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Are stablecoins truly diversifiers, hedges, or safe havens against traditional cryptocurrencies as their name suggests?

Authors:
Are stablecoins truly diversifiers, hedges, or safe havens against
traditional cryptocurrencies as their name suggests?
Gang-Jin Wang, Xin-yu Ma, Hao-yu Wu
Business School and Center for Finance and Investment Management, Hunan
University, Changsha 410082, China
Abstract
We study the diversifier, hedge, and safe haven properties of stablecoins
against traditional cryptocurrencies. Using the DCC-GARCH model and
dummy variable regression, we examine the risk-dispersion abilities of USD-
pegged and gold-pegged stablecoins against traditional cryptocurrencies, and
also compare their risk-dispersion abilities with their underlying assets. The
empirical results show that (i) stablecoins can serve as safe havens in specif-
ic situations, although most act merely as an effective diversifier in normal
market conditions; (ii) gold-pegged stablecoins perform worse as safe havens
than USD-pegged ones, but both of them perform better than their corre-
sponding underlying assets, and (iii) the safe haven property of stablecoins
changes across market conditions. The above results are robust when using
time-varying copula models. We further evaluate the risk management ap-
plications of stablecoins by analyzing mixed cryptocurrency-stablecoin port-
folios, finding evidence of USD-pegged stablecoins performing better than
gold-pegged stablecoins in extreme risk reductions. Our work provides im-
portant insights into diversification for cryptocurrency investors.
Keywords: cryptocurrency, stablecoin, safe haven, hedge, diversifier
JEL: C11, G11, G15
Corresponding authors.
Email addresses: wanggangjin@hnu.edu.cn (Gang-Jin Wang), maxixi@hnu.edu.cn
(Xin-yu Ma), wuhaoyu@hnu.edu.cn (Hao-yu Wu)
Preprint submitted to Research in International Business and Finance March 16, 2020
1. Introduction
1.1. Background
Cryptocurrency (e.g., Bitcoin) is designed as a decentralized peer-to-peer
payment system which allows online payments to flow directly from one par-
ty to another without going through a financial institution. Owing to these
attractive characteristics, cryptocurrency has received much attention in re-
cent years. A recent survey reported that more than a third of American
investors are willing to invest in or hold Bitcoin.1However, due to the dras-
tic fluctuation of cryptocurrency prices, investors with weak risk tolerance
hesitate to add cryptocurrencies to their portfolios. Figure 1 depicts the dai-
ly closing prices of Bitcoin (BTC), Litecoin (LTC), and Ripple (XRP) from
August 5, 2013 to March 20, 2019, obtained from coinmarketcap.com. For
instance, the value of Bitcoin has skyrocketed to over ten million times in
nine years from its introduction in 2009 to its peak price of nearly $20,000
in December 2017. However, during the next six months, Bitcoin depreci-
ated by nearly 70%, and the total market capitalization of cryptocurrencies
shrank from a peak value of $840 billion in January 2018 to $261.7 billion in
September 2018. Thus, the existence of giant bubbles in the cryptocurren-
cy markets has been investigated (Cheah & Fry,2015;Fry & Cheah,2016;
Corbet et al.,2018).
The high-volatility character of cryptocurrencies makes it difficult for in-
vestors to gain stable returns or maintain value. Thus, there is a pressing need
for proper investment tools to hedge against traditional cryptocurrencies. In
this context, stablecoins were introduced as an alternative to traditional cryp-
tocurrencies. Because of the difference in the conception of their technology,
stablecoins differ greatly from traditional cryptocurrencies, both in design
and in investors’ perceptions of them. A useful currency has three key func-
tions: as a store of value, a unit of account, and a medium of exchange.
Stability is a key premise for these three functions because: (i) investors
are reluctant to store their wealth in a volatile asset whose value constantly
changes, (ii) pricing goods becomes difficult with a fluctuating currency, and
(iii) people hope for fair payment for goods and services without changes in
price during the payment process. Therefore, volatile assets like traditional
cryptocurrencies cannot function as an effective store of value, a good unit
1See https://grayscale.co/insights/bitcoin-investor-study-2019.
2
of account, or a medium of exchange. Stablecoins are created to address
these issues of traditional cryptocurrencies. As their name implies, stable-
coins are designed to be a price-stable cryptocurrency. Further, they are
different from traditional cryptocurrencies in terms of investors’ perception-
s. Owing to their pegging mechanism, stablecoins bridge fiat currencies with
traditional cryptocurrencies. Unlike traditional cryptocurrencies, stablecoins
could maintain value and hedge risk for other volatile assets, catering to the
various needs of different investors.
Specifically, a stablecoin is pegged either to a fiat currency (e.g., US-
D and CNY), or to a commodity (e.g., precious metals such as gold and
silver). Since there is a network of decentralized vaults and commodity hold-
ers, stablecoins are always decentralized, which makes them more attractive
to cryptocurrency users. The market demand for low-volatility digital cur-
rency tools has accelerated the rapid development of stablecoins. Currently,
there are 23 types of stablecoins in circulation, of which many (e.g., Tether,
DGD, HGT, and XAUR) are fiat-collateralized, some (e.g., Dai, BitUSD,
and BitCNY) are crypto-collateralized, and a few (e.g., Havven and NuBits)
are non-collateralized.2According to Mita et al. (2019), (i) broadly speaking,
stablecoins are a stabilization mechanism achieved by controlling the propor-
tional relationship of exchange rate between traditional cryptocurrencies and
fiat currencies, and (ii) pegging is an effective way to reduce asset volatility.
Sidorenko (2020) also notes that the cryptocurrency market trend is moving
in the direction of transferring funds to some representative low-volatility
digital assets, which affirms the ability of stablecoins to save or exchange
market assets.
Since stablecoins are designed as low-volatility cryptocurrencies against
traditional high-volatility ones to avoid the risk of market declines, two ques-
tions naturally present themselves: what potential role would stablecoins play
in the fierce fluctuations in the prices of traditional cryptocurrencies? Would
they perform the roles of their anchors (e.g., gold and USD) as diversifiers,
hedges, or safe havens against traditional cryptocurrencies?
Many previous works (Baur & Lucey,2010;Baur & McDermott,2010;
Ranaldo & S¨oderlind,2010;Bekiros et al.,2017;Wen & Cheng,2018) have
2Note that on June 18, 2019, Facebook announced that Libra, a stablecoin fully backed
by a reserve of assets, will launch (along with 27 other partners) in 2020. The Libra project
has caused wide public concerns; for instance, the US Congress wanted Facebook to halt
the development of the Libra project due to regulatory hurdles.
3
proved the hedge or safe haven functions of gold or USD relative to tradi-
tional assets through different models. Based on this, and to answer the first
question, our paper studies whether low-volatility stablecoins have the same
functions as traditional assets. To highlight the low-volatility effect, we fo-
cus on the diversifier, hedge, and safe haven properties of stablecoins against
traditional cryptocurrencies, for instance, the most outstanding stablecoin,
Tether, against the most representative cryptocurrency, Bitcoin. On the one
hand, when stablecoins are strong hedging assets for traditional cryptocur-
rencies, this means that they are not related to traditional cryptocurrencies
under normal market conditions. On the other hand, when stablecoins are
strong safe haven assets for traditional cryptocurrencies, this means that sta-
blecoins are irrelevant to traditional cryptocurrencies under extreme market
conditions, which also implies that stablecoins can be applied to build invest-
ment portfolios superior to traditional cryptocurrencies. Further, to answer
the second question, we compare the potential functions of the low-volatility
stablecoins with traditional hedging or safe-haven assets (i.e., gold or USD)
against traditional cryptocurrencies.
It is worth noting that Chohan (2019) argues that whether stablecoins
are truly stable is still an unresolved question. This is because, in addi-
tion to technical or political factors, the credibility of pegged currencies and
resources is also uncertain, which will affect the demand of stablecoins for
market investors to a certain extent. Therefore, we consider this in our re-
search on the stable function of stablecoins.
Specifically, to examine whether stablecoins are diversifiers, hedges, or
safe havens against traditional cryptocurrencies, we follow Baur & Lucey
(2010) and Baur & McDermott (2010) in using the dynamic conditional cor-
relation (DCC)-GARCH model and the dummy variable regression. Like
in Reboredo (2013b), we also evaluate their risk management functions for
investors with analysis of portfolios including a stablecoin and a traditional
cryptocurrency. Finally, we employ time-varying copulas to test the robust-
ness of our findings.
1.2. Literature Review
Our work is related to the literature on whether gold and cryptocurrencies
(especially Bitcoin) are diversifiers, hedges, or safe havens against traditional
assets (e.g., stocks, USD, bonds and commodities). As a traditional class of
assets, gold has often been considered a safe haven. Much attention has been
4
focused on whether gold is a diversifier, a hedge, or a safe haven in the litera-
ture. Baur & Lucey (2010) first discuss the testable definition of a diversifier,
a hedge, and a safe haven, and pioneer the test of this hypothesis by building
a regression equation of gold returns on extreme returns of stocks and bonds.
According to their study, gold can be regarded as a hedge against stocks on
average and a safe haven in extreme stock market conditions, though the safe
haven property is short-lived. Following Baur & Lucey (2010) , a large body
of research has tested the diversification properties of gold from different per-
spectives (Baur & McDermott,2010;urg¨un & ¨
Unalmı¸s,2014). Moreover,
other approaches (e.g., copula models, quantile regressions, wavelet analysis,
and smooth transition) are also used to detect the diversifier, hedge, and
safe haven properties of gold (see, e.g., Ciner et al.,2013;Reboredo,2013a,b;
Beckmann et al.,2015;Bekiros et al.,2017;Dar & Maitra,2017;´
Smiech &
Papie˙z,2017;Wen & Cheng,2018).
With rapid changes in market capitalization and increasing media atten-
tion to cryptocurrencies in recent years, there has been a growing research
interest in the economics and finance of this new kind of asset (Corbet et al.,
2019;Wang et al.,2019a), particularly Bitcoin. Moreover, several studies
focus on their market efficiency (Vidal-Tom´as & Iba¨nez,2018;Zhang et al.,
2018;Zargar & Kumar,2019), information spillover effects among cryptocur-
rencies (Yi et al.,2018;Beneki et al.,2019;Katsiampa et al.,2019;Sifat et al.,
2019;Omane-Adjepong & Alagidede,2019), and investor attention to Bitcoin
(Dastgir et al.,2019;Shen et al.,2019).
