserve holdings
are relatively low are found to be smaller in magnitude and mostly insignificant compared
to when prevailing reserve holdings are relatively high. Again, since reserve levels at any
point in time after the GFC are higher than at any point in time before the crisis, this isconsistent with the suggestion that perhaps it is not that reserves matter more for risk taking
after the GFC per se, but, rather, that reserves matte more for risk taking when reserve
holdings are already high, as is the case after the GFC.
Table 5B again confirms that reserves matter more after the GFC compared toacross the full sample, but only as far as magnitude of coefficient estimates goes.
Interestingly, the Table 5B results suggest that the influence of reserves on the stock pricerisk indicator is more pronounced when the overall level of uncertainty is high.
Tables 6A and 6B pertain to the threshold model described in Equation (4), i.e. thethreshold model where Chinese CDS spreads are used to indicate risk. The Table 6A results
are similar to the Table 5A results just described, except once we include the US stock
price control variable in our estimations we no longer maintain significance of the reserve
variable regardless of whether we consider the full or the post-GFC sample and regardlessof whether prevailing reserve levels are high or low. The results provided in Table 6B showa (marginally) significant effect of reserves on the CDS spread only for the post-GFC highuncertainty segment, even after controlling for both exchange rates and US stock prices,again suggesting that the influence of reserves on risk is more discernable after the GFC
and when uncertainty is high.
Table 7 shows the results of the daily data event study analysis of the January 2008
to October 2019 period for which reserve announcement surprises can be constructed. The

15

top panel pertains to the daily data event study estimation of the stock price risk indicatorwith reserve announcement surprises in place of ∆𝑅𝐸𝑆𝑡 , i.e. the daily data event study
version of Equation (1). As the panel shows, the reserve variable coefficient estimates are
consistently positive and highly significant, at the 1% level, regardless of whether or not
we control for exchange rates and US stock prices. Even the magnitudes of the three reserve
variable coefficient estimates are very similar. These are strong findings as they after
addressing potential reverse causality endogeneity concerns further confirm that anincrease in reserve holdings is systematically and significantly associated with an increasein the stock price risk indicator.
In complete tandem, the bottom panel results, pertaining to the daily data event
study estimation of the CDS risk indicator and the daily data event study version of
Equation (2), similarly strengthen the evidence that an increase in reserve holdings is
associated with a systematic and significant decrease in the CDS risk indicator, thereby
again supporting the suggestion that reserve accumulation leads to an increase in risktaking.
Finally, our intraday results are reported in Table 8 and pertain to the intraday event
study version of Equation (1) without control variables. While all but one of 9 coefficientestimates are positive, consistent with the suggestion that an increase in reserves is
associated with an increase in stock prices and thus increased willingness to assume risk,
only one of these estimates is significant (the Shanghai SE Composite Index for the 1minute window). As noted earlier, our intraday analysis effectively addresses both omitted
variable and reverse causality endogeneity concerns but is restricted by the very low
number of reserve announcements made during market hours. In that sense the consistency

16

with respect to the sign of the reserve surprise coefficient estimates is quite remarkable, as
is the significant effect of reserves with respect to one of the stock market indices for the
1-minute window.
Overall, our results provide very consistent evidence across monthly frequency
estimations, and across daily and intraday event study estimations, that PBC reserve
accumulation after the GFC is systematically associated with an increase in willingness totake on more stock market risk (as domestic stock prices on average rise in response toreserve accumulation) and more sovereign credit default risk (as prices and thus demand
for sovereign default insurance on average decline in response to reserve accumulation),
thereby supporting the suggestion that reserve accumulation comes at the additional costof an increase in risk taking. Although a very different study with respect to methodology,
data, and time-period focus, our findings are nevertheless consistent with those of Tongand Wei (2019). By contrast, our findings are different from those of Fatum and Yetman(2020) and their lack of systematic evidence that reserve accumulation influences risktaking as per their focal risk measure of implied volatility of currency options. However,
our findings are not at odds with their stock price and CDS spread risk indicator based
results to the extent that for these two measures Fatum and Yetman (2020) do not rejectthat reserve accumulation matters for risk taking.

5.

Extensions and robustness

In this section we consider the influence of reserves on risk for other Asian emerging
economies, address if Chinese reserve accumulation increases domestic market
uncertainty, and analyze separately the post-July 2005 floating CNY regime.

