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Impact of commodities and global stock prices on the idiosyncratic risk of Bitcoin during the COVID-19 pandemic

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In times of exogenous systemic shocks, such as the COVID-19 pandemic, it is important to identify hedge or safe haven assets. Therefore, this paper analyzes changes in the idiosyncratic risk of Bitcoin in a portfolio of commodities and global stocks. For this purpose, the M-GARCH model employed considers the interdependence among all the portfolio assets by using a time-varying asset pricing framework. This framework measures the impact of commodities and global stock prices as sources of systemic risk for Bitcoin returns before and after the COVID-19 pandemic. The evidence suggests that during the COVID-19 pandemic, the effects of changes in commodities and global prices on the idiosyncratic risk of Bitcoin were statistically significant. The idiosyncratic risk of Bitcoin measured as a percentage of total variance not accounted for by the proposed model rose from 86.06% to 95.05% during the pandemic. These results are in line with previous studies regarding the properties of Bitcoin as a hedge or safe haven asset for a portfolio composed of commodities and global stocks.
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“Impact of commodities and global stock prices on the idiosyncratic risk of
Bitcoin during the COVID-19 pandemic”
AUTH ORS
Edgardo Cayón Fallon
Julio Sarmiento
ARTICLE INFO
Edgardo Cayón Fallon and Julio Sarmiento (2021). Impact of commodities and
global stock prices on the idiosyncratic risk of Bitcoin during the COVID-19
pandemic. Investment Management and Financial Innovations, 18(4), 213-222.
doi:10.21511/imfi.18(4).2021.19
DOI http://dx.doi.org/10.21511/imfi.18(4).2021.19
RELEASED ON Wednesday, 24 November 2021
RECE IVED ON Wednesday, 13 October 2021
ACCEPTED ON Tuesday, 16 November 2021
LICENSE
This work is licensed under a Creative Commons Attribution 4.0 International
License
JOURNAL "Investment Management and Financial Innovations"
ISSN PRINT 1810-4967
ISSN ONLINE 1812-9358
PUBLISHER LLC “Consulting Publishing Company “Business Perspectives”
FOUNDER LLC “Consulting Publishing Company “Business Perspectives”
NUMBER OF REFERENCES
37
NUMBER OF FIGURES
3
NUMBER OF TABLES
4
© The author(s) 2021. This publication is an open access article.
businessperspectives.org
213
Investment Management and Financial Innovations, Volume 18, Issue 4, 2021
http://dx.doi.org/10.21511/im.18(4).2021.19
Abstract
In times of exogenous systemic shocks, such as the COVID-19 pandemic, it is impor-
tant to identify hedge or safe haven assets. erefore, this paper analyzes changes in
the idiosyncratic risk of Bitcoin in a portfolio of commodities and global stocks. For
this purpose, the M-GARCH model employed considers the interdependence among
all the portfolio assets by using a time-varying asset pricing framework. is frame-
work measures the impact of commodities and global stock prices as sources of sys-
temic risk for Bitcoin returns before and aer the COVID-19 pandemic. e evidence
suggests that during the COVID-19 pandemic, the eects of changes in commodities
and global prices on the idiosyncratic risk of Bitcoin were statistically signicant. e
idiosyncratic risk of Bitcoin measured as a percentage of total variance not accounted
for by the proposed model rose from 86.06% to 95.05% during the pandemic. ese
results are in line with previous studies regarding the properties of Bitcoin as a hedge or
safe haven asset for a portfolio composed of commodities and global stocks.
Edgardo Cayón Fallon (Colombia), Julio Sarmiento (Colombia)
Impact of commodities
and global stock prices
on the idiosyncratic risk
of Bitcoin during
the COVID-19 pandemic
Received on: 13 of October, 2021
Accepted on: 16 of November, 2021
Published on: 24 of November, 2021
INTRODUCTION
It is no secret that cryptocurrencies, such as Bitcoin, represent a series
of opportunities and enigmas regarding what type of asset class they
resemble or if, indeed, they constitute a new asset class. For example,
Graf (2014) points out that academicians and practitioners are still ar-
guing about how to classify Bitcoin: as a commodity, intangible asset,
money, miscellaneous form, or private property. Graf (2014) makes
a convincing argument that Bitcoin is a form of “pure” commod-
ity money that, thanks to its intangible nature, takes the same role
as commodity-backed at monies that have existed through history.