Since Bitcoin has shown great resilience in times of turmoil, an increasing
number of papers discuss the potential roles of Bitcoin against traditional
assets (i.e., a safe haven, a hedging asset, or a diversifier) and draw different
conclusions. Many studies (Feng et al.,2018;Stensas et al.,2019;Urquhart
& Zhang,2019) find Bitcoin to be a great diversifier for stocks, commodities,
and certain currencies, while others (Bouri et al.,2017a;Stensas et al.,2019;
Wang et al.,2019b) observe that in particular situations, cryptocurrencies
such as Bitcoin can act as safe haven or hedging tools for commodity indices,
major stock indices, and currencies. However, Kliber et al. (2019) argue that
Bitcoin plays different roles across countries based on their analysis of five
countries. Further, they find that the transaction currency also matters; for
instance, Bitcoin can be regarded as a weak hedge when traded in USD. In
addition, a few works (see, e.g., Selmi et al.,2018;Bouoiyour et al.,2019)
compare the hedging and safe haven properties of Bitcoin with gold, finding
that both serve as safe haven assets in particular times (e.g., political and
5
economic turmoil).
In contrast, some studies question the hedge and safe-haven properties
of Bitcoin. For example, Bouri et al. (2017b) use a DCC-GARCH model to
examine the hedge and safe haven functions of Bitcoin for major world stock
indices. They find that the two properties of Bitcoin vary across horizons and
in the majority of cases, Bitcoin acts as only a poor hedge. Shahzad et al.
(2019) use a bivariate cross-quantilogram approach to study the safe haven
property of Bitcoin, gold, and commodity against stocks and find that each
can only act as a weak safe haven asset in some cases. Baur et al. (2018)
investigate the attributes of Bitcoin and show that it is mainly used as a
speculative investment rather than as a currency and medium of exchange.
Smales (2019) analyzes Bitcoin’s correlations with other assets and its price
discovery, volatility, and liquidity characteristics and concludes that it is not
worth considering Bitcoin as a safe haven.
Since Bitcoin lost more than half of its market share in the cryptocur-
rency market,3much attention has been paid to the capabilities of other
cryptocurrencies against traditional assets. Bouri et al. (2019) find that oth-
er leading cryptocurrencies including Ethereum and Litecoin can serve as
effective diversifiers as well as hedges, especially against Asian Pacific and
Japanese equities, suggesting that investors should consider other choices be-
sides Bitcoin alone. Wang et al. (2019c) investigate the hedge and safe haven
properties of 973 types of cryptocurrencies against 30 international indices,
and observe that cryptocurrency is a safe haven but not a hedge in general.
The above discussion indicates that the characteristics of cryptocurrencies
could be controversial, and the diversification capabilities of cryptocurren-
cies as represented by Bitcoin are contentious. Nevertheless, it is certain that
cryptocurrencies are extremely volatile and subject to speculative behavior
from investors, which means that before being treated as a diversification
tool, they should be diversified first. For investors who hold cryptocurren-
cies for capital gains rather than hedging other physical assets, a critical
issue could be how to diversify, hedge, and eliminate extreme losses from
cryptocurrencies.
As a type of cryptocurrency, stablecoins are designed to address the wild
price swings of traditional cryptocurrencies and bridge fiat currencies with
3According to Bouri et al. (2019), the market share of Bitcoin shrank from around 90%
in early 2017 to 48% in July 2018.
6
traditional cryptocurrencies (Mita et al.,2019;Sidorenko,2020). Stablecoins
offer users a way to enjoy the benefits of virtual currencies while also partial-
ly reduce their volatility risks. Recently, an increasing number of investors
have been attracted by the low-volatility character of stablecoins. High mar-
ket demand for low-volatility digital assets has already led to a remarkable
jump in the stablecoin economy (Sidorenko,2020). According to a report by
blockchain.com, the total market share of stablecoins in the cryptocurrency
market increased from 1.5% in September 2018 to 2.7% in February 2019
(Micky,2019). Compared with traditional cryptocurrencies, stablecoins are
preferred by market participants or investors who (i) are not willing to be
exposed to additional currency risk when transacting in cryptocurrencies;
(ii) need a tool to minimize risk in the turnover of cryptocurrencies without
withdrawing funds back to a bank fiat account; (iii) solely seek a store of
value on a censorship-resistant ledger to avoid the local banking system or
collapsing economies.
Since traditional cryptocurrencies are extremely volatile and stablecoin-
s are designed to address this problem, traditional cryptocurrency investors
have the motivation to add stablecoins into their portfolios to diversify, hedge,
and eliminate extreme losses. However, little attention has been paid to sta-
blecoins in the literature, especially their potential against traditional cryp-
tocurrencies. Our work aims to fill this gap by investigating the diversifier,
hedge, and safe haven properties of stablecoins against traditional cryptocur-
rencies.
1.3. Main contributions
Much research has examined the role of gold as a hedge or a safe haven against
different traditional assets, and a growing number of studies focus on the
relations between cryptocurrencies (especially Bitcoin) and traditional assets;
however, research on the potential roles of stablecoins against traditional
cryptocurrencies is scarce. Our work builds on the framework of Baur &
Lucey (2010) and Baur & McDermott (2010), but the targets and methods
adopted in our study are different. We choose three USD-pegged stablecoins
(Tether, BitUSD, and NuBits), three gold-pegged stablecoins (DGD, HGT,
and XAUR), and three traditional cryptocurrencies (Bitcoin, Litecoin, and
Ripple). Our research contributes to the existing literature by examining
the diversifier, hedge, and safe haven roles of stablecoins against traditional
cryptocurrencies. In addition, we examine whether stablecoins help to reduce
loss for cryptocurrency investors in extreme market situations by considering
7
a cryptocurrency-stablecoin portfolio. In greater detail, our contributions are
as follows.
First, we study the dependence structure between stablecoins and tradi-
tional cryptocurrencies using the DCC-GARCH model of Engle (2002), from
which we obtain dynamic conditional correlations of the pairs of assets un-
der study. We employ ARMA-GARCH models to fit marginal distributions
of returns of stablecoins and traditional cryptocurrencies, and use the DCC
model to estimate dynamic correlations between stablecoins and traditional
cryptocurrencies.
Second, to accurately analyze the dynamic correlations between stable-
coins and cryptocurrencies in different stages, we broaden the research using
multiple structural change models from Bai & Perron (1998,2003) to di-
vide the time horizon of traditional cryptocurrencies (BTC, LTC, XRP) into
three stages. We examine two aspects: (i) which stablecoin perform better
as the role of diversifier, hedge, or safe haven, and (ii) whether the function
of stablecoin will change across market stages.
Third, since the interdependence between stablecoins and traditional cryp-
tocurrencies is useful for portfolio investors who seek to reduce risk, we
evaluate the value-at-risk (VaR) and excepted shortfall (ES) reduction of
portfolios that consist of traditional cryptocurrencies and stablecoins with d-
ifferent weights. This evaluation helps determine which combinations reduce
downside risk to investors. Combining with the DCC-GARCH model and
downside risk evaluations, we find that (i) USD-pegged stablecoins perform
better as hedges and safe havens against traditional cryptocurrencies than
gold-pegged ones and (ii) Tether is a good asset for reducing losses caused
by the decline of traditional cryptocurrencies.
Finally, as a supplement, we use time-varying copulas to calculate the
dynamic dependence between stablecoins and traditional cryptocurrencies.
The results we obtain from the estimation of copulas are consistent with our
findings under the DCC framework.
1.4. Organization
The paper is structured as follows. Section 2outlines the methodologies.
Section 3describes the data and Section 4presents empirical results of the
DCC-GARCH model and dummy variable regression, which includes both
the full-period and subperiod analyses. Section 5shows the risk management
analysis of different-weighted portfolios. Section 6reports the robustness
8
test based on time-varying copulas. Section 7summarizes our findings and
conclusions.
2. Methodology
2.1. The DCC-GARCH model
We use the dynamic conditional correlation (DCC)-GARCH model of Engle
(2002) to capture dynamic relationships across time series. In this study,
a bivariate DCC-GARCH model is employed for pairs of return series to
avoid biased estimates of parameters. The estimation of the bivariate DCC-
GARCH model is carried out in two steps. In the first step, an ARMA-
GARCH model is estimated to fit the marginal distributions of returns. In the
second, a time-varying correlation matrix is computed using the standardized
residuals obtained from the first step estimation.
We first use different ARMA-GARCH models or ARMA-GJR-GARCH
models with different distributions to fit marginal distributions of returns
for different stablecoins and traditional cryptocurrencies. In some cases, we
establish a seasonal differential processing on the return series before the
ARMA-GARCH estimation to eliminate the disturbance of seasonal factors.
The ARMA(p,q)-GARCH(1,1) model is specified as4
ri,t =µi+
p
j=1
φi,jri,tj+
q
j=1
ϕi,jεi,tj+εi,t (1)
σ2
i,t =ci+αiε2
i,t1+βiσ2
i,t1(2)
ηi,t =εi,ti,t (3)
In Eq. (1) (i.e., mean equation), ri,t denotes the returns of stablecoins or
traditional cryptocurrencies i,µiis the constant term, φi,j and ϕi,j are au-
toregressive and moving average parameters, and εi,t is the residual series.
In Eq. (2) (i.e., variance equation), σ2
i,t is the conditional variance, ciis the
4About the specification of the ARMA component, we mainly use the AMRA(1,1),
ARMA(2,1) or ARMA(2,2) model to achieve a simple and good fitting effect and eliminate
the autocorrelation in returns. In the Online Appendix, Tables A1A6 report the detailed
estimation results of ARMA-GARCH(1,1) or ARMA-GJR-GARCH(1,1) models for return
series of traditional cryptocurrencies and stablecoins during different periods.
9
constant, αiis the parameter that captures the short-run persistence or the
ARCH effect and βiis the parameter that captures the long-run persistence
of volatility or the GARCH effect. In Eq. (3), ηi,t is the standardized residual
series following a specific i.i.d. distribution.5
To model the asymmetry volatility of stablecoins and traditional cryp-
tocurrencies, we consider the GJR-GARCH(1,1) model,6which is defined as
σ2
i,t =ci+αiε2
i,t1+γiε2
i,t1Ii,t1+βiσ2
i,t1(4)
where Ii,t1is a dummy variable, which is used to set a threshold to distin-
guish the impact of positive and negative shocks on conditional volatility.