17

First, to provide more context to our investigation we extend our monthly frequency
time-series estimations described in Equations (1) and (2) to consider the influence ofreserve accumulation on risk taking for Indonesia, South Korea, Malaysia, Philippines, andThailand. Tables 9 (domestic stock price as risk indicator) and 10 (local currency sovereign
CDS as risk indicator) present the results. When we make use of the domestic stock pricerisk indicator, we find that the results for Indonesia are particularly similar to those of
China, and when we employ the local currency sovereign CDS risk indicator the results forSouth Korea are particularly similar to those of China. Overall, these results suggest thatwhile we do not find systematic evidence to support that reserve accumulation increases
risk taking to the same extent as in the case of China, we nevertheless find indications that
reserve accumulation has qualitatively similar risk consequences for other Asian emerging
economies and thus that unintended increases in risk taking as a result of holding large
amounts of reserves is not unique to China.
Second, we address if Chinese reserve accumulation increases domestic stock
market uncertainty by estimating equation (1) at the monthly and the daily frequency with
the China ETF volatility index (“Chinese VIX”) as the dependent variable. 27 Using
monthly data our results suggest that an increase in reserve accumulation reduces market
uncertainty when we include either no control variables or only the exchange rate variable.
Once we control for US stock prices the influence of reserves is no longer significant.
When we carry out daily data event study estimations of the effects of reserveannouncement surprises we find no systematic influence of reserves on market uncertainty,

27

The China ETF volatility index (VXFXI) series starts in March 2011 and is available from the Chicago
Board of Trade.

18

regardless of whether or which control variables are included. Overall, these estimations
do not point to a systematic link between reserves and domestic market uncertainty.
Third and final, we redo the monthly frequency analysis on a reduced sample that
excludes the August 2001 to June 2005 period of fixed CNY/USD exchange rates. We do
so to ensure that the absence of significant reserve variable coefficient estimates pre-GFC
is not due to the fixed exchange rate regime that describes most of the pre-GFC period. As
it turns out, our previously described results remain unchanged.28

6.

Conclusion

We consider if the Chinese accumulation of reserves is associated with unintended
consequences in the form of increased private sector risk taking over the 2001 to 2019
period. Using sovereign CDS spreads and stock index prices as indicators of private sector
risk taking we first estimate monthly frequency models of the effects of reserve
accumulation on our risk taking indicators. Our results strongly suggest that as reserve
holdings increase so does the willingness of the private sector to take on more risk. When
we compare the effects of reserves on risk taking across sub-samples, our results show thatthe effects are noticeably more pronounced after the GFC while largely absent prior to andduring the GFC. Our monthly frequency threshold models confirm that reserves matter
more for risk taking after the GFC when reserve levels are consistently high.
We incorporate survey expectations of Chinese reserve announcements to identify
the surprise component of reserve announcements and, in turn, estimate event study models
at both daily and intraday frequencies to assess the effects of reserve surprises on the risk

28

Results pertaining to the VXFXI and the post-June 2005 estimations are available upon request.

19

indicators. Doing so allows us to effectively address omitted variable as well as reverse
causality endogeneity concerns and obtain more precise estimates of the effects of reserve
accumulation. The reserve surprise estimations provide further, and even stronger,
evidence that reserve accumulation is significantly and systematically associated with risk
indicator changes consistent with the suggestion that as reserve holdings increase so does
private sector risk taking.
Our results are important in that they point to an unintended consequence of reserve
accumulation that is not considered in standard studies of reserve accumulation and costsof holding reserves. Moreover, reserve accumulation driving up risk taking is an
unintended consequence that seems particularly counterproductive when a main objectiveof reserve accumulation is to provide insurance in the event of financial crises andincreased risk taking in and of itself makes such crises more likely.
Importantly, our evidence supporting the notion that reserve accumulation isassociated with increased risk taking and potentially costly by making crises more likely
does not in any way suggest that this unintended cost outweighs or off-sets the benefits ofholding large amounts of reserves. Indeed, our results do not call into question the validityof the precautionary savings motive for reserve accumulation or, for that matter, that
reserves for other reasons and via other mechanisms may deter or reduce the probability ofcrises. Rather, our study suggests that more reserves are associated with private sector
willingness to hold more risk which, in and of itself, may attenuate the benefits ofaccumulating reserves.