Baldan and Zen (2020) and Hayes (2019) reinforce this argument by
arguing that Bitcoin can be treated as a “virtual commodity” because
it can be produced (Bitcoin miners) and can be acquired by individu-
als in dierent marketplaces, which is very similar to the process in
which physical commodities are produced and traded. However, there
is evidence that, even though Bitcoin theoretically shares similar char-
acteristics with the commodities market, it behaves as a unique asset
class in its own right. According to CoinMarketCap (n.d.), more than
40% of the total USD 1.9 trillion cryptocurrency market capitalization
is represented by Bitcoin which has become a new kind of nancial
asset actively sought by investors. Additionally, the trading volume in-
creased during the COVID-19 pandemic contrary to expectation even
to the point that there is suspicion of a possible price bubble (Guegan
& Frunza, 2020). Also, during the COVID-19 pandemic, the Bitcoin
© Edgardo Cayón Fallon, Julio
Sarmiento, 2021
Edgardo Cayón Fallon, Professor
of Finance, Finance Department,
CESA Business School, Colombia.
(Corresponding author)
Julio Sarmiento, Professor of Finance,
Ponticia Universidad Javeriana,
Colombia.
is is an Open Access article,
distributed under the terms of the
Creative Commons Attribution 4.0
International license, which permits
unrestricted re-use, distribution, and
reproduction in any medium, provided
the original work is properly cited.
www.businessperspectives.org
LLC “P “Business Perspectives
Hryhorii Skovoroda lane, 10,
Sumy, 40022, Ukraine
BUSINESS PERSPECTIVES
JEL Classification G11, G12, G15
Keywords Bitcoin, safe haven, COVID-19, idiosyncratic risk,
systemic risk, diversication
Conict of interest statement:
Author(s) reported no conict of interest
214
Investment Management and Financial Innovations, Volume 18, Issue 4, 2021
http://dx.doi.org/10.21511/im.18(4).2021.19
has seen its value increase while other traditional nancial assets and commonly traded commodities
have seen their value plummet. Due to its unique characteristics, it is important in an asset pricing con-
text, to see if indeed common global systemic factors can explain the variance in Bitcoin prices during
normal market conditions and if the eects on variance remain constant in times of exogenous systemic
shocks such as the COVID-19 pandemic. erefore the purpose of the study is to test the properties of
Bitcoin during the before mentioned periods and to test if indeed the Bitcoin exhibited the characteris-
tics of a safe haven asset during the COVID-19 pandemic.
1. LITERATURE REVIEW
Bouri et al. (2017) argued that Bitcoin can act
as a diversier for a portfolio of common asset
classes, such as commodities and global stocks.
Accordingly, the denitions suggested by Baur
and Lucey (2010) are used where a hedge asset is
dened as an asset with a negative correlation
with the rest of the portfolio, a diversier as an
asset with a positive correlation that has a rela-
tionship to the systemic risk of the portfolio, and
nally, a safe haven is an asset that is uncorrelat-
ed or negatively correlated with the portfolio in
times of increasing market volatility or systemic
risk. Shahzad et al. (2019) decided to test the hy-
pothesis of a “weak” versus “strong” safe haven
denition based on the predictability of the stock
market based on the previous variations of Bitcoin
in extreme market conditions. If indeed, Bitcoin
is a “strong” safe haven asset, negative extreme
stock index returns should be followed by positive
Bitcoin returns. Conversely, a “weak” safe haven
asset is where there is no evidence of predictability
between the assets in extreme market conditions.
It was found that, in most periods of extreme mar-
ket conditions under analysis, Bitcoin would fall
under the “weak” safe haven asset classication.
On the empirical side, and dierent periods, the
evidence suggests that Bitcoin can act as a diver-
sier under time-varying conditions (Bakry et al.,
2021; Carpenter, 2016; Eisl et al., 2015). On a dif-
ferent setting, and using a portfolio composed of
investment-grade bonds and global industry stock,
Akhtaruzzaman et al. (2020) found that Bitcoin
acted as a diversier for these kinds of assets. For
the majority of common nancial assets, there
was a contagion eect during the COVID-19 pan-
demic (Akhtaruzzaman et al., 2021). On the other
hand, Ghorbel and Jeribi (2021b) found that in the
case of energy markets (oil and gas) Bitcoin cannot
be considered a diversier. During the COVID-19
pandemic, the linkages between oil and Bitcoin
were stronger than in the pre-pandemic period
(Ghorbel & Jeribi, 2021a). However, Belhassine
and Karamti (2021) using an asset pricing frame-
work found evidence that Bitcoin showed the
properties of a safe haven asset for investments in
the Shanghai Stock Index.