As in Engle (2002), the DCC-GARCH(1,1) equation is specified for the
conditional covariance matrix Qtwith element qij,t, which is a positive-
definite matrix:
Qt= (1 θ1θ2)¯
Q+θ1Qt1+θ2ηt1η
t1(5)
where θ1and θ2are non-negative parameters that satisfy θ1+θ2<1 and
capture the influences of previous DCCs and previous shocks on the cur-
rent DCC respectively; ¯
Qis the unconditional covariance matrix of ηt;
and ηt= (η1t, η2t)represents a marginally standardized innovation vector,
that is, the vector of standardized residuals obtained from the estimation of
(GJR)-GARCH(1,1) process.
The DCC matrix is calculated by:
ρt=JtQtJt(6)
5In our work, four distributions are considered to fit the standardized residual series of
returns for traditional cryptocurrencies and stablecoins: Gaussian distribution, student’s t-
distribution, the generalized error distribution (GED) and skewed student’s t-distribution.
For the returns of the traditional cryptocurrencies and stablecoins during the full-sample
period, most of them always follow a Gaussian distribution, except that (i) the return
series of NuBits perfectly obeys a skewed student’s t-distribution and (ii) that of HGT
and XAUR follows a GED. But for the subperiod sample, the best fitted distributions for
different returns are diverse and respectively obey the above four distributions. However,
most of the returns are subject to a Gaussian distribution or a student’s t-distribution. The
best fitted marginal distributions of returns for the full-period sample and the subperiod
sample can be found in Tables A1A6 in the Online Appendix.
6Note that according to McAleer (2014) and Caporin & Costola (2019), the GJR-
GARCH model is asymmetric but fails to capture the leverage effect.
10
where Jt= diag q1/2
11,t , q1/2
22,t is a normalized matrix to ensure that ρtis a
correlation matrix, and qii,t is the (i, i)-th element of matrix Qt.
The pairwise DCC between stablecoin iand traditional cryptocurrency j
is given by:
ρij,t =qij,t
qii,tqjj,t
(7)
2.2. Dummy variable regression
The role of stablecoins as diversifiers, hedges, or safe havens against tradi-
tional cryptocurrencies are examined using the dummy regression model as in
Baur & McDermott (2010), Ratner & Chiu (2013) and Bouri et al. (2017b).
Dynamic conditional correlations ρij,t between stablecoin iand traditional
cryptocurrency jare extracted from the bivariate DCC-GARCH model in-
to separate time series and then regressed on dummy variables indicating
extreme downward movements in the lower 10th, 5th, or 1st percentile of
the return distribution of traditional cryptocurrency j. The dummy variable
regression model is specified as:
ρij,t =m0+m1D(rcrypto q10) + m2D(rcryptoq5) + m3D(rcryptoq1) + vt(8)
where ρij,t represents the pairwise conditional correlations between stablecoin
iand traditional cryptocurrency j,vtis the error term, rcry ptoq10 ,rcrypto q5,
and rcrypto q1are 10%, 5%, and 1% quantiles of returns for traditional cryp-
tocurrency j, and D(·) is a dummy variable for obtaining the extreme price
movement of traditional cryptocurrency j. Taking D(rcryptoq10) as an exam-
ple, it takes 1 if the return of traditional cryptocurrency jis smaller than
the 10% quantile, otherwise it takes 0.
Following Baur & McDermott (2010), Ratner & Chiu (2013) and Bouri
et al. (2017b), we determine whether stablecoin iis a diversifier, a hedge or
a safe haven against traditional cryptocurrency jby analyzing the estimated
values and statistical significance of mi(i= 0,1,2,3) in Eq. (8). In summary,
there are five cases: (i) if m0is significantly positive, stablecoin i(or the other
assets under study) is a diversifier against traditional cryptocurrency j; (ii)
if m0is zero, stablecoin iis a weak hedge against traditional cryptocurrency
j; (iii) if m0is significantly negative, stablecoin iis a strong hedge; (iv) if m1,
m2and m3are not significantly different from zero, stablecoin iis a weak
safe haven against traditional cryptocurrency j; and (v) if m1,m2and m3
are negative, stablecoin iis a strong safe haven.
11
3. Data and primary analysis
Considering both the liquidity and market capitalization, we select three rep-
resentative USD-pegged stablecoins including Tether, BitUSD, and NuBits,
and three typical gold-pegged stablecoins including DGD, HGT, and XAUR.
Tables 1and 2present the detailed information on these six stablecoins. In
what follows, we show a brief introduction to them.
USD-pegged stablecoins can be exchanged with USD usually at a 1:1
ratio. Tether was initially designated to be worth $1.00, and equal amounts
of dollars must be deposited to support its issuance. However, on March 14,
2019 it was announced that only $0.74 cash was backing each Tether, which
meant that the value of Tether had shrunk by 26%. BitUSD, which is linked
with the blockchain of BitShares, is backed by at least twice the amount
calculated based on BitShares’ price. The essence of the BitShares system
is the Market Anchoring Mechanism, which aims to make market prices of
BitUSD ultimately consistent with those of the USD under the excess of
BitShares. NuBits is the world’s first stablecoin designed to keep its value at
$1.00. Thus, the supply of NuBits is dynamically adjusted in response to the
changes in its demand. NuBits experienced ups and downs in demand, and
remained infamously underperforming for 3 months in 2016 and thereafter
grew 1.5 times by the end of 2017.
Gold-pegged stablecoins are backed by gold and can be exchanged for
physical gold. DGD is constructed by DigixDAO (a decentralized autonomous
institution) and is disposed in the blockchain by DigixGlobal. DGD is issued
as an ICO (Initial Coin Offerings), which provides holders with the corre-
sponding voting rights according to the amount of DGD they hold. HGT,
which is a HelloGold project, corresponds to the rights and interests of Hel-
loGold platform’s trading of handling and custody fee, because HelloGold’s
business model includes a gold financial service platform based on gold backed
tokens (GBT) and equity certificates based on HGT. Its operation is similar
to that of DGD. XAUR, which is in the POS blockchain and switches to
Ethereum, is created for the exchange of a unit physical gold.
In addition to USD-pegged and gold-pegged stablecoins, we use three
traditional cryptocurrencies, including Bitcoin (BTC), Litecoin (LTC) and
Ripple (XRP), to determine the potential roles of stablecoins against tra-
ditional cryptocurrencies. According to the cryptocurrency market capital-
ization as of March 20, 2019, BTC, XRP, and LTC are the first, third, and
fifth largest cryptocurrencies, respectively. We do not consider the second
12
and fourth largest cryptocurrencies, ETH and Bitcoin Cash, because their
starting trading dates are later than the start date of our investigated sample
period. The data on stablecoins and traditional cryptocurrencies are from
coinmarketcap.com. For comparison, we also take into account the underly-
ing assets of USD-pegged and gold-pegged stablecoins, USD and gold. We
use the US Dollar Index (USDX) and the Gold Index of London (AUUSDO)
to represent USD and gold.
Because launch dates for the two types of stablecoins are different (see
Tables 1and 2), the sample periods that we collect for USD-pegged and gold-
pegged stablecoins are not identical. USD-pegged stablecoins were launched
earlier than gold-pegged ones, and their sample period is from March 6, 2015
to March 20, 2019, or 1438 observations. The starting date is determined by
the day when Tethers returns changed from zeros to floating values. The sam-
ple period for gold-pegged stablecoins starts on October 13, 2017 and ends on
March 20, 2019, or 524 observations. We collect the daily closing prices of the
six stablecoins in the corresponding sample period. In each sample period,
we also collect the daily closing prices of the three traditional cryptocurren-
cies (i.e., BTC, LTC, and XRP) and the corresponding underlying-asset (i.e.,
USD or gold). For simplicity, we designate the sample from March 6, 2015
to March 20, 2019 including three USD-pegged stablecoins, three traditional
cryptocurrencies, and USD as the USD-pegged sample, and the other sam-
ple from October 13, 2017 to March 20, 2019, including three gold-pegged
stablecoins, three traditional cryptocurrencies, and gold as the gold-pegged
sample.
Table 3shows the descriptive statistics of returns for the six stablecoins
and three traditional cryptocurrencies as well as for their underlying assets,
USD and Gold. We find that NuBits and HGT show the highest volatility
among USD-pegged and gold-pegged stablecoins, respectively. Among tradi-
tional cryptocurrencies, XRP has the highest return and standard deviation
in both samples (see Panels A and B). Contrary to our expectations, the
returns of stablecoins, especially gold-pegged stablecoins, also fluctuate con-
spicuously. The returns of cryptocurrencies are quite extreme; for example,
the maximum return of HGT reaches 125% while its minimum return is low
to 92%. As discussed, stablecoins were created to reduce the risk of excessive
fluctuations in traditional cryptocurrencies. Thus, when the market is under
stress, the prices of stablecoins should remain unchanged or only fluctuate
slightly. However, the huge fluctuations of HGT show that stablecoins can-
not fully realize stable functions in extreme market conditions. The high
13
kurtosis of the virtual assets under study indicates a great number of tail re-
turns, and the results of non-zero skewness reflect asymmetric distributions
of returns. The Jarque-Bera test results imply that none of the returns follow
Gaussian distribution, which is in accordance with results drawn from skew-
ness and kurtosis. USD and Gold, as traditional safe haven assets, present
some features that are distinct from those of cryptocurrencies. Their returns
are much less volatile than those of both stablecoins and traditional cryp-
tocurrencies. The distributions of their returns are close to the Gaussian
distribution, with a skewness near 0 and a kurtosis near 3. The ADF unit
root tests show that all returns are stationary, indicating that they can be
modeled on ARMA-GARCH or ARMA-GJR-GARCH.