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22

Figure 1. Reserves: China
4500

4000

(USD billion)

3500

3000
2500

2000
1500

1000
500

0
2001

2003

2005

2007

2009

Source: Bloomberg

23

2011

2013

2015

2017

2019

Figure 2. Reserve Announcement Surprises: China
200
150(USD million)

market open
market closed

100
50
0
-50
-100
-150
2008

2010

2012

2014

2016

Source: Own calculations based on data from Bloomberg

24

2018

Figure 3. Risk Indicators and Control Variables

Five-Year Sovereign CDS Spread: China

Stock Price Index: China
120

300

(HKD)

(bsp)

100

250

80

200

60

150

40

100

20

50

0
2001 2003 2005 2007 2009 2011 2013 2015 2017 201902001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Exchange Rate (CNY/USD)

Stock Price Index: US

9

3500

(USD)
8.5

3000

8

2500

7.5

2000

7

1500

6.5

1000

6

500

5.5
2001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Source: Bloomberg and Markit

25

02001 2003 2005 2007 2009 2011 2013 2015 2017 2019

Table 1. Descriptive Statistics: July 2001 to October 2019

Start of
PeriodEnd ofPeriod

Mean

Max

Min

Standard
Deviation

Reserves: China (USD billion)

195.8

3092.42210.53993.2195.8

1253.8Stock Price Index: China (HKD)

1.9

75.0

53.1

103.9

13.7

22.2

CDS Spread (bps)

67.6

40.8

68.3

245.8

9.8

39.8

CNY/USD Exchange Rate

8.28

7.15

7.10

8.28

6.05

0.79

Stock Price Index: US (USD)

989.9

2891.21506.32891.2

700.7

569.7

Source: Bloomberg and Markit

26

Table 2. Baseline Estimations (Full Sample): Stock Price Index and CDS
Dependent Variable (Risk Indicator): ∆𝑆𝑡
From August 2001 to October 2019
ΔRES
1.25***
1.09***

0.45*( 3.72)( 3.28)( 1.82)-1.87***(-3.50)-1.33***(-3.48)ΔCNYUSD
ΔUSS
Constant
R2
obs

0.97***( 8.93)0.75( 1.58)
0.09
218

0.65
( 1.43)
0.13
218

0.39
( 1.05)0.42
218

Dependent Variable (Risk Indicator): ∆𝐶𝐷𝑆𝑡
From August 2001 to October 2019
ΔRES
-2.87***-2.77***-1.33**(-3.81)(-3.61)(-2.52)

1.17( 1.35)-0.02(-0.03)ΔCNYUSD
ΔUSS
Constant
R2
obs-2.17***(-6.71)0.53( 0.52)
0.10
218

0.59
( 0.59)
0.11
218

1.16
( 1.45)0.42
218

Notes: 1. t statistics in parentheses.
2. ***, **, and * denote significant at the 1%, 5%, and the 10% levels,
respectively.

27

Table 3. Baseline Estimations (Sub-Samples): Stock Price Index
Dependent Variable (Risk Indicator): ∆𝑆𝑡
Pre GFC Sample
From August 2001 to July 2007
ΔRES
-0.48
(-0.97)ΔCNYUSD

-0.40

-0.52

(-0.73)(-1.30)-4.95**-3.66*(-2.46)(-1.75)ΔUSS
Constant
R2
obs

0.81***( 4.74)3.08***( 2.77)
0.02
71

2.33*
( 1.86)
0.07
71

GFC Sample
From August 2007 to December 2008
ΔRES
3.20
4.61***( 1.43)ΔCNYUSD( 2.89)-0.60(-0.27)

8.29*

5.12

( 1.81)( 1.38)ΔUSS
Constant

2.52( 2.68)0.29
71

1.88***( 3.54)3.05( 0.81)
0.22
17

5.86
( 1.63)0.56
17

Post GFC Sample
From January 2009 to October 2019
ΔRES
2.59***
2.23***

1.05***

R2
obs

-2.05
(-0.62)0.11
17( 7.76)ΔCNYUSD( 6.35)-1.96***-1.54(-4.77)(-5.56)ΔUSS
Constant
R2
obs( 2.90)0.73***( 6.51)
1.99***( 4.13)0.31
129

Notes: Same as Table 2.

