Finance, Markets and Valuation
DOI: 10.46503/BJWT6248
Corresponding author
Jawad Saleemi
Received: 13 Mar 2021
Revised: 30 Apr 2021
Accepted: 3 May 2021
Finance, Markets and
Valuation
ISSN 2530-3163.
Finance, Markets and Valuation Vol. 7, Num. 1 (January-June 2021), 1–11
COVID-19 uncertainty and Bitcoin market, linking the
liquidity cost to the cryptocurrency yields
COVID-19 incertidumbre y mercado de Bitcoin, vinculando
el coste de la liquidez a los rendimientos de la criptodivisa
Jawad Saleemi
ID
1,2
1
Department of Management Sciences, University of Gujrat. Gujrat, Pakistan. Email:
j.saleemi@yahoo.com
2
Department of Economics and Social Sciences, Universitat Politècnica De València. Valencia,
Spain. Email: Jasa1@doctor.upv.es
JEL: G12
Abstract
The cryptocurrency market is emerging as a new asset class for the investment. As the traditional asset
prices are oen noted to be influenced by the liquidity risk, this study links the cryptocurrency liquidity
cost to its yields. Pre-pandemic uncertainty, the Bitcoin liquidity cost was found to be priced in its returns
during the same trading session. Post-pandemic crisis, the relationship was changed. The liquidity cost
was reported not to be priced in the Bitcoin returns at the time of same trading session. Post-pandemic
crisis, however, the liquidity cost imposed by the liquidity supplier on day t − 1 was noted to be priced in
the Bitcoin return of day t . In the cryptocurrency market, this study quantifies the eects on the Bitcoin
returns of its liquidity cost, and if such eects vary pre- and post-pandemic uncertainty.
Keywords: Cryptocurrency Market; Asset Pricing; Liquidity Cost; Returns
Resumen
El mercado de las criptomonedasestá surgiendo como una nueva clase de activos para la inversión. Dado
que los precios de los activos tradicionales suelen estar influidos por el riesgo de liquidez, este estudio
vincula el coste de liquidez de las criptomonedas a sus rendimientos. Antes de la incertidumbre pandémi-
ca, se comprobó que el coste de liquidez de Bitcoin estaba tasado en sus rendimientos durante la misma
sesión de negociación. Después de la crisis pandémica, la relación cambió. El coste de la liquidez no se
incluyó en el precio de los rendimientos del Bitcoin en la misma sesión de negociación. Sin embargo, des-
pués de la crisis pandémica, se observó que el coste de liquidez impuesto por el proveedor de liquidez
en el día t − 1 tenía un precio en el rendimiento de Bitcoin del día t . En el mercado de criptomonedas,
este estudio cuantifica los efectos en el rendimiento de Bitcoin de su coste de liquidez, y si tales efectos
varían antes y después de la incertidumbre pandémica.
Keywords: Mercadode criptomonedas; Fijación de precios de los activos; Coste de liquidez; Rendimientos
How to cite: Saleemi, J. (2021) COVID-19 uncertainty and Bitcoin market, linking the liquidity cost to the
cryptocurrency yields. Finance, Markets and Valuation 7(1), pp. 1–11.
1
Finance, Markets and Valuation Vol. 7, Num. 1 (January-June 2021), 1–11
1 Introduction
In late 2019, the rumors about a new virus, named to the coronavirus, had started to begin from
China. In early 2020, the virus had surged worldwide. Due to the exceptional rate of fatality, the
virus was announced as a pandemic on March 11, 2020. It led to a social distancing and home
isolation across the globe. Over 1 billion people was compelled to follow immeasurable social,
and economic restrictions. With regard to the easing lockdown or its associated restrictions,
the common challenge for the governments was to ascertain the appropriate time. The health
oicials were noted to be concerned about the impact of the easing restrictions. In late Q2 2020,
the lockdown was temporarily removed, but time varies to each country.