4. Empirical results
4.1. Dynamic conditional correlations
4.1.1. Full period analysis
Figure 2presents the full-period dynamic conditional correlations (DCCs) be-
tween three prevalent USD-pegged stablecoins (i.e., Tether, BitUSD, and Nu-
Bits) and three representative traditional cryptocurrencies (i.e., BTC, LTC
and XRP). Figure 2(c), (f), and (i) depict DCCs between NuBits and three
traditional cryptocurrencies. We find that the DCCs are almost all positive,
suggesting that in most times NuBits is positive correlated to traditional
cryptocurrencies. In particular, the DCCs between NuBits and LTC and
between NuBits and XRP show a similar trend. The DCCs between BitUSD
and traditional cryptocurrencies are also positive as the whole (see Fig. 2(b),
(e), and (h)), but their DCCs are highly volatile. Figure 2(a), (d), and (g)
show that the dynamic correlation results for Tether are noteworthy. The D-
CCs between Tether and three traditional cryptocurrencies are nearly stable
and equal to 0 before April 2017, and have been fluctuating thereafter. The
main reason of these pronounced structural changes in the DCC processes
may be the following unique properties of Tether. Namely, the closing prices
of Tether are nearly fixed at $1.00 before April 2017, thus its returns are n-
early 0 for a relatively long time, partly reflecting the stability of Tether. We
observe that the DCCs between Tether and traditional cryptocurrencies are
not always positive. Particularly, the dynamic correlations between Tether
and XRP are negative throughout the sample period.
In summary, according to the full-period DCCs between USD-pegged
stablecoins and traditional cryptocurrencies in Fig. 2, we find that (i) BitUSD
14
and NuBits are positive-related to traditional cryptocurrencies in most times
and (ii) Tether seems to have the best risk-dispersion effects.
Figure 3shows the full-period DCCs between three gold-pegged stable-
coins (i.e., HGT, DGD, and XAUR) and three traditional cryptocurrencies
(i.e., BTC, LTC, and XRP). Unlike USD-pegged stablecoins, the DCCs be-
tween gold-pegged stablecoins and three traditional cryptocurrencies are al-
most always positive. This result suggests that the hedge effect of the three
gold-pegged stablecoins against traditional cryptocurrencies is quite limited,
especially for DGD, because its dynamic correlations with the three tradi-
tional cryptocurrencies exceed 0.5 at most times.
In short, based on the DCCs between gold-pegged stablecoins and tra-
ditional cryptocurrencies, we find little hedging effect of gold-pegged stable-
coins against traditional cryptocurrencies, which suggests that gold-pegged
stablecoins may not be a perfect class of assets for cryptocurrency investors
to build a hedge portfolio.
4.1.2. Subperiod analysis
The second half of 2017 witnessed the boom of cryptocurrencies including
BTC, LTC, and XRP; for example, over several months, the price of one BTC
rose from $2500 to $19,000. However, this was not a lasting trend: cryp-
tocurrencies experienced dramatic declines in 2018, and the value of BTC
declined by more than 50%. In this context, to further examine whether
the diversifier, hedge, or safe haven roles of stablecoins are affected by mar-
ket environments, we check the robustness over the full-period sample using
multiple structural change models from Bai & Perron (1998,2003) to detect
breakpoints in the returns of the three traditional cryptocurrencies. Specif-
ically, we use the sequential L+ 1 breaks vs. Lmethod from Bai & Perron
(1998,2003) to detect breakpoints in the returns of BTC, LTC, and XRP,
and divide the full period into subperiods before and after the breakpoints.
Table 4shows breakpoints in the returns of three traditional cryptocur-
rencies over the full period from March 6, 2015 to March 20, 2019 (corre-
sponding to the USD-pegged sample period). Note that here we only consider
the USD-pegged sample period because of a short gold-pegged sample period.
We detect two breakpoints in the returns of each traditional cryptocurrency
over the entire period, meaning that the entire period is divided into three
subperiods in terms of each traditional cryptocurrency. Based on the break-
points of each traditional cryptocurrency, we divide the USD-pegged sample
period into three subperiods, which correspond to the boom, the depression,
15
and the recovery, respectively.
Figure 4shows the DCCs between USD-pegged stablecoins and BTC in
three subperiods. During subperiod 1, DCCs between (i) Tether and BTC
fluctuate between 0.2 and 0.2, and most of the correlations are negative, (ii)
BitUSD and BTC are all positive, reaching the peak value over 0.4, and (iii)
NuBits and BTC float sharply in the range [0.5,1]. During subperiod 2,
Tether and BTC are negatively correlated in most cases, while DCCs between
another two stablecoins (i.e. BitUSD and NuBits) and BTC stabilize at 0.117
and 0.046, respectively. During subperiod 3, we find that (i) DCCs between
Tether and BTC fluctuate around zero and the maximum value is close to
0.5, (ii) DCCs between BitUSD and BTC are mostly negative during the first
half subperiod, but become positive and show an increasing trend during the
other half, and (iii) NuBits is positively correlated with BTC (except for
isolated cases).
From the above various results obtained from the DCC analysis in Fig. 4,
we find that DCCs between USD-pegged stablecoins and BTC are time-
varying and stablecoins have different DCC patterns with traditional cryp-
tocurrencies. In particular, two of the three USD-pegged stablecoins (i.e., Bi-
tUSD and NuBits) present statically positive correlations with BTC during
subperiod 2, implying that they can be influenced by the extreme downside
of BTC. It is a reminder that regardless of their special issuing mechanism,
stablecoins are still one class of the virtual assets. The relations between sta-
blecoins and traditional cryptocurrencies are quite like cross-sector relations
in stock markets. The slump of a “giant” stock may lead to a fall in the
others, including those from different sectors. However, we should note the
special case of Tether, because it is negatively correlated with BTC during
all three subperiods in general.
Figure 5depicts the DCCs between USD-pegged stablecoins and LTC
in each subperiod. Overall, the results are similar to that shown in Fig. 4,
except that during subperiod 2, NuBits maintains a negative correlation with
LTC at 0.014. Also, the DCCs between BitUSD and LTC are close to 0.1
during the first subperiod and remain prominently positive during the next
two stages.
Figure 6exhibits the DCCs between USD-pegged stablecoins and XRP in
different subperiods. In contrast to the results obtained from Figs. 4and 5,
the dynamic correlations between Tether and XRP stabilize at 0.046 during
subperiod 2, which reflects some particular traits of XRP and suggests that
it is difficult for investors to avoid the risk of a fall in XRP by investing in
16
USD-pegged stablecoins.
4.2. Dummy variable regression
In this section, we use the dummy variable regression to check whether stable-
coins are diversifiers, hedges or safe havens for traditional cryptocurrencies.
We examine the potential roles of USD-pegged and gold-pegged stablecoin-
s over the entire sample period. Further, we consider the potential roles
of USD-pegged stablecoins in subperiods corresponding to different market
conditions.
4.2.1. Full-period analysis
In Table 5, we report estimated coefficients of Eq. (8) for examining the
diversifier, hedge, and safe haven properties of stablecoins in the entire peri-
od. We first focus on the estimated coefficient m0to analyze the diversifier
or hedge properties of stablecoins against traditional cryptocurrencies. The
estimated m0of Tether are all negative at the 5% significance level, which
are 0.004 for BTC, 0.019 for LTC, and 0.017 for XRP, suggesting the
hedge role of Tether against traditional cryptocurrencies. The estimates of
BitUSD are 0.152 for BTC, 0.149 for LTC, and 0.107 for XRP at the 1%
significance level, which indicate that BitUSD can be a diversifier for them.
Like BitUSD, the estimated coefficients of NuBits are all positive at the 1%
significance level, also implying the diversifier property of NuBits against
traditional cryptocurrencies. The diversifier property also holds for the three
gold-pegged stablecoins, because their estimated coefficients m0are positive
at the 1% significance level. To summarize, all stablecoins are diversifiers
for the three traditional cryptocurrencies, except Tether, which is a strong
hedge asset for them.
We then focus on the estimated coefficients m1,m2, and m3(correspond-
ing to the 10%, 5% and 1% quantiles, respectively) to explore the safe haven
property of stablecoins against traditional cryptocurrencies. For extreme
downward situations, most estimated coefficients m1,m2, and m3are insignif-
icant. For example, none of the estimates of NuBits or HGT are significant
for BTC, LTC, or XRP at any of the three quantiles. However, a few esti-
mates are significant at certain quantile levels. For USD-pegged stablecoins,
the estimated m2of Tether for BTC are 0.019 at the 10% significance level
and 0.034 for LTC at the 5% significance level, indicating that Tether can be
a safe haven asset for BTC and LTC. The estimated m1of BitUSD for BTC,
17
LTC, and XRP are all significantly negative. Specifically, the estimated co-
efficients m1of BitUSD are 0.060 for BTC at the 1% significance level, and
0.031 and 0.024 for LTC and XRP at the 5% significance level, respec-
tively, indicating that BitUSD can act as a safe haven asset for traditional
cryptocurrencies (especially for BTC) in the 10% downward situations.
For gold-pegged stablecoins, we have the following findings: (i) the esti-
mated m2of XAUR for BTC is zero at the 10% significance level, suggesting
that XAUR can be a weak safe haven for BTC; (ii) the estimated coefficient
m1of DGD for BTC is 0.105 at the 5% significance level, which implies
DGD can be considered a strong safe haven for BTC; (iii) the estimated m1
and m3of XAUR and DGD for XRP are all significantly negative, implying
that they can act as a strong safe haven for XRP; and (iv) all estimated m1,
m2, and m3of gold-pegged stablecoins for LTC are insignificant, suggesting
that gold-pegged stablecoins are not safe havens for LTC.
Notably, there are significant differences among the three USD-pegged
stablecoins with regard to the diversifier, hedge, and safe haven properties.
Tether can be regarded as a hedge on average, but as a diversifier in extreme
conditions. Although BitUSD co-moves with traditional cryptocurrencies, it
is a strong safe haven at certain quantiles. NuBits is neither a hedge nor a safe
haven against traditional cryptocurrencies. One convincing explanation for
these differences may be that different USD-pegged stablecoins actually have
very different blockchain algorithms, though they are backed by the same
asset (i.e., USD). Namely, Tether is fiat-collateralized, BitUSD is crypto-
collateralized, and NuBits is basically algorithmic and non-collateralized. D-
ifferent algorithms, levels of transparency, and market positions may result
in various indications.
The reason stablecoins sometimes act as safe havens for traditional cryp-
tocurrencies is worth pondering. A possible explanation is that when extreme
downward market conditions occur, investors transfer their money from tra-
ditional cryptocurrencies to some stablecoins and this leads to the appreci-
ation of these stablecoins. Another potential interpretation could be that
when negative shocks appear, investors transfer their money from cryptocur-
rencies to traditional safe haven assets (e.g., USD and gold) to reduce risks,
which leads to the price increase of USD and gold, and then accordingly
causes the appreciation of corresponding stablecoins.