28

1.87***( 4.10)
0.41
129

0.52
( 1.11)0.54
129

Table 4. Baseline Estimations (Sub-Samples): CDS
Dependent Variable (Risk Indicator): ∆𝐶𝐷𝑆𝑡
Pre GFC Sample
From August 2001 to July 2007
ΔRES
-0.09
-0.09
(-0.39)(-0.38)ΔCNYUSD-0.03(-0.02)

ΔUSS
Constant-0.03(-0.12)-0.65(-0.40)-0.39**(-2.01)-0.23(-0.36)
0.00
71

-0.32
(-0.47)
0.11
71

GFC Sample
From August 2007 to December 2008
ΔRES
-9.06**-10.39**(-2.09)(-2.35)-1.39(-0.51)

R2
obs

-0.22
(-0.37)0.00
71

ΔCNYUSD-7.78(-0.99)ΔUSS
Constant-2.30(-0.39)-3.25***(-6.60)3.78( 0.56)
0.35
17

-1.07
(-0.18)0.72
17

Post GFC Sample
From January 2009 to October 2019
ΔRES
-6.52***-6.30***(-4.77)(-4.35)-2.34*(-1.94)

R2
obs

8.57*
( 1.76)0.32
17

ΔCNYUSD

1.20( 1.33)

ΔUSS
Constant
R2
obs

-0.21
(-0.22)-2.46***(-4.90)-4.48( -2.98)0.25
129

Notes: Same as Table 2.

29

-4.40
( -2.93)
0.26
129

0.10
( 0.08)0.45
129

Table 5A. Stock Price Index and Reserve Amount Thresholds
Dependent Variable (Risk Indicator): ∆𝑆𝑡
Threshold variable: 𝑅𝐸𝑆𝑡−1
ΔRES (High)

From August 2001 to October 2019
3.72***
3.42***
1.19( 6.08)( 5.60)( 1.27)

From January 2009 to October 2019
2.37***
2.52***
1.84**( 4.38)( 4.26)( 2.29)2.88( 1.13)2.15( 1.39)ΔCNYUSD (High)ΔUSS (High)
Constant (High)R2 (High)
obs (High)ΔRES (Low)0.94***( 2.92)-0.70(-0.68)0.46
400.76**( 2.24)ΔCNYUSD (Low)1.56( 1.59)
0.32
62
0.30
( 0.94)-1.15(-0.91)
0.58
36
0.29
( 1.11)-2.67***(-6.64)-1.74***(-4.35)ΔUSS (Low)
Constant (Low)
R2 (Low)
obs (Low)
SupF(1|0)
SupF(2|1)
Threshold1.58( 0.87)0.37( 1.17)-0.93(-1.10)0.48
242.76***( 7.21)-0.53(-0.54)0.50
24
2.27***( 5.87)-0.83(-0.78)0.54
24
1.09***( 2.63)-2.20***(-5.62)-1.68***(-6.48)0.95***( 7.37)
1.30***( 2.59)0.04
177
18.10***
6.40
0.435

1.20***( 2.72)0.19
15523.53***
7.48
0.405

0.91**( 2.22)
0.40
181
13.01
13.60
0.438

Notes: Same as Table 2.

30

2.09
( 1.37)0.76***( 6.27)
2.76***( 4.96)0.31
104
11.34*
4.35
0.447

2.66***( 5.14)0.46
104
18.14**
10.61
0.447

0.99*
( 1.79)
0.58
104
11.18
4.90
0.447

Table 5B. CDS and Reserve Amount Thresholds
Dependent Variable (Risk Indicator): ∆𝑆𝑡
Threshold variable: 𝑉𝐼𝑋𝑡−1
ΔRES (High)

From August 2001 to October 2019
3.38***
1.95***
0.93***( 4.78)( 4.27)( 2.81)

From January 2009 to October 20192.62***
3.84***
2.40***( 7.69)( 5.56)( 3.40)-0.39(-0.22)-7.48**(-2.51)ΔCNYUSD (High)ΔUSS (High)
Constant (High)R2 (High)
obs (High)ΔRES (Low)0.96***( 6.80)-0.74(-0.45)0.50
220.91***( 2.69)ΔCNYUSD (Low)-0.25(-0.32)
0.17
98
0.22
( 0.52)-0.17(-0.27)
0.50
91
0.00
( 0.01)-2.84***(-6.62)-2.05***(-5.97)