In early 2021, many countries are noted to reconsider the lockdown with numerous social,
political, and economic restrictions. The pandemic uncertainty is raising concerns on the
future economic perspectives. Due to the coronavirus crisis, the global financial markets are
at the risk of a higher volatility (Zhang, Hu, & Ji, 2020). The pandemic is impacting the global
economic development (Goodell, 2020), and yields on the assets (Al-Awadhi, Alsaifi, Al-Awadhi,
& Alhammadi, 2020). Following the pandemic uncertainty, the investor pessimistic emotions
caused the illiquidity and higher trading cost in the Australian Securities Exchange market
(Saleemi, 2020b). The returns on the traditional stock market are more sensitive to the liquidity
cost imposed by the liquidity providers during the pandemic uncertainty (Saleemi, 2021).
The cryptocurrency market is referred to a safe haven for distinct reasons: unregulated
from the monetary policy, a store of value, and weak relationship with the traditional financial
assets (Klein, pham thu hien, & Walther, 2018). As the recent crisis develops, the market for
the Bitcoin does not act as a safe haven (Conlon & McGee, 2020). Whether the cryptocurrency
market should be regulated under the government financial policy, the discussion is still active.
In 2009, the Bitcoin was first introduced as a digital currency and another investment asset
class. Although there are numerous cryptocurrencies, the Bitcoin has received a huge attention
due to its massive price fluctuation. The Bitcoin price has highly escalated during the pandemic.
However, the price shocks are still occurring in the Bitcoin market.
The market liquidity determines the asset price (Amihud & Mendelson, 1991), and its returns
(Amihud, Hameed, Kang, & Zhang, 2015). The inventory holder requires to liquidate its position
due to the earning incentives. The liquidity matters to redeem the position. The market liquidity
is the immediacy of a transaction execution. In order words, the low trading cost is referred to the
higher liquidity. The market liquidity risk can also be relevant for the Bitcoin holders. Although
the liquidity risk has immediate impact on the trading, it is an active discipline of research
(Guijarro, Moya-Clemente, & Saleemi, 2019). This study investigateswhether the market liquidity
or its associated cost is a relevant element to determine yields in the cryptocurrency market
during the recent pandemic. Additionally, the work reports the impact of liquidity cost on the
cryptocurrency returns pre- and post-pandemic crisis.
The liquidity cost can be referred to a conditional cost that the liquidity provider asks against
providing the liquidity. The forward-looking investor tends to be protected against the provision
of illiquidity and imposes a cost on the seller. The bid-ask spread is oen applied to measure
the entire liquidity cost. The higher spread elucidates reluctance of the liquidity supplier to
invest in an asset without imposing a cost on the counterparty (Saleemi, 2020a). In this context,
the liquidity provider or the spread size influences the asset prices. Although the price for the
Bitcoin is noted to be massively surged in the recent pandemic, it matters to unveil eects on
the cryptocurrency returns of the liquidity cost during the pandemic uncertainty.
The research paper is organized as follows. The literature is reviewed in Section 2. The
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Finance, Markets and Valuation Vol. 7, Num. 1 (January-June 2021), 1–11
material and methods are discussed in Section 3. The empirical findings are presented and
elucidated in Section 4. The main outcomes of the research paper are reported in Section 5.
2 Review of Literature
The literature in the cryptocurrency market has rapidly expanded under various avenues: the
cryptocurrency’s features (Wu, Pandey, & Dba, 2014), price speculation (Cheah & Fry, 2015),
price formation (Dyhrberg, 2016), volatility (Katsiampa, 2017), price clustering (Urquhart, 2017),
transaction cost (Kim, 2017), market eiciency (Bariviera, Basgall, Hasperué, & Naiouf, 2017),
returns and volatility (Omane-Adjepong, Ababio, & Alagidede, 2019), market persistence (Bouri,
Lau, Lucey, & Roubaud, 2019), and relationship between cryptocurrencies and traditional
securities (Gil-Alana, Abakah, & Rojo, 2020). To the author’s knowledge, however, the impact
of the pandemic uncertainty on the relationship between liquidity cost and cryptocurrency
returns has not been explored.