Dyhrberg (2016) analyzed the volatility of Bitcoin
under dierent generalized autoregressive condi-
tional heteroskedasticity (GARCH) specications
and concluded that, for the period under scrutiny,
Bitcoin shared some characteristics common to
currency and gold. It was concluded that Bitcoin
could be a new kind of asset class that lies between
a currency and a commodity in terms of volatil-
ity. Conversely, Zhang et al. (2021) used a condi-
tional value at risk measure to quantify the impact
of volatility shocks. It was found that there is evi-
dence of volatility spillovers from Bitcoin to other
kinds of asset classes such as equities, commod-
ities, bonds, and currencies for certain periods.
Hoang et al. (2020) measured the connectedness
of Bitcoin to a portfolio composed of oil and a se-
ries of agricultural commodities. e correlation
of Bitcoin to the commodities was indeed low;
thus, Bitcoin could act as an eective portfolio
hedge to a portfolio of commodities. Salisu et al.
(2019) found that, under an arbitrage price theo-
ry framework (APT), the inclusion of Bitcoin as
an explanatory factor for predicting stock returns
from the G7 countries improved the performance
of the model and oered more explanatory pow-
er than other macroeconomic factors, except the
country’s interest rate. Erdas and Caglar (2018)
used an asymmetric causality test to see the di-
rection of the volatility spillovers of Bitcoin and
a series of indices and commodities. It was found
that there was a unidirectional eect of Bitcoin on
the S&P 500 and that a positive shock in Bitcoin
leads to a negative shock in the S&P 500 and vice
versa. Using a contagion framework, Matkovskyy
and Jalan (2019) found evidence that in times of
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http://dx.doi.org/10.21511/im.18(4).2021.19
crisis investors shied away from Bitcoin to saf-
er nancial assets. Wang et al. (2 021) argued that
as the volume traded in the cryptocurrency in-
creased, the eect of contagion to stock markets
will also increase over time. ere is evidence
that within the dierent cryptocurrencies there
are contagion eects in times of high price vola-
tility and the contagion tends to originate usually
from the most traded ones (Caporale et al., 2021;
Ferreira & Pereira, 2019). Schwenkler and Zheng
(2020) argue that there is little evidence of con-
tagion in the cryptocurrency market and that
on the contrary positive idiosyncratic shocks are
due to competition among dierent cryptocur-
rencies. is nding was corroborated by Qarni
and Gulzar (2021) using a dierent methodology
for the period comprehended between 2000 and
2017. Among the dierent cryptocurrencies avail-
able in the market there is a consensus that Bitcoin
is the most important in terms of volume, trada-
bility, and interdependencies with other nancial
markets (Ahelegbey et al., 2021; Chen et al., 2020;
Tsiaras, 2021).
e negative impact of the COVID-19 pandemic
on the real economy was felt at the global, region-
al, and local levels with devastating eects on em-
ployment and economic growth around the globe
(Danylyshyn, 2020; Ozili & Arun, 2020; Sansa,
2020). ere is evidence that the COVID-19 pan-
demic had a negative impact on traditional nan-
cial assets in dierent countries in Asia (Phuong,
2021). Finally, Ozturk and Cavdar (2021) argued
that, in times of an exogenous systemic shock,
such as the COVID-19 pandemic, there was an in-
crease in volatility spillovers between Bitcoin and
the currency and oil markets.
2. AIMS
e study aims to test the hypothesis that dur-
ing the COVID-19 pandemic, the eect of mar-
ket shocks emanating from changes in the price
of commodities and global stock prices dimin-
ished substantially during the pandemic. By us-
ing a three-factor model that allows for channels
of transmissions between all the factors, namely,
commodity, global stocks, and Bitcoin returns, it
is possible to estimate the contribution of com-
modities and global stocks to the variance of the
Bitcoin idiosyncratic risk during the COVID-19
pandemic.
3. METHODOLOGY
e sample contains the daily closing price of the:
1) Bitcoin (BTC) index, which is the most-traded
cryptocurrency; 2) e Bloomberg Commodity
Index (BCOM), which is an excess return-weight-
ed market capitalization index composed of the
most-traded commodities in the futures market;
and 3) e S&P Global Broad Market Index (BMI),
which includes the most representative stocks
from emerging and developed markets. e data
were extracted from Bloomberg and covered the
period from February 10, 2016, to March 2, 2021.
Summary statistics for the data are presented in
Table 1.