18
4.2.2. Subperiod analysis
Table 7shows the estimated coefficients of Eq. (8) during three subperiods.
We find that the diversifier, hedge, and safe haven properties of stablecoin-
s (i.e., USD-pegged stablecoins) are not static and change across different
subperiods. Specifically, during subperiod 1, the estimated coefficients m0of
Tether are negative for all traditional cryptocurrencies at the 1% significance
level, which are 0.016 for BTC, 0.004 for LTC, and 0.006 for XRP. The
estimates of BitUSD and NuBits are all positive at the 1% significance lev-
el. These results confirm the findings in the full-sample analysis, including
the hedge role of Tether and the diversifier property of BitUSD and NuBits
against traditional cryptocurrencies. However, the safe haven property of
USD-pegged stablecoins are disappointing during subperiod 1, because none
of them can be regarded as a weak or strong safe haven against extreme
downside movements of the three traditional cryptocurrencies.
Since subperiod 1 witnessed the boom of cryptocurrencies and a major
increase in blockchain investments, returns on cryptocurrencies during this
stage were quite fruitful. In contrast to the sharp rise of traditional cryp-
tocurrencies, the prices of stablecoins are much less volatile. Therefore, the
correlations between the two kinds of assets are smaller than those in the full
sample period. On the other hand, cryptocurrencies barely show any move-
ment or dramatic decline in this subperiod, and stablecoins cannot exert
their safe haven function against extreme downside risks.
During subperiod 2, the estimated coefficients m0of Tether are 0.018
for BTC, 0.072 for LTC and 0.046 for XRP, showing that Tether acts as
a strong hedge for BTC and LTC. BitUSD shows no hedge effectiveness for
three traditional cryptocurrencies in this subperiod because all estimates of
m0are significantly positive. NuBits is an effective diversifier for BTC and
XRP, and it acts as a strong hedge for LTC because its estimated coefficient
m0is 0.014 at the 1% significance level.
When it comes to extreme downward situations during subperiod 2, the
estimated coefficients m1,m2, and m3of Tether are insignificant for all tra-
ditional cryptocurrencies, indicating that Tether has no safe haven function
during this crisis period. BitUSD appears to be a strong safe haven for XRP
with a negative estimated m3(i.e., 0.036) at the 1% significance level. Nu-
Bits can be regarded as a weak safe haven asset for XRP in the 10% and 5%
extreme downward conditions because the estimates of m1and m2equal to
zero at the 5% significance level.
19
Because traditional cryptocurrencies experienced dramatic declines dur-
ing subperiod 2, the results of this stage reveal the diversifier, hedge, and
safe haven properties of USD-pegged stablecoins under an extreme down-
ward market condition. During this crisis period, none of the three stable-
coins can be regarded as a strong safe haven for BTC or LTC. Since BTC is
the most influential cryptocurrency, its price behavior has a great impact on
the other digital currency assets. Thus, the sharp decline of BTC influences
the behavior of stablecoins and eliminates their effectiveness in diversifying
extreme downside risk. Nevertheless, BitUSD and NuBits have a safe haven
function for XRP. This finding implies that during times of crisis, XRP in-
vestors could invest in USD-pegged stablecoins to avoid risk. In terms of
international influence and liquidity, USD is the most powerful currency in
the world currency system, and it is widely acknowledged as a safe haven as-
set (see, e.g., Wen & Cheng,2018). Therefore, USD-pegged stablecoins are
always regarded as virtual asset with stable intrinsic value. Investors may
have the motivation to turn to USD-pegged stablecoins when XRP meets
adverse movements, and the increasing demand would push up the prices of
stablecoins.
During subperiod 3, the cryptocurrency market gradually rebounded and
became less fluctuant than the first two stages. The estimated coefficients m0
of Tether are 0.013 for BTC, 0.025 for LTC, and 0.036 for XRP, which
indicate that Tether can be regarded as a strong hedge against XRP and
LTC, and it is only a diversifier for BTC. Regarding BitUSD, the estimates
of m0are 0.144 for BTC, 0.195 for LTC, and 0.171 for XRP. These three
estimates are significant at the 1% level, suggesting that BitUSD is no more
than an effective diversifier for traditional cryptocurrencies on average. Like
BitUSD, the estimated coefficients m0of NuBits are all positive at the 1%
significance level, which implies that NuBits is also a diversifier for tradition-
al cryptocurrencies on average. As for extreme downward situations, most
estimates of m1,m2, and m3are insignificant. The only exception is the
estimated coefficient m3of BitUSD for BTC, which is 0.118 at the 10%
significance level. This indicates the strong safe haven property of BitUSD
for BTC at the 1% extreme downward quantile.
The subperiod results are consistent with those of the full sample analysis,
but they also indicate that the market condition matters to the diversifier,
hedge, and safe haven properties of USD-pegged stablecoins. Our main find-
ings are as follows: (i) Tether is a strong hedge in most conditions except
for BTC in subperiod 3 and for XRP in subperiod 2. There is no significant
20
estimate of Tether as a safe haven in any subperiods, indicating that Tether
has no safe haven property in extreme downward conditions. (ii) BitUSD is
a diversifier in all conditions but shows a strong safe haven property for XRP
at the 1% quantile in the crisis period and for BTC at the same quantile in
subperiod 3. (iii) In most situations, NuBits is a diversifier except for LTC
in subperiod 2, and it can be regarded as a weak safe haven for XRP at the
10% and 5% quantiles in the crisis period.
4.3. Evidence for USD and gold
Next, we build regression models to compare the diversifier, hedge, and safe
haven properties of stablecoins with those of their underlying assets, USD
and gold. Table 5shows the estimated results of USD and gold from the
dummy variable regression (i.e., Eq. (8)).
The estimated m0of USD are 0.010 for BTC, 0.042 for LTC, and 0.040
for XRP at the 1% significance level, indicating that USD can be a strong
hedge for BTC and XRP, but can be no more than an effective diversifier
against LTC. The estimated m0of gold is 0.050 for BTC, 0.020 for LTC,
and 0.076 for XRP, which implies that gold is a strong hedge for BTC and
is a diversifier against LTC and XRP.
For extreme negative returns on traditional cryptocurrencies, the esti-
mated coefficients of USD are zero for BTC and LTC in the 5% extreme
market condition (i.e., m2), and for XRP at the 10% and 1% quantiles (i.e.,
m1and m3) at the 5% significance level. However, all estimates m1,m2, and
m3of Gold are not significant at any levels. There are several interesting
findings from the above results. (i) USD performs a little better than gold
in the hedge function, because USD can be regarded as a strong hedge for
BTC and LTC, while gold is a strong hedge only for BTC. (ii) The two tradi-
tional assets have limited safe haven effects for traditional cryptocurrencies.
USD can only act as a weak safe haven for BTC, LTC, and XRP for certain
quantiles and gold is no more than a diversifier in all situations. In some
way, this means that returns of USD are not impacted by extreme downward
risks of cryptocurrencies. (iii) USD serves as a better hedge than BitUSD
and NuBits, but a worse one than Tether, while its safe haven properties are
not as strong as that of BitUSD. This shows that USD has closer connections
with BTC, LTC, and XRP on average than Tether, and has closer connec-
tions with cryptocurrencies during the crisis period than BitUSD. (iv) Gold
does act as a better hedge tool for traditional cryptocurrencies than stable-
coins it backs. However, we find that gold is not a safe haven for traditional
21
cryptocurrencies.
On the whole, stablecoins act better than their underlying assets as safe
havens. The better safe haven property of stablecoins may be because sta-
blecoins were introduced due to the huge price fluctuations of traditional
cryptocurrencies, which serve as a medium of exchange between the world-
wide digital currency and the fiat currency. Thus, it is more convenient for
investors to transfer their money from traditional cryptocurrencies to sta-
blecoins than to USD or gold due to lower transaction fees and the basic
blockchain technology they share.
5. Implications for risk management
To understand whether stablecoins function as a risk-dispersion tool in ex-
treme market events and reduce the losses for investors, we construct differ-
ent asset combinations and apply the extreme value theory (EVT) approach
to the following portfolio analysis. Under the EVT framework, we use the
peaks-over-threshold (POT) method with the generalized pareto distribution
(GPD) to calculate the value-at-risk (VaR) and excepted shortfall (ES) of the
portfolios under study.
First, we consider a portfolio, called portfolio 1, obtained by minimizing
the risk of a cryptocurrency-stablecoin portfolio without reducing the ex-
pected return. Following Kroner & Ng (1998) and Reboredo (2013b), the
optimal weight of the stablecoin in portfolio 1 at time tis specified as
ωS
t=hC
thSC
t
hS
t2hSC
t+hC
t
(9)
where ωS
tis between 0 and 1. Specifically, if ωS
t>1, ωS
t= 1, and if ωS
t<0,
ωS
t= 0. hS
t,hC
tand hSC
tare the conditional volatility of a stablecoin, the
conditional volatility of a traditional cryptocurrency and the conditional co-
variance between them at time t, respectively. ωS
tis the weight of a stablecoin
and the weight of a traditional cryptocurrency is equal to 1 ωS
t.
Besides portfolio 1, we build a passively managed portfolio 2, which gives
the same weight to a stablecoin and a traditional cryptocurrency, and the
weight at time tis specified as
ωS
t=1
2(10)
22
Given a portfolio composed of a traditional cryptocurrency and a stable-
coin, its return can be expressed as
rt= log(ωS
terS
t+ (1 ωS
t)erC
t) (11)
where rS
t,rC
t, and rtare returns of a stablecoin, a traditional cryptocurrency
and their portfolio, respectively.
The next step is to calculate VaR and ES of the three traditional cryp-
tocurrencies as well as portfolios established before. We employ the POT
approach from Davison & Smith (1990) to measure extreme risks for the tra-
ditional cryptocurrencies and portfolios under study. Practically, the mea-
surement comprises two steps: first, a GPD is used to fit the excess distri-
bution of the returns of each asset (traditional cryptocurrency and portfolio)
with the POT method to extract extremes; second, two extreme risk mea-
sures VaR and ES are estimated as extreme quantiles of the GPD.
According to the POT approach, the excess distribution of returns rt
based on the threshold ηcan be approximated by the GPD as:
Gξ,ψ(η)(x) = 1 1 + ξx
ψ(η)1
(12)
where xrepresents the exceedance over the threshold η,ξis the shape pa-
rameter, and ψ(η)>0 is the scale parameter. The threshold is selected to
ensure that a given percentage of extremes lies above it.