ΔUSS (Low)
Constant (Low)
R2 (Low)
obs (Low)
SupF(1|0)
SupF(2|1)
Threshold

0.33( 0.24)0.74***( 3.34)
1.58***( 3.09)
0.34
114
3.04*
( 1.79)2.28( 1.34)0.77
131.57***( 4.47)0.37( 0.26)
0.87
13
0.54
( 1.49)-2.09***(-4.93)-1.63***(-5.73)0.94***( 5.25)0.90*( 1.86)0.05
195
11.29**
4.07
29.15

1.41***( 3.02)
0.24
119
11.99
11.12
17.47

0.88**( 1.95)
0.37
126
10.12
6.55
18.07

Notes: Same as Table 2.

31-6.61***(-4.08)0.73***( 5.99)5.90***( 4.26)
0.24
14
8.00
5.96
11.99

1.37***( 2.95)0.34
115
17.69**
17.48**
14.19

0.11( 0.23)0.48
115
16.69**
8.97
26.05

Table 6A. Stock Price Index and Market Uncertainty Thresholds
Dependent Variable (Risk Indicator): ∆𝐶𝐷𝑆𝑡
Threshold variable: 𝑅𝐸𝑆𝑡−1
ΔRES (High)

From August 2001 to October 2019-8.55***-9.01***-1.83(-4.42)(-4.34)(-1.24)

From January 2009 to October 2019-7.52**-7.50**-1.55(-2.38)(-2.34)(-0.68)-5.27(-1.10)2.23( 0.52)ΔCNYUSD (High)ΔUSS (High)
Constant (High)R2 (High)
obs (High)ΔRES (Low)-3.22***(-6.89)1.66( 0.61)0.31
62-0.76**(-2.24)ΔCNYUSD (Low)0.01( 0.00)
0.32
62
-0.53
(-1.57)1.66( 0.93)
0.58
81
-0.37
(-1.20)2.43***( 3.05)

1.75**( 2.32)

ΔUSS (Low)
Constant (Low)
R2 (Low)
obs (Low)
SupF(1|0)
SupF(2|1)
Threshold-0.03(-0.02)-3.82***(-3.61)
5.54**( 2.04)0.39
25-6.38***(-5.14)5.84*( 1.76)0.43
26-6.06***(-4.40)2.67( 1.00)
0.55
46
-1.24
(-1.08)

1.93**( 2.16)

1.17( 1.36)-0.76***(-3.91)-0.74(-1.07)0.03
15537.29***
5.69
0.405

-0.87
(-1.29)0.10
15538.54***
3.85
0.405

-0.35
(-0.60)0.27
13632.91**
12.63
0.393

Notes: Same as Table 2.

32

-2.74
(-0.84)-1.40***(-3.71)-7.15***(-4.02)0.26
10319.40*
18.18**
0.447-7.20***(-3.88)0.27
102
20.89*
21.43***
0.400

-0.60
(-0.41)
0.33
82
9.76
8.21
0.399

Table 6B. CDS and Market Uncertainty Thresholds
Dependent Variable (Risk Indicator): ∆𝐶𝐷𝑆𝑡
Threshold variable: 𝑉𝐼𝑋𝑡−1
ΔRES (High)

From August 2001 to October 2019-12.57*** -12.11***-5.85(-5.34)(-4.54)(-1.52)

From January 2009 to October 2019-13.31*** -14.81***-5.62*(-3.26)(-3.62)(-1.79)29.03( 0.87)30.89*( 1.75)ΔCNYUSD(High)ΔUSS (High)
Constant (High)R2 (High)
obs (High)ΔRES (Low)-2.46**(-2.19)7.19( 1.14)0.48
22-1.25***(-3.18)ΔCNYUSD (Low)8.28( 1.33)0.50
22-1.10***(-2.86)

5.52( 0.89)
0.64
22
-0.53
(-1.62)

1.46*( 1.72)0.47( 0.68)ΔUSS (Low)
Constant (Low)
R2 (Low)
obs (Low)
SupF(1|0)
SupF(2|1)
Threshold30.64( 1.43)-4.76***(-3.10)-9.30(-0.97)0.48
13-3.50***(-4.54)-3.21(-0.31)0.57
13-2.98***(-3.52)

9.08( 1.25)
0.75
13
-0.63
(-0.73)

1.91**( 2.28)0.85( 1.04)-1.55***(-7.45)-0.08(-0.12)0.04
195116.73***
8.03
29.15

0.00
( 0.00)0.06
19599.34***
6.21
29.15

0.65
( 1.08)0.33
19530.29*
5.42
29.15

Notes: Same as Table 2.