Table 1. Descripve stascs for Bitcoin, the
Bloomberg Commodity Index, and the S&P
Global Broad Market Index
Stasc Bitcoin (BTC)
Bloomberg
Commodity
Index (BCOM)
S&P Global
Broad Market
Index (BMI)
Mean 0.003802 0.000114 0.000494
Median 0.003219 0.000536 0.000743
Maximum 0.209837 0.033741 0.079546
Minimum –0.26 8099 –0.042709 –0.10 0267
Standard
deviaon 0.0460 43 0.008090 0.009756
Skewness –0.292648 0.413539 –1.8 01706
Kurtosis 7.65 326 6 6.057759 28.02425
Observaons 1,272 1,272 1,272
Note: Descriptive statistics for the three indices is from
February 10, 2016 to March 3, 2021.
In the case of Bitcoin and the indices’ returns,
the data are negatively skewed with a high kur-
tosis. Moreover, Bitcoin has a higher expected
return, which is associated with much higher
volatility than the commodity and global indi-
ces. Due to the negative skewness, there is ev-
idence of a leverage effect, where negative re-
turns outweigh positive returns in most cases.
The statistical properties of the data were fun-
damental in the choice of model for estimating
conditional returns to model the interdepend-
ences between the daily returns for Bitcoin and
the indices to correct for problems of correla-
tion among the variables. Counterintuitively,
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Investment Management and Financial Innovations, Volume 18, Issue 4, 2021
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the correlation of Bitcoin with the Bloomberg
Commodity Index (BCOM) is lower than with
the S&P Global Broad Market Index (BMI).
Figure 1 shows a trend at the price level for
Bitcoin and the Bloomberg Commodity Index
(BCOM).
At the price level, the BTC and the BCOM have
a positive correlation of 12.81%; the same calcu-
lation with the BMI, the correlation is 15.52%.
Conversely, the correlation between BMI and
BCOM for the period in question is 46.11%. This
is a good indicator that, for the period under
scrutiny, Bitcoin does not correlate heavily with
traditional physical and financial assets. Figure
2 shows a trend at the price level for Bitcoin and
the S&P Global Broad Market Index (BMI).
e present study aims to model the unique com-
ponent of the total risk of Bitcoin (BTC) returns.
ere are a series of considerations: 1) e inu-
ence of systemic shocks that derive from the com-
modities and global stock markets under an ex-
ogenous systemic shock, such as the COVID-19
pandemic, which is not attributable to market
conditions; 2) e dynamics of the volatility be-
tween Bitcoin (BTC) and both the Bloomberg
Commodity Index (BCOM) and the S&P Global
Note: The Y-axis from the left indicates the closing daily value of the BCOM, and the Y-axis from the right indicates the closing
daily value of the Bitcoin (BTC).
Figure 1. Bitcoin (BTC) and the Bloomberg Commodity Index (BCOM)
from February 10, 2016 to March 2, 2021
0
1000
2000
3000
4000
5000
6000
7000
0
20
40
60
80
100
11-Feb-2016 11-Feb-2017 11-Feb-2018 11-Feb-2019 11-Feb-2020 11-Feb-2021
BCOM BTC
Note: The Y-axis from the left indicates the closing daily value of the BMI, and the Y-axis from the right indicates the closing
daily value of the Bitcoin (BTC).
Figure 2. Bitcoin (BTC) and the S&P Global Broad Market Index (BMI)
from February 10, 2016 to March 2, 2021
0
1000
2000
3000
4000
5000
6000
7000
0
50
100
150
200
250
300
350
400
11-Feb-2016 11-Feb-2017 11-Feb-2018 11-Feb-2019 11-Feb-2020 11-Feb-2021
Global BTC
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Investment Management and Financial Innovations, Volume 18, Issue 4, 2021
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Broad Market Index (BMI) and explore the chang-
es in their dynamics due to the COVID-19 pan-
demic. One plausible explanation is that Bitcoin
(BTC) is an asset that has certain characteristics
that resemble a commodity but has some inter-
dependence with the global markets. erefore,
it is important to model for these dynamics to
have an accurate estimate of Bitcoin’s unique or
idiosyncratic risk under an asset pricing context.