The estimated parameters in Eq. (12) are applied to compute the two
risk measurements VaR and ES as:
VaRq=η
ψ(η)
ξ1T
Nη
(1 q)ξ(13)
and
ESq=VaRq
1ξ+ψ(η)ξη
1ξ(14)
where VaRqrepresents the q-th quantile of returns rt, ESqrepresents the
expected loss under the condition that rt>VaRq,Trepresents the sample
size of returns, and Nηis the number of exceedances over the threshold η.
The final step is to compare the VaR and ES of a cryptocurrency-stablecoin
portfolio, including a traditional cryptocurrency and a stablecoin with those
of only a traditional cryptocurrency in extreme markets, so that we can know
23
whether the stablecoin really plays a safe haven role against the traditional
cryptocurrency. To make the calculation results easier to compare, we in-
troduce the VaR or ES reduction measure, which is the difference between
the VaR or ES of a traditional cryptocurrency and that of a corresponding
cryptocurrency-stablecoin portfolio.
Tables 79show the reductions of VaR and ES provided by portfolio 1 and
portfolio 2 at the 95%, 99%, and 99.9% confidence levels (corresponding to
the 5%, 1% and 0.1% risk levels), which represent the value of loss reduction
of USD-pegged stablecoins and gold-pegged ones against three traditional
cryptocurrencies. For comparison, we also present results of the underlying-
assets (i.e., USD and gold) in Tables 79. We find that reductions of portfolio
1 are much larger than that of portfolio 2, meaning that a positive strategy
is better than a passive one that simply divides weights of two assets evenly.
Under the combination of USD-pegged stablecoins, almost all VaR and
ES reductions of portfolios are positive, which means that they do help to re-
duce the investors’ losses in extreme downward markets. In addition, Tether
provides larger downside risk reductions than NuBits and BitUSD in almost
all cases against traditional cryptocurrencies, which implies that Tether is
a better safe haven. However, note that the BTC–BitUSD portfolio does
not perform well at the 99.9% confidence level with the negative reductions
of VaR and ES, indicating limited risk-reduction effects of BitUSD against
BTC in the most extremely falling market.
Portfolios containing gold-pegged stablecoins perform much worse than
those including USD-pegged ones, which may partly reflect the fact that
USD is more stable than gold. Only the LTC–XAUR, XRP–XAUR, and
XRP–DGD portfolios show a few positive VaR and ES reductions at differ-
ent confidence levels. This is consistent with our previous analysis to some
extent, of which we detect the safe haven property of DGD and XAUR at
specific quantiles. But note that VaR and ES reductions of gold-pegged
stablecoins are not as much as those of their underlying asset, gold.
USD-pegged stablecoins provide the highest downside risk reductions for
XRP at 99% and 99.9% confidence levels and the lowest for BTC at all con-
fidence levels. Among them, Tether provides the largest reductions that are
close to USD and higher than gold. Especially, the VaR and ES reduction-
s of the XRP–Tether portfolio are large as 0.295 and 0.404 at the 99.9%
confidence level.
The above results show that no other stablecoins perform better than
Tether in acting as a safe haven against traditional cryptocurrencies. Besides,
24
Tether also shows a good value of hedge based on the evaluation of DCCs.
Combining both of the conclusions, there is evidence that Tether is absolutely
a good asset for risk management on both normal and extreme circumstances.
6. Robustness test
To check the robustness of our results on different dynamic correlation ap-
proaches, we use time-varying copulas from Patton (2006) to model the dy-
namic dependence structure between stablecoins and traditional cryptocur-
rencies.7Based on dynamic dependence coefficients, we then use the dummy
variable regression from Eq. (8) to calculate the coefficients that can reflect
the diversifier, hedge, and safe haven functions of stablecoins against tradi-
tional cryptocurrencies. As we can see from Table 10, the results of Eq. (8)
based on time-varying copulas are consistent with those of DCC models. The
estimated coefficients m0of Tether are 0.012 for BTC and 0.047 for LTC,
indicating that Tether behaved as a strong hedge against BTC and LTC.
While the other stablecoins can be regarded as no more than an effective
diversifier against traditional cryptocurrencies since their estimated m0are
all positive.
The consistency of the two dynamic correlation approaches also holds
for the safe haven property of stablecoins against traditional cryptocurren-
cies. For USD-pegged stablecoins, the estimated coefficients m3of Tether
and NuBits are 0.034 and 0.085 for XRP at the 5% significance level, re-
spectively, which implies that Tether and NuBits are both strong safe havens
against XRP in the 1% extreme downside market. The estimated m1of Bi-
tUSD are 0.04 for BTC at the 1% significance level and 0.011 for XRP
at the 10% significance level, implying that BitUSD can act as a strong safe
haven against BTC and XRP in the 10% downside market. For gold-pegged
stablecoins, the estimated coefficients m1,m2and m3are all positive under
all circumstances of the quantile. In summary, USD-pegged stablecoins can
serve as safe havens against traditional cryptocurrencies under specific situ-
ations, while gold-pegged stablecoins show no safe haven property under any
condition. Namely, the results using time-varying copulas are robust and
consistent with our central findings.
7The fitting results of dynamic copula models are reported in the Tables A7A9 in the
Online Appendix.
25
7. Conclusions
In this study, we have investigated the diversifier, hedge, and safe haven
properties of stablecoins against traditional cryptocurrencies. We first used
the ARMA-GARCH models for fitting marginal distributions of each return
series under study, and then employed the DCC model to obtain dynamic
conditional correlations between stablecoins and traditional cryptocurren-
cies. We next established dummy variable regressions to examine the poten-
tial properties of stablecoins against traditional cryptocurrencies using their
dynamic conditional correlations. Both the full-period and subperiod analy-
ses were conducted with these methods. To test the loss-reduction abilities
of stablecoins from a risk management perspective, we calculated VaR and
ES reductions, defined as the differences between VaR and ES of a tradition-
al cryptocurrency and a cryptocurrency-stablecoin portfolio, respectively. In
addition, we also compared the results of the underlying assets (i.e., USD and
gold) with those of stablecoins. Our sample data were daily closing prices
for three USD-pegged stablecoins (i.e., Tether, BitUSD, and NuBits), three
gold-pegged stablecoins (i.e., DGD, HGT, and XAUR), and three traditional
cryptocurrencies (i.e., Bitcoin, Litecoin, and Ripple). The sample periods
are from March 6, 2015 and October 13, 2017 to March 20, 2019 for analysis
of USD-pegged stablecoins and gold-pegged ones, respectively.
We have four major findings as follows. (i) USD-pegged stablecoins have
better risk-dispersion abilities for traditional cryptocurrencies than gold-
pegged ones according to dynamic conditional correlations. Among USD-
pegged stablecoins, Tether shows the best properties of risk diversification,
while BitUSD and NuBits are positive-related to traditional cryptocurrencies
in most of the time. (ii) Based on the dummy variable regression, we find
that Tether can be regarded as a strong hedge for all the three traditional
cryptocurrencies from the full-period analysis, while each of other stablecoin-
s is no more than an effective diversifier. However, some of them do show
the safe haven property under certain quantile levels. For instance, BitUSD
is a strong safe haven against three traditional cryptocurrencies at the 10%
quantile and some gold-pegged stablecoins also perform well at certain quan-
tiles. Comparing the results of full-period analysis with the subperiod one,
we find that the market condition matters to the risk-dispersion effectiveness
of USD-pegged stablecoins. (iii) We find gold acts better as a hedge than the
stablecoins it backs, but lacks in the power of safe haven. USD is a better
hedge than two of the USD-pegged stablecoins, but it is not as good a safe
26
haven as the stablecoins it backs. (iv) From the risk management perspec-
tive, we find that gold-pegged stablecoins hardly have any function in VaR
and ES reductions, while their underlying asset gold helps eliminate extreme
risks to some extent. Both the USD-pegged stablecoins and USD can be
regarded as great tools in reducing extreme losses.
In summary, our work sheds light on the diversifier, hedge, and safe haven
properties of USD-pegged and gold-pegged stablecoins against traditional
cryptocurrencies from various perspectives, and provides virtual currency in-
vestors with comprehensive risk management analysis in normal and extreme
downside market conditions. Since the literature on the potential roles (e.g.,
diversifiers, hedges, or safe haven) of stablecoins is scarce and still evolving,
there are three possible directions for future research. The first is to study
the hedging ability of stablecoins against traditional assets in different coun-
tries. The second is to investigate the safe haven property of stablecoins
during political and economic turmoil. The third is to further compare the
potential roles of stablecoins with traditional safe haven assets such as gold
and USD, in order to explore whether they compete with or complement
each other.
Acknowledgements
This work was supported by the National Natural Science Foundation of
China (Grant nos. 71871088, 71501066, 71971079, 71850006, and 71521061),
the Huxiang Youth Talent Support Program, and the Hunan Provincial Nat-
ural Science Foundation of China (Grant no. 2017JJ3024).
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Table 1: Introduction to USD-pegged stablecoins.
Tether BitUSD NuBits
Tickers USDT, EURT BITUSD USNBT
Launch Date 2014 2014 2014
Market Cap $2,803,538,394 $4,468,672 $587,517
Top-Level Category Asset-Collateralized Asset-
Collateralized
Algorithmic
Reference Peg USD USD USD
Country Location Distributed US Sweden
Platform Omni Protocol Unknown Unknown
Table 2: Introduction to gold-pegged stablecoins.
HelloGold DigixDAO Xaurum
Tickers GOLDX, HGT DGD XAUR
Launch Date 2017 Early 2014 2016
Market Cap $206,641 $76,042,442 $2,824,259
Top-Level Category Asset-Collateralized Asset-Collateralized Asset-
Collateralized
Reference Peg 1g of gold 1g of gold 1g of gold
Country Location Malaysia Singapore Slovenia
Platform Ethereum Ethereum Ethereum
33
Table 3: Descriptive statistics of returns for two samples.