33

25.29**( 2.50)-1.68***(-5.09)-2.49**(-2.35)0.15
115
27.13*
1.16
26.05

-2.31
(-2.23)0.18
11530.86**
2.99
26.05

0.57
( 0.50)0.37
11581.38***
6.62
26.05

Table 7. Daily Data Estimations: Reserve Announcement Surprises
Dependent Variable (Risk Indicator): ∆𝑆𝑡
RESSURP

0.50***( 3.32)ΔFX

0.47***( 3.29)0.45***( 3.18)-0.82(-1.17)-0.65(-1.07)

ΔUSS
Constant
R2
obs

0.41**( 2.34)0.07( 0.49)
0.13
76

0.09
(0.61)
0.15
76

0.06
( 0.45)0.22
76

Dependent Variable (Risk Indicator): ∆𝐶𝐷𝑆𝑡
RESSURP-0.98***(-3.57)

ΔFX-0.96***(-3.47)-0.96***(-3.34)0.46( 0.42)0.35( 0.33)

ΔUSS
Constant
R2
obs

-0.26
(-1.17)-0.37(-1.08)
0.10
76

-0.38
(-1.10)0.10
76Notes: Same as Table 2.

34

-0.36
(-1.07)0.10
76

Table 8. Intraday Estimations: Reserve Announcement Surprises
CSI 300 Index
RESSURP
Constant
R2
obs

1-minute
1.01( 0.78)2-minute
2.32( 0.80)5-minute
0.62( 0.11)-0.47(-0.29)
0.04
9

-1.99
(-0.72)
0.06
9

-3.68
(-0.55)0.00
91-minute
2.04**( 2.48)2-minute
2.63( 1.18)5-minute
1.20( 0.33)-0.12(-0.10)
0.21
9

1.06
( 0.32)
0.05
9

-0.94
(-0.17)0.00
91-minute
0.97( 0.99)2-minute
2.00( 0.88)5-minute
1.82( 0.34)0.19( 0.14)
0.05
9

-0.68
(-0.25)
0.05
9

-2.03
(-0.31)0.01
9Shanghai SE Composite Index
RESSURP

Constant
R2
obs

Shenzhen SE Composite Index
RESSURP

Constant
R2
obs
Notes: Same as Table 2.

35

Table 9. Baseline Estimations for Comparison Countries: Stock Price IndicesDependent Variable (Risk Indicator): ∆𝐿𝑂𝐶𝑆𝑇𝑂𝐶𝐾𝑡

ΔLOCRES

Indonesia (Full Sample)
From M ay 2004 to October 2019
0.88***
0.47***
0.40***( 4.79)( 4.11)( 4.01)Indonesia (Post GFC)
From January 2009 to October 2019
0.88***
0.51***
0.44***( 6.55)( 3.82)( 3.63)-1.22***(-7.40)-1.19***(-6.53)

ΔLOCUSD

ΔUSS
Constant
R2
obs

ΔLOCRES

0.46***( 5.18)
1.38***( 3.45)0.21
1851.67***( 4.97)0.45
185

ΔLOCRES

1.31***( 3.65)0.45
1290.87***( 2.57)0.51
129-0.85***(-7.92)-0.07(-0.53)0.86***( 8.79)0.70*( 1.84)0.10
218

0.77**( 2.11)
0.17
218

0.45
( 1.49)0.42
218-0.44***(-3.71)
0.57***( 5.64)0.45( 1.24)
0.14
129

0.51
( 1.58)
0.38
129

-0.01
(-0.04)0.52
129

M alaysia (Full Sample)
From August 2001 to October 2019
0.41***
0.29**
0.22**( 2.95)( 2.12)( 1.97)