In the cryptocurrency market, this study focuses on the liquidity cost and its eects on
the cryptocurrency returns pre- and post-pandemic crisis. The market for cryptocurrencies is
reported to be significantly ineicient (Nan & Kaizoji, 2019). The eiciency for the market of
cryptocurrencies is relevant to the certain periods (Kristoufek & Vosvrda, 2019). The cryptocur-
rencies are exposed to the systematic risk (Corbet, Lucey, & Yarovaya, 2018), and sensitive to
various events (Tran & Leirvik, 2020). The cryptocurrencies are found to be interlinked in terms
of volatility spillover, lead-lag impact, and market co-movement (Sifat, Mohamad, & Shari,
2019).
The liquidity substantially varies among cryptocurrencies (Phillip, Chan, & Peiris, 2018). As
the liquidity increases, the market for cryptocurrencies becomes more eicient (Brauneis &
Mestel, 2018). Unlike in the retail foreign exchange markets, the transaction cost for the Bitcoin
is substantially lower (Kim, 2017). The liquidity for the cryptocurrency is considerably related
to the market eiciency (Wei, 2018). The simpler infrastructure or government free design in
the cryptocurrency market causing a surge in its trading quantity, price, and volatility (Corbet
et al., 2018).
The Bitcoin, in general, is held for two reasons: electronic cash and speculation. The Bitcoin
holders are preliminary concerned with the future fundamental value of the Bitcoin (Cheah
& Fry, 2015). The Bitcoin is sensitive to the speculative behavior and price bubbles (Corbet et
al., 2018). The market for the Bitcoin is more ineicient compared to the stock, gold, or forex
markets (Al-Yahyaee, Mensi, & Yoon, 2018). The market for cryptocurrencies is not cointegrated
with the stock market indices (Gil-Alana et al., 2020).
In the traditional asset class, the market liquidity is oen priced in the returns (Amihud et
al., 2015). The liquidity provider tends to be compensated against the risk of illiquidity. In this
context, a liquidity cost is imposed on the seller. This leads to a decline in the asset price. The
liquidity cost or lower bid-price ensures, that a liquidity provider can possibly generate yields
on the future resale of the holding inventory. The yield sensitivity to the market liquidity shocks
can generate higher returns on the investment (Le & Gregoriou, 2020).
This study investigates whether the liquidity cost can be applied as a measure of yields in
the cryptocurrency market during the pandemic uncertainty. The market liquidity is elucidated
in various avenues: eective trading cost (Roll, 1984); asymmetric information eects (Glosten
& Milgrom, 1985); market features of immediacy, trading cost, depth, breadth, and resiliency
(Lybek, Sarr, & and, 2002); and price impact (Liu, 2006). The market liquidity, in general, relates
to the easiness of transaction execution with bearing a low cost. The liquidity measurement is
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Finance, Markets and Valuation Vol. 7, Num. 1 (January-June 2021), 1–11
a multidimensional subject.
In the asset pricing literature, the abundant liquidity models have been proposed over time.
Although the list of liquidity cost models is huge, the two aspects are found to be common. The
friction or cost in the market determines the liquidity and its impact is time varying (Degennaro
& Robotti, 2007). In a market, the bid-ask spread is highly considered to estimate the cost and
ease of trading (Corwin & Schultz, 2012). The spread is modelled under three major avenues:
inventory immediacy cost, asymmetric information cost, and order processing cost (Huang &
Stoll, 1997).
The immediacy cost model argues that the liquidity providers are compensated against the
future price uncertainty. The liquidity provider reduces its risk exposure against the holding
inventory and thus, imposes a cost on the seller. The asymmetric information model assumes
that the informed trader drives the market liquidity. The uninformed counterparty can possibly
lose in the trading (Gorton & Metrick, 2010). In this context, the liquidity provider imposes a cost
on the seller. The liquidity provider also tends to be compensated against the order processing
cost.
The bid and ask are the quoted prices for an asset. The bid is the maximum price that a
buyer is willing to pay against the asset. The ask relates to the minimum price that a seller
would accept to redeem its position. The liquidity provider would provide the immediacy of
transaction at the best bid price and liquidate the investment at the best ask price. This implies,
that the liquidity provider tends to generate yields on the investment. The spread is a range
between the quoted prices. The size of spread elucidates the ease and cost of trading for an
asset.