Furthermore, the COVID-19 pandemic provides a
chance to observe the change of volatility dynam-
ics between market factor shocks and to deter-
mine if Bitcoin (BTC) can act as a safe haven asset
in times of uncertainty due to exogenous systemic
shocks, such as the COVID-19 pandemic.
e rst step is to model the time-varying volatil-
ity interaction of Bitcoin (BTC) returns between
the Bloomberg Commodity Index (BCOM) and
the S&P Global Index (BMI) returns. e same
procedure is used by Cayon and orp (2014) and
Cayon and Sarmiento (2020) to model systemic
conditional variance in the context of nancial
shocks. e rst step is to allow the conditional
means of the variables to follow an autoregressive
moving-average (ARMA) process in the following
forms (see Equation 1) to avoid the problems in-
volved with serial correlation and to ensure that
the residuals to be employed in the calculation of
the multivariate GARCH (M-GARCH) represent
a unique risk, as dened in an asset pricing con-
text, of the variables in question. erefore, the
proposed ARMA processes for each variable are:
, 0 1, , 2, ,
3,,14,,2 ,
, 0 1, , 2, ,
3,,14,,2
5, , 1 ,
, 0 1, ,
,
,
btc t btc bcom t btc bmi t
btc btc t btc btc t btc t
bcom t bcom btc t bcom bmi t
bcom bcom t bcom bcom t
bcom bcom t bcom t
bmi t bmi bcom t
rr
rr
ρρ ρ
ρρ
θε ε
ρρ ρ
−−
++ +
++
=++
2, ,
,
bmi btc t
bmi bmi t bmi bmi t devel t
r
rr
−−
+
(1)
Where rbtc,t are the daily returns of the Bitcoin
(BTC) for the observed period, rbcom,t the dai-
ly returns of the Bloomberg Commodity Index
(BCOM), and
,devel t
r
are the daily returns of the
S&P Global index (BMI), where in each ARMA
process for
,
,
btc t
r
,
,
bcom t
r
and
,bmi t
r
which accounts
for the contemporaneous interactions between
each factor to account for the correlation among
them. e next step is to model the conditional
covariance using the residuals of the variables ob-
tained from Equation 1 using the following spec-
ication (Equation 2) for a multivariate GARCH
model (M-GARCH):
11
11
11
11
11
2
0 1, 2,
2
0 1, 2,
2
0 1, 2,
, 0 1, ,
2, , ,
,
,
,
,
t t t tt
t t t tt
t t t tt
tt ttt t
t tt t
btc btc btc btc btc
bcom bcom bcom bcom bcom
bmi bmi bmi bmi bmi
btc bcom btc bcom btc bcom
btc bcom btc bcom
hh
hh
hh
h
h
h
ααε α
αα ε α
αα ε α
αα εε
α
−−
−−
−−
−−
−−
=
=++
=++
=++
+
++
11
11
11
11
, 0 1, ,
2, , ,
, 0 1, ,
2, , ,
1
,
,
0
0,
0
tt tt t t
tt t t
tt tt t t
tt t t
t
t
t
t
btc bmi btc bmi btc bmi
btc bmi btc bmi
bcom bmi bcom bmi bcom bmi
bocm bmi bcom bmi
btc
bcom t
bmi
btc
h
h
h
h
N
αα εε
α
αα ε ε
α
ε
ε
ε
−−
−−
−−
−−
=
=
++
+
++
+











,,
,,
,,
,
t t tt
tt t tt
tt t t t
btc bcom btc bmi
btc bcom bcom bmi bcom
btc bmi bmi bcom bmi
hh
hh h
hh h












(2)
where
t
btc
h
is the conditional variance of ltered
returns for Bitcoin (BTC),
t
bcom
h
is the condition-
al variance of ltered returns for the Bloomberg
Commodity Index (BCOM), and
t
bmi
h
is the con-
ditional variance of ltered returns for the S&P
Global index (BMI). In addition,
,
tt
btc bcom
h
is the
covariance between the Bitcoin (BTC) and the
Bloomberg Commodity Index (BCOM),
,
tt
bcom bmi
h
is the covariance between the Bloomberg
Commodity Index (BCOM) and the S&P Global
index (BMI), and nally,
,
tt
btc bmi
h
is the covari-
ance between the Bloomberg Commodity Index
(BCOM) and the Bitcoin (BTC). Using the tted
values from the conditional variances and co-
variances from the M-GARCH model, the paper
uses Equation 3 to compute the β of a two-factor
model in which the Bloomberg Commodity Index
(BCOM) and the S&P Global Index (BMI) explain
the variance of Bitcoin (BTC):
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Investment Management and Financial Innovations, Volume 18, Issue 4, 2021
http://dx.doi.org/10.21511/im.18(4).2021.19
( ) ( )
( ) ( )
( ) ( )
( ) ( )
, ,,
2
,
, ,,
2
,
,
,
t t t tt tt
t
t t tt
t tt tt tt
t
t t tt
bmi btc bcom bcom bmi btc bmi
bcom
bcom bmi bcom bmi
bcom btc bmi bocm bmi btc bmi
bmi
bcom bmi bcom bmi
hh h h
hh h
hh h h
hh h
β
β
=
=
(3)
e advantage of this specication under an asset
pricing context is that it allows for the discompos-
ing the variance of Bitcoin (BTC) into systemic and
idiosyncratic components in the following form:
22
,
2,
t t t tt
t t tt t
btc bcom bcom bmi bmi
bcom bmi bcom bmi
hhh
hh
ε
ββ
ββ
= ++
++
(4)
where
t
btc
h
is the variance of the Bitcoin (BTC),
2
tt
bcom bcom
h
β
is the part of the variance att ributed to
the systemic shocks transmitted by the Bloomberg
Commodity Index (BCOM), and
2
tt
bmi bmi
h
β
is the
part of the variance attributed to the system-
ic shocks transmitted by the S&P Global index
(BMI). e term
,
2t t tt
bcom bmi bcom bmi
h
ββ
accounts for
the eect of the covariance between the two sys-
temic factors on the variance of the Bitcoin (BTC),
and
t
h
ε
is the part of the variance attributed to id-
iosyncratic factors or unique risk. erefore, it al-
lows for the further decomposition of the variance
of Bitcoin (BTC) into its systemic and idiosyncratic
risk components using Equation 5:
22
,
,
,
2
,
.