M alaysia (Post GFC)
From January 2009 to October 2019
0.32**
0.14
0.09( 2.40)( 1.24)( 0.87)-0.58***(-6.14)-0.52***(-5.65)

ΔUSS

R2
obs

1.01**( 2.47)0.23
129-0.55***(-4.39)ΔLOCUSD

Constant

0.39***( 4.04)South Korea (Post GFC)
From January 2009 to October 2019
1.04***
0.34
0.16( 3.26)( 1.60)( 0.81)

ΔUSS

R2
obs

1.34***( 4.23)0.51
185-0.95***(-5.64)South Korea (Full Sample)
From August 2001 to October 20191.08***
0.64**
0.31( 3.87)( 2.22)( 1.45)ΔLOCUSD

Constant-0.94***(-6.27)-0.33***(-3.27)
0.35***( 4.58)0.42*( 1.69)
0.08
218

0.46*
( 1.95)
0.16
218

0.29
( 1.29)0.28
218-0.35***(-3.55)0.24***( 2.77)

0.52**( 2.02)0.06
1290.55**( 2.32)
0.21
129

0.26
( 1.13)0.29
129Notes: 1. t statistics in parentheses.
2. ***, **, and * denote significant at the 1%, 5%, and the 10% levels,
respectively
3. LOCSTOCK is the MSCI stock price index for a given comparison country. LOCRES is the amount ofreserves (USD million) held by a given comparison country. LOCUSD is the exchange rate in local currency
per USD for a given comparison country.

36

Table 9. Baseline Estimations for Comparison Countries: Stock Price Indices- continued
Dependent Variable (Risk Indicator): ∆𝐿𝑂𝐶𝑆𝑇𝑂𝐶𝐾𝑡

ΔLOCRES

Philippines (Full Sample)
From August 2001 to October 2019
0.39*
0.15
0.14
( 1.88)( 0.84)( 0.82)

Philippines (Post GFC)
From January 2009 to October 2019
0.75**
0.42
0.44( 2.28)( 1.38)( 1.48)-1.28***(-5.06)-1.19***(-4.49)ΔLOCUSD
ΔUSS
Constant
R2
obs

ΔLOCRES0.47***( 4.48)0.78**( 2.07)0.02
218

0.82**( 2.34)0.14
218

0.34***( 3.40)1.06***( 2.79)0.08
1291.16***( 3.34)0.23
1290.77**( 2.28)0.30
129Thai (Post GFC)
From January 2009 to October 20191.03***
0.29*
0.12( 5.40)( 1.73)( 0.59)-2.02***(-6.27)-1.64***(-8.40)

ΔUSS

R2
obs

0.61*
( 1.81)0.25
218-0.79***(-2.74)

Thai (Full Sample)
From August 2001 to October 2019
0.74***-0.08-0.02( 4.22)(-0.37)(-0.10)ΔLOCUSD

Constant-0.87***(-3.58)-1.42***(-5.76)
0.67***( 6.19)0.74*( 1.83)
0.07
218

0.59
( 1.63)
0.25
218

0.38
( 1.19)0.43
218-1.35***(-5.68)0.44***( 4.15)0.92**( 2.41)0.19
1290.82**( 2.49)
0.40
129

0.40
( 1.28)0.50
129Notes: 1. t statistics in parentheses.
2. ***, **, and * denote significant at the 1%, 5%, and the 10% levels,
respectively
3. LOCSTOCK is the MSCI stock price index for a given comparison country. LOCRES is the amount ofreserves (USD million) held by a given comparison country. LOCUSD is the exchange rate in local currency
per USD for a given comparison country.

37

Table 10. Baseline Estimations for Comparison Countries: CDS Spreads
Dependent Variable (Risk Indicator): ∆𝐿𝑂𝐶𝐶𝐷𝑆𝑡

ΔLOCRES

Indonesia (Full Sample)
From M ay 2004 to October 2019-5.39**-2.34**-1.76**(-2.47)(-2.40)(-2.26)

Indonesia (Post GFC)
From January 2009 to October 2019
-4.54***-2.01*-1.36(-3.91)(-1.76)(-1.44)9.02***( 3.91)

8.25***( 5.31)ΔLOCUSD
ΔUSS
Constant
R2
obs

ΔLOCRES

-4.32***(-5.33)-2.47(-0.91)0.17
185-4.54**(-2.19)0.46
185

ΔLOCRES

-5.31**(-2.23)
0.43
129

-1.19
(-0.58)0.58
1292.88***( 6.81)