3 Data and Methods
The recent crisis and its damages on various aspects of the financial markets are continuing
under discussion. In this study, the focus lies on the relationship between cryptocurrency
spreadsand its yields. Since various liquidity cost models are introduced, the common challenge
for the researchers is to determine the appropriate spread proxy. A few shortcomings are
reported in distinct spread models (Goyenko, Holden, & Trzcinka, 2009). This study adopts
two spread proxies, namely the Eective Spread (ES) and Cost-based Market Liquidity (CBML)
spread.
In the asset pricing literature, the spread proxies, in general, are examined under two
avenues: intraday data and low-frequency data. The asset prices quoted numerous times once
a day are reported to the intraday data. Conversely, the low-frequency data for an asset can be
elucidated to daily features, such as, the opening price, ask price, bid price, closing price, and
trading volume. In this study, the daily ask price, bid price, and closing price of the Bitcoin are
considered in the analysis. The analysis is performed during the period March 10, 2014 – April
21, 2021.
Among the liquidity cost measures, the ES model is oen applied to estimate the real cost
of trading. The ES proxy is computed from the low-frequency data, and constructed as below:
E f f ect i v eSpr ead
t
=
2|C
t
− η
t
|
η
t
(1)
An alternative proxy of the bid-ask spread, namely the CBML, is proposed by (Saleemi,
2020a). In this study, the CBML method is constructed from the low-frequency data. The
analytical expression of the CBML proxy is given as below:
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Finance, Markets and Valuation Vol. 7, Num. 1 (January-June 2021), 1–11
C BM L
t
=
q
S
t −1
− v
s
t
2
(2)
For the previous trading-day session,
S
t −1
is a ratio of the asset range to its closing price,
and estimated as below:
S
t −1
=
H
t −1
− L
t −1
C
t −1
(3)
Where,
H
t −1
is the highest price of the Bitcoin;
L
t −1
refers to the Bitcoin lowest price; and
C
t −1
denotes to its closing price for the past trading-day session. The CBML model looks at
past prices by a logic that the liquidity supplier tends to be compensated against the price
fluctuation, return uncertainty, and order processing expenses. In the following trading-day
session, the CBML method estimates the information eects on the buyer-initiated trade and
seller-initiated trade. In this context, the ratio of an informed asset range to its closing price is
estimated on day t at:
v
s
t
=
v
ask
t
− v
bi d
t
C
t
(4)
Assuming risk neutrality in the future trading-day session, the asset is valued at:
η
t
=
1
2
(
H
t
+ L
t
)
(5)
Where,
η
t
is the mean value of day
t
. The model assumes equal presence of the informed
buyer and seller in the market. In this context, the ask value is assumed conditional at:
v
ask
t
= H
t
π + η
t
π (6)
Meanwhile, the estimated bid value is assumed conditional at:
v
bi d
t
= L
t
π + η
t
π (7)
To understand the relationship between cryptocurrency spreads and its yields, the return
on the Bitcoin is calculated as below:
BR
t
=
p
t
p
t −1
− 1 (8)
Where,
BR
t
is yield on the Bitcoin for day
t
;
p
t
denotes to its closing price of day
t
; and
p
t −1
refers to its previous trading-day closing price. The Bitcoin yields are computed on a daily basis.
3.1 Benchmark model
The study is performed by means of a multiple linear regression analysis. In the benchmark
model, the Bitcoin liquidity cost acts as an explanatory variable and return on the Bitcoin refers
to a response variable. In this study, the benchmark model is constructed as below:
BR
t
= α + β
1
SP
t
+ β
2
SP
t −1
+
t
(9)
Where,
BR
t
is the Bitcoin return of day
t
;
SP
t
refers to the Bitcoin spread of day
t
;
SP
t −1
denotes to the Bitcoin spread of day
t − 1
; and
t
is the error term. The control variable is
not considered to perform the regression analysis. As earlier mentioned, a liquidity supplier
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Finance, Markets and Valuation Vol. 7, Num. 1 (January-June 2021), 1–11
V N Min Median Mean Max SD S K
ES 2600 3.07E-16 0.01602 0.0261 0.456877 0.0308 3.5836 29.1771
CBML 2600 0.0000219 0.0202 0.0304 0.523060 0.0344 3.7014 30.3997
BR 2600 -0.391816 0.00181 0.0025 0.272286 0.0391 -0.1621 12.0922
Note: Variables: V; Observations: N; Eective Spread: ES; Cost-Based Market Liquidity: CBML; Bitcoin
Return; BR; Standard Deviation: SD; Skewness: S; Kurtosis: K.