t t t t t t tt
t
t
t
t
t
bcom bcom bmi bmi bcom bmi bcom bmi
sys btc
btc
idio btc
btc
hh h
uh
h
uh
ε
β β ββ
++
=
= (5)
e resulting time series for the percentage of BTC
variance explained by systemic risk are obtained
from Equation 5. With the resulting percentage
time series, it is straightforward to test if there is
a statistically signicant dierence between the
average idiosyncratic risk between the pre-COV-
ID-19 and post-COVID-19 periods using a t-test
for the dierence in means. ere is also a struc-
tural time break series test for robustness purposes.
4. RESULTS AND DISCUSSION
e time series for the Bitcoin idiosyncratic risk
in terms of percentage is obtained by applying
the procedure described in Equations 1-5. e
NBC news on COVID-19 timeline (2020) was
used to identify the period when the pandemic
started to take on momentum, which was when
the World Health Organization (WHO) declared
a global public health emergency aer more than
9,000 deaths were conrmed on January 30, 2020.
erefore, the pre-COVID-19 period is between
February 10, 2016, and January 29, 2020, and the
post-COVID-19 period is between January 30,
2020, and March 2, 2021. e average idiosyn-
cratic risk is calculated for each period, and the
results are summarized in Table 2. From the ta-
ble, one can observe that the distinction between
the two averages is statistically signicant and
that the unique or idiosyncratic risk of the Bitcoin
(BTC) return increases almost 900 basis points
from the pre-COVID-19 period, showing that
Bitcoin can act as a safe haven or hedge asset, at
least from a portfolio composed of global stocks
and commodities.
Table 2. T-test for disncons in unique or
idiosyncrac risk of Bitcoin for the pre-COVID-19
period and the post-COVID period
Stasc Pre-COVID Post-COVID
Mean (BTC unique risk) 86.06%*** 95.05%***
Variance 0.0 09 51 0.0 0417
Observaons 998 292
Group variance 0.00883
Hypothecal dierence
between means 0
Degrees of freedom 1288
T-Stat –14. 824
P(T < = t) value one-t ail 2.508E–29
T-Stat crical value (one tail) 1.646
P(T < = t) value two-tails 5.015E–29
T-Stat crical value (two tails) 1.961
Note: The table reports the average unique risk for the time
series for each period obtained by applying the procedure
described in Equations 1-5. *, **, and *** mean confidence
levels at 90%, 95%, and 99%, respectively.
Figure 3 details the dynamic behavior of system-
ic risk explained by the interaction between the
Bloomberg Commodity Index (BCOM) and the
S&P Global Broad Market Index (BMI). In 2020,
on average, the risk generated from the systemic
explanatory factors tends to dampen during the
height of the COVID-19 pandemic.
In Figure 3 the eect during the rst year of the
pandemic is noticeable when compared with the
behavior of systemic risk in previous periods. For
219
Investment Management and Financial Innovations, Volume 18, Issue 4, 2021
http://dx.doi.org/10.21511/im.18(4).2021.19
example, the average idiosyncratic risk for Bitcoin
for 2016, 2017, 2018, and 2019 was 85.63%, 85.76%,
88.38%, and 85.43%, respectively, and for 2020
and the fraction of 2021, the unique risk of Bitcoin
was 92.97% and 96.86%, respectively. In Table 3,
there are the monthly averages of the total sys-
temic risk attributable to both the commodity and
global stock factors, as well as the contribution of
the covariance between those factors, to see the ef-
fect of their interdependence in the estimates for
unique risk for the year before and the year aer
the pandemic.