Obs. Mean Std. dev. Min. Max. Skewness Kurtosis Jarque-Bera ADF
Panel A: USD-pegged sample
Tether 1438 0.000 0.014 0.047 0.500 29.125 1011.361 61126098***
99.939***
BitUSD 1438 0.000 0.057 0.772 0.734 0.436 83.911 392291***
28.057***
NuBits 1438 0.002 0.058 0.358 0.924 3.450 61.411 207278***
17.706***
BTC 1438 0.002 0.038 0.208 0.225 0.298 8.472 1815***
37.942***
LTC 1438 0.002 0.059 0.514 0.510 0.799 17.601 12927***
38.008***
XRP 1438 0.002 0.072 0.616 1.027 3.086 45.073 108341***
23.437***
USD 1042 0.000 0.000 0.021 0.025 0.098 5.073 188***
32.184***
Panel B: Gold-pegged sample
DGD 524 0.003 0.077 0.460 0.580 0.404 12.148 1841***
23.313***
HGT 524 0.005 0.201 0.918 1.249 0.657 7.833 547***
20.444***
XAUR 524 0.003 0.070 0.296 0.544 0.477 10.645 1296***
28.967***
BTC 524 0.001 0.044 0.185 0.225 0.003 6.049 202***
22.278***
LTC 524 0.000 0.027 0.092 0.169 1.212 9.988 1194***
22.465***
XRP 524 0.000 0.075 0.353 0.607 1.945 18.147 5339***
13.605***
Gold 360 0.000 0.006 0.020 0.024 0.015 3.992 15***
20.492***
Notes: Jarque-Bera statistic tests for the null hypothesis of normal distribution. ADF is a statistic of
the Augmented Dickey-Fuller test for a unit root. *** denotes the rejection of null hypothesis at the 1%
significance level.
Table 4: Breakpoints in returns of three traditional cryptocurrencies (i.e., BTC, LTC, and
XRP) from March 6, 2015 to March 20, 2019.
Break BTC LTC XRP
1 25/03/2017 28/03/2017 18/03/2017
2 17/12/2017 19/12/2017 08/01/2018
34
Table 5: Estimation results on the diversifier, hedge, and safe haven properties of stable-
coins against traditional cryptocurrencies (i.e., BTC, LTC, and XRP) in the entire period
based on DCC-GARCH models.
Hedge (m0) 10% quantile (m1) 5% quantile (m2) 1% quantile (m3)
Panel A: Potential roles of stablecoins against BTC
USD-pegged Tether 0.004**
0.009 0.019*0.007
BitUSD 0.152***
0.060*** 0.026 0.010
NuBits 0.192*** 0.016 0.001 0.023
USD 0.010*** 0.000 0.000** 0.000
Gold-pegged DGD 0.580***
0.026 0.068 0.105**
XAUR 0.346*** 0.000 0.000*0.000
HGD 0.230***
0.005 0.015 0.001
Gold 0.050*** 0.000 0.000 0.000
Panel B: Potential roles of stablecoins against LTC
USD-pegged Tether 0.019***
0.012 0.034**
0.004
BitUSD 0.149***
0.031** 0.023 0.013
NuBits 0.151*** 0.013 0.004 0.003
USD 0.042*** 0.000 0.000** 0.000
Gold-pegged DGD 0.597***
0.080*0.023 0.080
XAUR 0.345***
0.014 0.001 0.017
HGD 0.271***
0.002 0.025 0.004
Gold 0.020*** 0.000 0.000 0.000
Panel C: Potential roles of stablecoins against XRP
USD-pegged Tether 0.017*** 0.000 0.000 0.000
BitUSD 0.107***
0.024** 0.027 0.038
NuBits 0.147***
0.001 0.005 0.028
USD 0.040*** 0.000** 0.000 0.000**
Gold-pegged DGD 0.553***
0.117*** 0.032 0.170*
XAUR 0.314***
0.050*0.030 0.130**
HGD 0.214***
0.002 0.002 0.007
Gold 0.076***
0.013 0.047 0.009
Notes: ***,** , and *denote the rejection of null hypothesis at the 1%, 5%, and 10% significance
levels, respectively.
35
Table 6: Estimation of diversifier, hedge, and safe haven properties of stablecoins against traditional cryptocurrencies (i.e., BTC, LTC, and
XRP) from the subperiod analysis.
BTC LTC XRP
m0m1m2m3m0m1m2m3m0m1m2m3
Panel A: Subperiod 1
Tether 0.016*** 0.000 0.003 0.014 0.004*** 0.007 0.007 0.009 0.006*** 0.001 0.003 0.003
BitUSD 0.166*** 0.005 0.001 0.022 0.095*** 0.000 0.000 0.000 0.035*** 0.000 0.003 0.002
NuBits 0.039***
0.010 0.001 0.073 0.014***
0.009 0.016*0.013 0.035*** 0.002 0.003 0.011
Panel B: Subperiod 2
Tether 0.018**
0.009 0.045 0.040 0.072*** 0.031 0.043 0.045 0.046*** 0.000 0.000 0.000
BitUSD 0.012*** 0.000 0.000 0.000 0.205*** 0.002 0.003 0.030 0.213*** 0.008 0.001 0.036***
NuBits 0.046*** 0.000 0.000 0.000 0.014*** 0.000 0.000 0.000 0.021*** 0.000** 0.000*** 0.000
Panel C: Subperiod 3
Tether 0.013*0.009 0.005 0.005 0.025*** 0.017 0.022 0.021 0.036*** 0.003 0.001 0.095
BitUSD 0.144***
0.116 0.096 0.118*0.195***
0.056 0.042 0.059 0.171***
0.071 0.015 0.003
NuBits 0.367*** 0.009 0.017 0.011 0.338*** 0.043 0.001 0.002 0.331*** 0.011 0.022 0.006
Notes: ***,** , and *denote the rejection of null hypothesis at the 1%, 5%, and 10% significance levels, respectively.
36
Table 7: Downside risk evaluation for portfolios against BTC.
Tether BitUSD NuBits USD DGD HGT XAUR Gold
Portfolio 1
VaR reduction (95%) 0.053 0.019 0.021 0.059 0.007 0.009 0.003 0.067
ES reduction (95%) 0.078 0.027 0.022 0.088 0.013 0.013 0.004 0.095
VaR reduction (99%) 0.094 0.036 0.021 0.107 0.016 0.017 0.004 0.113
ES reduction (99%) 0.114 0.031 0.028 0.134 0.024 0.011 0.009 0.138
VaR reduction (99.9%) 0.139 0.022 0.037 0.170 0.035 0.001 0.016 0.171
ES reduction (99.9%) 0.149 0.031 0.049 0.188 0.045 0.010 0.023 0.193
Portfolio 2
VaR reduction (95%) 0.032 0.016 0.007 0.034 0.017 0.071 0.058 0.041
ES reduction (95%) 0.049 0.019 0.004 0.051 0.024 0.100 0.002 0.057
VaR reduction (99%) 0.059 0.027 0.000 0.061 0.023 0.117 0.004 0.066
ES reduction (99%) 0.077 0.004 0.006 0.081 0.052 0.143 0.008 0.082
VaR reduction (99.9%) 0.100 0.025 0.015 0.109 0.092 0.177 0.013 0.104
ES reduction (99.9%) 0.118 0.120 0.028 0.133 0.168 0.201 0.017 0.121
37
Table 8: Downside risk evaluation for portfolios against LTC.
Tether BitUSD NuBits USD DGD HGT XAUR Gold
Portfolio 1
VaR reduction (95%) 0.072 0.031 0.031 0.079 0.018 0.015 0.013 0.030
ES reduction (95%) 0.109 0.049 0.041 0.126 0.024 0.017 0.015 0.044
VaR reduction (99%) 0.127 0.061 0.041 0.149 0.027 0.018 0.017 0.054
ES reduction (99%) 0.183 0.075 0.074 0.218 0.034 0.016 0.017 0.062
VaR reduction (99.9%) 0.257 0.097 0.120 0.309 0.043 0.012 0.018 0.071
ES reduction (99.9%) 0.355 0.084 0.206 0.428 0.050 0.009 0.019 0.074
Portfolio 2
VaR reduction (95%) 0.042 0.028 0.016 0.045 0.040 0.109 0.037 0.019
ES reduction (95%) 0.065 0.036 0.023 0.072 0.054 0.145 0.077 0.030
VaR reduction (99%) 0.076 0.051 0.021 0.084 0.057 0.165 0.103 0.037
ES reduction (99%) 0.114 0.034 0.056 0.126 0.097 0.213 0.139 0.041
VaR reduction (99.9%) 0.166 0.028 0.105 0.181 0.151 0.279 0.187 0.046
ES reduction (99.9%) 0.242 0.122 0.197 0.258 0.252 0.342 0.218 0.047
Table 9: Downside risk evaluation for portfolios against XRP.
Tether BitUSD NuBits USD DGD HGT XAUR Gold
Portfolio 1
VaR reduction (95%) 0.067 0.027 0.029 0.077 0.001 0.012 0.008 0.091
ES reduction (95%) 0.114 0.057 0.051 0.131 0.012 0.012 0.022 0.150
VaR reduction (99%) 0.138 0.072 0.058 0.154 0.014 0.020 0.025 0.184
ES reduction (99%) 0.205 0.111 0.106 0.243 0.049 0.033 0.066 0.253
VaR reduction (99.9%) 0.295 0.163 0.173 0.361 0.098 0.111 0.124 0.344
ES reduction (99.9%) 0.404 0.221 0.271 0.541 0.185 0.229 0.221 0.428
Portfolio 2
VaR reduction (95%) 0.040 0.023 0.011 0.044 0.002 0.062 0.006 0.053
ES reduction (95%) 0.069 0.043 0.029 0.074 0.008 0.071 0.025 0.087
VaR reduction (99%) 0.084 0.057 0.035 0.087 0.010 0.082 0.031 0.106
ES reduction (99%) 0.126 0.071 0.078 0.140 0.034 0.065 0.078 0.147
VaR reduction (99.9%) 0.181 0.093 0.138 0.210 0.067 0.040 0.144 0.201
ES reduction (99.9%) 0.250 0.079 0.230 0.326 0.128 0.031 0.247 0.253
38
Table 10: Estimation results on the diversifier, hedge, and safe haven properties of stable-
coins against traditional cryptocurrencies (i.e., BTC, LTC, and XRP) in the entire period
based on time-varying copulas.