Table 1. Descriptive Statistics
reduces its risk by imposing a cost on the seller. The Bitcoin buyer can also redeem its position
in the following trading day. Therefore, the study investigates whether the cost imposed by the
liquidity supplier on day
t − 1
is relevant to determine the return of day
t
at the time of Bitcoin
redemption.
Figure 1. The Bitcoin spreads are graphed on a monthly basis
4 Results
The descriptive statistics are estimated on a daily basis, and reported in Table 1. It is noted that
the Bitcoin spreads are positively skewed with fat-tailed distribution. This implies, that the
spread measures have the right-skewed distributions with most values to the right of their mean.
Conversely, the Bitcoin return is negatively skewed with fat-tailed distribution. The negative
skewness for the return indicates the le-skewed distributions with most values to the le of
mean value. The fat-tailed distributions or higher kurtosis values for the spread proxies and
return are indicating the extreme values in the corresponding dataset. On a monthly basis, the
fluctuation in the Bitcoin spreads and its yields are graphed in Figure 1 and Figure 2, respectively.
It is vividly noted that the liquidity cost and returns are time-varying in the Bitcoin market.
It matters to unveil whether the liquidity cost is an appropriate measure to estimate yields
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Finance, Markets and Valuation Vol. 7, Num. 1 (January-June 2021), 1–11
Figure 2. The Bitcoin returns are graphed on a monthly basis
in the cryptocurrency market. The analysis is separated in two avenues: the impact on Bitcoin
returns of the liquidity cost pre-pandemic crisis; and if the pandemic uncertainty influences the
relationship between Bitcoin liquidity cost and its returns. At the first stage, the study performs
a multiple linear regression analysis during the period March 10, 2014 – March 10, 2020. On a
daily basis, the Bitcoin returns are examined as a linear combination of liquidity cost imposed
by the liquidity supplier on day t and day t − 1.
Pre-pandemic crisis, the regression relationship is quantified in Table 2. On the same trading
day, the Bitcoin returns are positive and significantly associated with its liquidity cost, estimated
by ES and CBML measures. This implies, that the liquidity suppliers tend to be compensated
against the provision of illiquidity in the Bitcoin market. The higher spread or liquidity cost
compensates the liquidity providers with higher returns on the Bitcoin resale. Pre-pandemic
crisis, hence, the Bitcoin liquidity cost is priced in its yields during the same trading session.
The study also considers if a buyer of the Bitcoin or the Bitcoin liquidity supplier chooses
to redeem its position in the following trading session. In this context, the study investigates
whether the liquidity cost imposed by the buyer on day
t − 1
is relevant to determine the return
of day t at the time of Bitcoin redemption. It is noted, that the Bitcoin return of day t is positive
and insignificantly associated with its liquidity cost of day
t − 1
. Pre-pandemic uncertainty,
thereby, the liquidity cost imposed by the investor on day
t − 1
is not priced in return of day
t
during the following trading session.
The following experiment is performed during the period March 10, 2014 – April 21, 2021.