From Table 3, and for all periods under observa-
tion, the covariance between the commodity in-
dex and the global stock markets is negative. is
negative covariance tends to lower the eect of sys-
temic risk. Before the pandemic, in October 2019
and December 2019, the commodity index acted
as a greater contributor to systemic risk than the
global stock index. It is interesting that, at the be-
ginning of the pandemic, the portion of the total
systemic risk attributable to the factors was higher
than before the pandemic. However, as the pan-
demic evolved during the year, the contribution of
the factors to systemic risk dampened rapidly. is
means that, in a portfolio composed of commod-
ities, global stocks, and Bitcoin, the latter can act
as a safe haven asset in times of exogenous system-
ic shocks (such as the pandemic), since common
sources of systemic shock, such as the global stock
and commodities markets, become insignicant
sources of systemic risk transmission in times of
uncertainty due to a global exogenous systemic
shock (namely, the COVID-19 pandemic). To test
the robustness of the t-test for dierences in mean
for the pre-COVID-19 and post-COVID-19 peri-
ods, there was an alternative structural break test
for the date in which the sample was divided into
two (Table 4).
From Table 4, the structural break test rejects the
null hypothesis that each subsample is not statis-
tically dierent; therefore, the choice of date for
dividing the data in the sample contributed to ex-
plaining the dierences in means.
Note: The figure shows the proportion of conditional volatility for the returns in the Bitcoin (BTC) returns due to systemic
volatility arising from shocks from commodities and global stock markets. The proportions are obtained using the estimates
from Equations 1-5 and their respective time series for the period under scrutiny.
Figure 3. Condional variance decomposion: Bitcoin returns
from February 10, 2016 to March 2, 2021
220
Investment Management and Financial Innovations, Volume 18, Issue 4, 2021
http://dx.doi.org/10.21511/im.18(4).2021.19
CONCLUSION
is paper examines the interaction between sources of market systemic shocks and their eect on the
variance of Bitcoin when exposed to a global exogenous systemic shock such as the COVID-19. e pan-
demic oers a unique opportunity to test the properties of Bitcoin as a safe haven asset. In the proposed
framework, the Bloomberg Commodity Index (BCOM) and the S&P Global Market Index (BMI) as
Table 3. Monthly average of unique and systemic risk of the Bitcoin aributable to shocks from the
Bloomberg Commodity Index and the S&P Global Broad Market Index
Period
Tot al
idiosyncrac
or unique risk
aributable
to the Bitcoin
(BTC)
Total systemic risk
aributable to the
Bloomberg Commodity
Index (BCOM) and the
S&P Global Broad Market
Index (BMI)
Contribuon
of the Bloomberg
Commodity
Index (BCOM)
to systemic risk
Contribuon
of the S&P Broad
Market Index
(BMI) to systemic
risk
Contribuon
of the covariance
between the
BCOM and BMI
to systemic risk
January 2019 84. 31% 15.69% 8.17% 16.24% –8.72%
February 2019 90.39% 9.61% 4.57% 11. 82% –6 .78%
March 2019 92.13% 7. 8 7% 4.49% 11.12% 7.7 3%
April 2019 88.43% 11.57% 6.26% 18.20% –12 .90%
May 2019 73.36% 26.64% 5.06% 32.35% –10.77 %
June 2019 78.52% 21.48% 9.04% 31 .21% –18.78%
July 2019 71.22% 28.78% 19.43% 48 .74% –39.39%
August 2019 87.8 3 % 12.17 % 5.22% 13.98% –7. 03%
September 2019 87.6 1% 12.39% 17. 07 % 19.6 8% –24.36%
October 2019 94.25% 5.75% 6.97% 5.19% 6.41%
November 2019 9 0.7 7% 9.23% 12.29% 12.43% –15.48%
December 2019 87.1 7% 12.83% 17.93% 12.82% –17.92 %
January 2020 73.31% 26.69% 20.83% 41 .85% –35.99%
February 2020 95.39 % 4.61% 3.7 5% 4.65% –3.80%
March 2020 90.7 5% 9.25% 3.12% 7.43% –1.3 0%
April 2020 96.00% 4.0 0% 4.11% 0.95% –1. 06 %
May 2020 95.60% 4.4 0% 0.45% 4.7 1% –0.76%
June 2020 97. 2 3% 2 .77% 1.34% 2.66% –1 .24 %
July 2020 96.78% 3.22% 2.50% 3.14% –2.42%
August 2020 94.83% 5.17 % 1.67% 7.69 % 4.19%
September 2020 97. 27% 2.73% 1.46% 3.04% –1.7 7%
October 2020 96.43% 3.57% 1.23% 4.96% –2.62%
November 2020 98.38% 1.62% 0.95% 1.72% –1 .05%
December 2020 84.30% 15.70% 9.19% 20.63% –14 .12%
Note: The average percentages of total systemic risk as a proportion of systemic risk and the respective contributions of each
factor to systemic risk are corrected by their covariance are obtained using the estimates from Equations 1-5.