1.48***( 2.85)-2.08***(-4.68)2.11( 1.55)
0.15
218

1.75
( 1.41)0.28
218

2.53**( 2.14)0.40
218

1.79***( 3.74)-1.51***(-4.74)0.25( 0.20)
0.24
129

0.05
( 0.04)
0.47
129

1.45
( 1.47)0.56
129

M alaysia (Full Sample)
From August 2001 to October 2019-1.77**-0.65-0.10(-2.39)(-1.00)(-0.21)

M alaysia (Post GFC)
From January 2009 to October 2019-3.10***
-1.31
-0.76
(-2.76)(-1.55)(-1.08)5.45***( 6.68)5.13***( 5.83)

ΔUSS

R2
obs

-3.22
(-1.20)0.16
1292.67***( 5.04)ΔLOCUSD

Constant-3.65***(-4.65)South Korea (Post GFC)
From January 2009 to October 2019-4.73***-2.35***-1.86***(-4.47)(-3.60)(-3.07)

ΔUSS

R2
obs

-1.53
(-0.78)0.59
1856.01***( 4.46)South Korea (Full Sample)
From August 2001 to October 2019-4.74***-2.60-1.80**(-3.89)(-2.20)(-1.98)ΔLOCUSD

Constant

6.38***( 3.44)

3.71***( 4.95)-2.47***(-7.25)-0.32(-0.25)
0.06
218

-0.70
(-0.64)
0.32
218

0.46
( 0.51)
0.54
218

3.29***( 4.19)-2.71***(-6.16)-2.2(-1.23)0.11
129-2.50*(-1.68)
0.40
129

0.74
( 0.59)0.59
129Notes: 1. t statistics in parentheses.
2. ***, **, and * denote significant at the 1%, 5%, and the 10% levels,
respectively
3. LOCCDS is the five-year sovereign CDS series for a given comparison country. LOCRES is the amount ofreserves (USD million) held by a given comparison country. LOCUSD is the exchange rate in local currency
per USD for a given comparison country.

38

Table 10. Baseline Estimations for Comparison Countries: CDS Spreads- continued
Dependent Variable (Risk Indicator): ∆𝐿𝑂𝐶𝐶𝐷𝑆𝑡
ΔLOCRES

Philippines (Full Sample)
From August 2001 to October 2019-2.12**-0.45-0.38(-1.98)(-0.45)(-0.39)

Philippines (Post GFC)
From January 2009 to October 2019-1.88*
0.42
0.26(-1.93)( 0.56)( 0.33)

8.91***( 5.39)8.22***( 6.14)ΔLOCUSD
ΔUSS
Constant
R2
obs

ΔLOCRES

-3.79***(-5.15)-1.74(-0.79)
0.02
218

-2.07
(-1.04)0.19
218-3.09***(-5.70)-2.55(-1.33)0.02
129-3.24**(-2.04)
0.33
129

0.26
( 0.20)0.55
129Thai (Post GFC)
From January 2009 to October 2019-3.62***
-1.38
-0.29
(-3.21)(-1.21)(-0.35)4.87***( 4.40)4.98***( 3.49)

ΔUSS

R2
obs

-0.39
(-0.21)
0.39
218

4.58***( 4.52)

Thai (Full Sample)
From August 2001 to October 2019-1.92**
0.05-0.17(-2.39)( 0.05)(-0.27)ΔLOCUSD

Constant

5.63***( 4.65)

2.62***( 3.55)-2.50***(-7.44)-0.06(-0.05)
0.05
218

0.29
( 0.25)
0.17
218

1.07
( 1.06)
0.44
218

3.10
( 3.13)-2.78***(-6.37)-1.36(-0.87)
0.15
129

-1.04
(-0.70)
0.26
129

1.60
( 1.22)0.51
129Notes: 1. t statistics in parentheses.
2. ***, **, and * denote significant at the 1%, 5%, and the 10% levels,
respectively
3. LOCCDS is the five-year sovereign CDS series for a given comparison country. LOCRES is the amount ofreserves (USD million) held by a given comparison country. LOCUSD is the exchange rate in local currency
per USD for a given comparison country.

39