The subperiod studies the pandemic eects on the relationship between Bitcoin spreads and
its returns. For the subperiod, the Bitcoin returns are studied as a linear combination of its
liquidity cost imposed by the liquidity provider on day
t
and day
t − 1
. Post-pandemic crisis,
the relationship is reported in Table 3, and quantified by means of a multiple linear regression
analysis. On the same trading session, the Bitcoin returns are found to be negatively associated
with its liquidity cost. However, the relationship is noted not to be statistically significant. Post-
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Finance, Markets and Valuation Vol. 7, Num. 1 (January-June 2021), 1–11
Variables Estimate p-value
BR (a) Intercept -0.0008168 0.5050
ES 0.0726563 0.0113*
E S
t −1
0.0367245 0.2001
BR (b) Intercept -0.0002699 0.83034
CBML 0.0706449 0.00715**
C BM L
t −1
0.0045626 0.86198
Note: a) Adjusted R-squared: 0.003816; F-statistic: 5.158; p-value: 0.005823; (b) Adjusted R-squared:
0.002836; F-statistic: 4.088; p-value: 0.01691; Significance codes: ‘***’ < 0.001; ‘**’ < 0.01; ‘*’ < 0.05
Table 2. The analysis is performed pre-pandemic crisis
Variables Estimate p-value
BR (a) Intercept 0.002540 0.3895
ES -0.018083 0.7673
E S
t −1
0.130377 0.0333 *
BR (b) Intercept 0.006991 0.0142 *
CBML -0.081079 0.1345
C BM L
t −1
0.044001 0.4162
Note: a) Adjusted R-squared: 0.006602; F-statistic: 2.349; p-value: 0.09675; (b) Adjusted R-squared:
0.0007322; F-statistic: 1.149; p-value: 0.3181; Significance codes: ‘***’ < 0.001; ‘**’ < 0.01; ‘*’ < 0.05
Table 3. The analysis is performed post-pandemic crisis
pandemic crisis, therefore, the Bitcoin liquidity cost is not priced in its returns during the same
trading session.
Post-pandemic crisis, the study also investigates the relationship between the liquidity cost
imposed by the liquidity supplier on day
t − 1
against accepting the inventory and the return
on day
t
at the time of inventory redemption. Table 3 reports, that the Bitcoin return of day
t
is
positive and significantly explained by the eective spread of day
t − 1
. This implies, that the
liquidity cost imposed by the investor on day
t − 1
is relevant to determine the Bitcoin yield
in the following trading session. Post-pandemic crisis, thus, the Bitcoin liquidity cost of day
t − 1
is priced in its return of day
t
. Conversely, the Bitcoin returns are also positively related to
its liquidity cost of day
t − 1
, estimated by the CBML model. Nevertheless, the relationship is
not statistically significant. Since the adopted spread proxies are based on distinct analytical
assumptions, the spread measures are expected to impact the analysis.
5 Conclusions
In the cryptocurrency market, this study examines the eects of liquidity cost on yields pre- and
post-pandemic uncertainty. Using distinct measures of the liquidity cost, the study is based on
the Bitcoin. If the same trading session was analyzed pre-pandemic crisis, the Bitcoin returns
were positive and significantly explained by its liquidity cost. Pre-pandemic uncertainty, the
Bitcoin liquidity cost was found to be priced in its returns during the same trading day. When
the following trading session was examined pre-pandemic crisis, the liquidity cost imposed by
the investor on day t − 1 was not priced in return of day t during the Bitcoin redemption.
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Finance, Markets and Valuation Vol. 7, Num. 1 (January-June 2021), 1–11
Post-pandemic uncertainty, the results were changed. With regard to the same trading
session, the Bitcoin returns were negative and insignificantly associated with the liquidity cost.
Thereby, the liquidity cost was not priced in the Bitcoin returns during the same trading session.
If the following trading session was studied in the cryptocurrency market, the return of day
t
was positive and significantly explained by the eective spread of day
t − 1
. Post-pandemic
crisis, the liquidity cost imposed by the investor on day
t − 1
was found to be priced in the
return of day t at the time of Bitcoin redemption.
Since huge price fluctuations are reported in the Bitcoin market, this study helps to un-
derstand the market liquidity risk associated with the Bitcoin trading. Following the study
results, the Bitcoin holders can better manage the market liquidity and its associated cost at
the time of Bitcoin trading. Although various cryptocurrencies are operating in the market, this
study cannot be generalized in the market for cryptocurrencies. The findings encourage other
researchers to study the relationship between various cryptocurrencies’ liquidity cost and their
yields during the pandemic uncertainty.
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