Table 4. Breusch–Godfrey Lagrange mulplier tests for serial correlaon
Null hypothesis: There is no structural break for the me series of Bitcoin (BTC) due to the COVID -19
pandemic aer January 31, 2019.
Alternave hypothesis: There is a structural break for the me series of the Bitcoin (BTC) due to the COVID -19
pandemic aer January 31, 2019.
Structur al Break Test Point Period 998
Total Sums of Squares of Residuals 12.369
First Subset Sums of Squares of Residuals 9.07
Second Subset Sums of Squares of Residuals 2.152
Computed Test Stasc 129.642***
P-value 0.000
Note: This table summarizes the results obtained from running a structural break test for the date mentioned in the hypothesis
for the time series of unique risk obtained using the procedure from Equations 1-5.
221
Investment Management and Financial Innovations, Volume 18, Issue 4, 2021
http://dx.doi.org/10.21511/im.18(4).2021.19
possible sources of market systemic shock transmission to test their eect on the unique risk of Bitcoin
(BTC) before and during the ongoing COVID-19 pandemic.
e results show that for the rst year of the pandemic, the unique risk or idiosyncratic risk of Bitcoin
(BTC) rose signicantly compared to the pre-COVID period. A statistically signicant increase in id-
iosyncratic is a desirable characteristic of safe haven assets in a time of economic crisis. e results
demonstrate that Bitcoin (BTC) indeed exhibits characteristics that are expected from safe haven or
hedge assets during periods of increased global systemic risk due to exogenous shocks. Finally, it is im-
portant to highlight that the contribution of variance to the Bitcoin (BTC) from systemic risk sources
such as global equities and commodities during the period of the study exhibited a time-variant behav-
ior. In other words, during the period previous to the pandemic, commodities were a major source of
systemic risk to the Bitcoin (BTC) as compared to global equities; however, during the pandemic global
equities became the major source of systemic risk.
AUTHOR CONTRIBUTIONS
Conceptualization: Edgardo Cayón Fallon, Julio Sarmiento.
Data curation: Edgardo Cayón Fallon, Julio Sarmiento.
Formal analysis: Edgardo Cayón Fallon.
Investigation: Edgardo Cayón Fallon, Julio Sarmiento.
Methodology: Edgardo Cayón Fallon, Julio Sarmiento.
Soware: Edgardo Cayón Fallon, Julio Sarmiento.
Validation: Edgardo Cayón Fallon, Julio Sarmiento.
Writing – original dra: Edgardo Cayón Fallon, Julio Sarmiento.
Writing – review & editing: Edgardo Cayón Fallon, Julio Sarmiento.
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... Several kinds of research on consumer reactions to pandemics have been carried out (Donthu and Gustafsson, 2020). Fallon and Sarmiento (2021) explored the relationship between pandemic cases and stock market trends. Machmuddah et al. (2020) predicted a 50% security value drop during the pandemic, but a fast recovery thereafter once the short-term shock of labor supply in the market eased. ...
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This paper examines mean and volatility spillovers between three major cryptocurrencies (Bitcoin, Litecoin and Ethereum) and the role played by cyber-attacks. Specifically, trivariate GARCH-BEKK models are estimated which include suitably defined dummies corresponding to different types, targets and number per day of cyber-attacks. Significant dynamic linkages (interdependence) between the three cryptocurrencies under investigation are found in most cases when cyber-attacks are taken into account, Bitcoin appearing to be the dominant cryptocurrency. Further, Wald tests for parameter shifts during episodes of turbulence resulting from cyber-attacks provide evidence that the latter affect the transmission mechanism between cryptocurrency returns and volatilities (contagion). More precisely, cyber-attacks appear to strengthen cross-market linkages, thereby reducing portfolio diversification opportunities for cryptocurrency investors. Finally, the conditional correlation analysis confirms the previous findings.
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