Hedge (m0) 10% quantile (m1) 5% quantile (m2) 1% quantile (m3)
Panel A: Potential roles of stablecoins against BTC
USD-pegged Tether 0.012*** 0.002 0.011 0.006
BitUSD 0.181***
0.040*** 0.027 0.029
NuBits 0.144*** 0.024 0.008 0.039
Gold-pegged DGD 0.559***
0.017 0.033 0.017
HGT 0.258***
0.104 0.003 0.001
XAUR 0.415*** 0.033 0.072 0.093
Panel B: Potential roles of stablecoins against LTC
USD-pegged Tether 0.047***
0.012 0.031** 0.000
BitUSD 0.177***
0.005 0.008 0.002
NuBits 0.105*** 0.046***
0.035 0.032
Gold-pegged DGD 0.641***
0.015 0.021 0.015
HGT 0.308***
0.008 0.004 0.012
XAUR 0.390*** 0.003 0.007 0.008
Panel C: Potential roles of stablecoins against XRP
USD-pegged Tether 0.000***
0.008 0.002 0.034**
BitUSD 0.178***
0.011*0.011 0.015
NuBits 0.141*** 0.006 0.008 0.085**
Gold-pegged DGD 0.581***
0.032 0.004 0.096
HGT 0.247*** 0.001 0.004 0.000
XAUR 0.334*** 0.003 0.003 0.014
Notes: ***,** , and *denote the rejection of null hypothesis at the 1%, 5%, and 10% significance
levels, respectively.
39
2013-12 2014-12 2015-12 2016-12 2017-12 2018-12
Time
0
0.5
1
1.5
2
Close price
104(a) BTC
2013-12 2014-12 2015-12 2016-12 2017-12 2018-12
Time
0
100
200
300
400
Close price
(b) LTC
2013-12 2014-12 2015-12 2016-12 2017-12 2018-12
Time
0
0.5
1
1.5
2
2.5
3
3.5
Close price
(c) XRP
Figure 1: Daily closing prices of BTC, LTC, and XRP from August 5, 2013 to March 20,
2019. Source: coinmarketcap.com.
2016 2017 2018 2019
Time
-0.5
0
0.5
DCC
(a) BTC & Tether
2016 2017 2018 2019
Time
-0.5
0
0.5
DCC
(b) BTC & BitUSD
2016 2017 2018 2019
Time
-0.5
0
0.5
1
DCC
(c) BTC & NuBits
2016 2017 2018 2019
Time
-1
-0.5
0
0.5
DCC
(d) LTC & Tether
2016 2017 2018 2019
Time
-0.2
0
0.2
0.4
0.6
DCC
(e) LTC & BitUSD
2016 2017 2018 2019
Time
-0.2
0
0.2
0.4
DCC
(f) LTC & NuBits
2016 2017 2018 2019
Time
-0.02
-0.015
-0.01
-0.005
0
DCC
(g) XRP & Tether
2016 2017 2018 2019
Time
-0.0175
-0.017
-0.0165
DCC
2016 2017 2018 2019
Time
-0.4
-0.2
0
0.2
0.4
DCC
(h) XRP & BitUSD
2016 2017 2018 2019
Time
-0.2
0
0.2
0.4
DCC
(i) XRP & NuBits
Figure 2: DCCs between USD-pegged stablecoins and traditional cryptocurrencies from
full sample analysis. Notes: Because the change of DCCs between XRP and Tether is not
obvious in the range [0.02,0], we insert a secondary subgraph into panel (g) to magnify
the fluctuation of DCCs.
40
2017-12 2018-06 2018-12
Time
0
0.5
1
DCC
(a) DGD & BTC
2017-12 2018-06 2018-12
Time
0
0.1
0.2
0.3
0.4
DCC
(b) HGT & BTC
2017-12 2018-06 2018-12
Time
0
0.1
0.2
0.3
0.4
DCC
(c) XAUR & BTC
2017-12 2018-06 2018-12
Time
0.3462
0.3464
0.3466
DCC
2017-12 2018-06 2018-12
Time
-0.5
0
0.5
1
DCC
(d) DGD & LTC
2017-12 2018-06 2018-12
Time
0
0.2
0.4
0.6
DCC
(e) HGT & LTC
2017-12 2018-06 2018-12
Time
0
0.2
0.4
0.6
DCC
(f) XAUR & LTC
2017-12 2018-06 2018-12
Time
-0.5
0
0.5
1
DCC
(g) DGD & XRP
2017-12 2018-06 2018-12
Time
0
0.1
0.2
0.3
DCC
(h) HGT & XRP
2017-12 2018-06 2018-12
Time
0
0.2
0.4
0.6
DCC
(i) XAUR & XRP
Figure 3: DCCs between Gold-pegged stablecoins and traditional cryptocurrencies from
full sample analysis. Notes for this figure: Because the change of DCCs between BTC and
XAUR is not obvious in the range [0,0.4], we insert a secondary subgraph into panel (c)
to magnify the fluctuation of DCCs.
2015-06 2015-12 2016-06 2016-12
Time
-0.2
-0.1
0
0.1
0.2
DCC
(a) Tether & BTC
Subperiod 1
2015-06 2015-12 2016-06 2016-12
Time
0
0.2
0.4
0.6
DCC
(b) BitUSD & BTC
Subperiod 1
2015-06 2015-12 2016-06 2016-12
Time
-0.5
0
0.5
1
DCC
(c) NuBits & BTC
Subperiod 1
2017-06 2017-12
Time
-0.4
-0.2
0
0.2
0.4
DCC
(d) Tether & BTC
Subperiod 2
2017-06 2017-12
Time
0
0.05
0.1
0.15
DCC
(e) BitUSD & BTC
Subperiod 2
2017-06 2017-12
Time
0.1168
0.117
0.1172
DCC
2017-06 2017-12
Time
0
0.02
0.04
0.06
DCC
(f) NuBits & BTC
Subperiod 2
2017-06 2017-12
Time
0.0454
0.0455
0.0456
DCC
2018-06 2018-12
Time
-0.5
0
0.5
DCC
(g) Tether & BTC
Subperiod 3
2018-06 2018-12
Time
-0.5
0
0.5
1
DCC
(h) BitUSD & BTC
Subperiod 3
2018-06 2018-12
Time
-0.5
0
0.5
1
DCC
(i) NuBits & BTC
Subperiod 3
Figure 4: DCCs between USD-pegged stablecoins and BTC from subperiod analysis.
Notes: Because the change of DCCs between BTC and BitUSD is not obvious in the
range [0,0.15], we insert a secondary subgraph into panel (e) to magnify the fluctuation of
DCCs. Similarly, we insert a secondary subgraph into panel (f) to magnify the fluctuation
of DCCs between BTC and NuBits.
41
2015-06 2015-12 2016-06 2016-12
Time
-0.1
-0.05
0
0.05
0.1
DCC
(a) Tether & LTC
Subperiod 1
2015-06 2015-12 2016-06 2016-12
Time
0
0.05
0.1
DCC
(b) BitUSD & LTC
Subperiod 1
2015-12 2016-12
Time
0.0946
0.0948
0.095
DCC
2015-06 2015-12 2016-06 2016-12
Time
-0.2
-0.1
0
0.1
0.2
DCC
(c) NuBits & LTC
Subperiod 1
2017-06 2017-12
Time
-0.5
0
0.5
DCC
(d) Tether & LTC
Subperiod 2
2017-06 2017-12
Time
-0.2
0
0.2
0.4
0.6
DCC
(e) BitUSD & LTC
Subperiod 2
2017-06 2017-12
Time
-0.015
-0.01
-0.005
0
DCC
(f) NuBits & LTC
Subperiod 2
2017-06 2017-12
Time
-0.0142
-0.014
-0.0138
DCC
2018-06 2018-12
Time
-0.5
0
0.5
DCC
(g) Tether & LTC
Subperiod 3
2018-06 2018-12
Time
-0.5
0
0.5
1
DCC
(h) BitUSD & LTC
Subperiod 3
2018-06 2018-12
Time
-0.2
0
0.2
0.4
0.6
DCC
(i) NuBits & LTC
Subperiod 3
Figure 5: DCCs between USD-pegged stablecoins and LTC from subperiod analysis.
Notes: Because the changes of DCCs between LTC and BitUSD is not obvious in the
range [0,0.1], we insert a secondary subgraph into panel (b) to magnify the fluctuation of
DCCs. Similarly, we insert a secondary subgraph into panel (f) to magnify the fluctuation
of DCCs between LTC and NuBits.
42
2015-06 2015-12 2016-06 2016-12
Time
-0.1
-0.05
0
0.05
DCC
(a) Tether & XRP
Subperiod 1
2015-06 2015-12 2016-06 2016-12
Time
-0.2
-0.1
0
0.1
0.2
DCC
(b) BitUSD & XRP
Subperiod 1
2015-06 2015-12 2016-06 2016-12
Time
-0.1
0
0.1
0.2
0.3
DCC
(c) NuBits & XRP
Subperiod 1
2017-06 2017-12
Time
0
0.02
0.04
0.06
DCC
(d) Tether & XRP
Subperiod 2
2017-06 2017-12
Time
0.046
0.0465
DCC
2017-06 2017-12
Time
-0.2
0
0.2
0.4
0.6
DCC
(e) BitUSD & XRP
Subperiod 2
2017-06 2017-12
Time
0
0.01
0.02
0.03
DCC
(f) NuBits & XRP
Subperiod 2
2017-06 2017-12
Time
0.0209
0.021
0.0211
DCC
2018-06 2018-12
Time
-1
-0.5
0
0.5
DCC
(g) Tether & XRP
Subperiod 3
2018-06 2018-12
Time
-0.5
0
0.5
1
DCC
(h) BitUSD & XRP
Subperiod 3
2018-06 2018-12
Time
-0.2
0
0.2
0.4
0.6
DCC
(i) NuBits & XRP
Subperiod 3
Figure 6: DCCs between USD-pegged stablecoins and XRP from subperiod analysis.
Notes: Because the change of DCCs between XRP and Tether is not obvious in the
range [0,0.06], we insert a secondary subgraph into panel (d) to magnify the fluctuation of
DCCs. Similarly, we insert a secondary subgraph into panel (f) to magnify the fluctuation
of DCCs between XRP and NuBits.
43
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The author considers the legal and economic nature of stablecoin, the development stages of low-volatility assets, describes the existing models of their implementation in the public and private sectors. Particular attention is paid to the assessment of legal risks of mono-secured and multi-secured cryptocurrency on the example of projects in Venezuela and Russia, as well as the possibility of using stablecoin for the development of the financial system and evasion from economic sanctions.
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