Digital currencies have been developed only after the global recession in 2008. Therefore, there is only little knowledge about the behavior of cryptocurrency during financial crisis. This study will examine if the return volatility of cryptocurrencies in pre-COVID-19 and COVID-19 periods caused any differences in returns. The ten most traded cryptocurrency market returns are examined in this study using the ARMA-EGARCH model to determine the impact of return volatility both before and during the COVID-19 epidemic.

1. THE EFFECT OF COVID 19 ON THE
RETURN-VOLATILITY RELATIONSHIP
IN CRYPTOCURRENCY MARKETS

2. INTRODUCTION
 Digital currencies have been developed only after the global recession in
2008. Therefore, there is only little knowledge about the behavior of
cryptocurrency during financial crisis.
 This study will examine if the return volatility of cryptocurrencies in pre-
COVID-19 and COVID-19 periods caused any differences in returns.
 The ten most traded cryptocurrency market returns are examined in this
study using the ARMA-EGARCH model to determine the impact of return
volatility both before and during the COVID-19 epidemic.

3. LITERATURE REVIEW
 Corbet in 2018 look at how cryptocurrencies and other financial assets change
over time.
 Bjerg in 2016 explained that Bitcoin is money
 Katsiampa in 2017 compares several competing GARCH-type models to find
out how volatility works in the case of cryptocurrencies.
 Kakinaka in 2021 use the fractal method of MF-ADCCA to study the
asymmetric cross-correlation between price return and return volatility in
cryptocurrency markets.

4. NEED FOR THE STUDY – RESEARCH GAP
 Existing literatures restricts their analysis to a few cryptocurrency markets,
primarily Bitcoin and Ethereum.
 This study evaluates the ten most traded cryptocurrency markets
 This study took into account nearly the same sample size for both the time
period before and after the COVID-19 pandemic.

5. RESEARCH QUESTION
 How the effect of covid 19 pandemic impacted on the return-volatility
relationship in ten most traded cryptocurrencies, namely Tether, Bitcoin,
Ethereum, Ripple, Litecoin, Bitcoin Cash, EOS, Chainlink, Cardano, and
Monero during and prior to Covid 19 ?
 Whether cryptocurrency can be considered a suitable asset for investing
during the pandemic period ?

6. OBJECTIVES
 To examine the effect of Covid 19 pandemic on return volatilities of the ten
most traded cryptocurrencies.
 To study, evaluate, and investigate the dynamics of cryptocurrencies before
and during the COVID-19 pandemic and to compare it with other financial
assets.
 To find out whether there is any correlation between volatility of
cryptocurrency and other financial assets.

7. HYPOTHESIS
 Hypothesis 2 (H2). The mean absolute percentage error for the volatility of
cryptocurrency before the COVID-19 outbreak is more than during the COVID-
19 outbreak.
 Hypothesis 1 (H1). The volatility of cryptocurrency is higher than that of other
financial assets.
 Hypothesis 3 (H3). There is a positive correlation between Bitcoin, S&P 500,
gold and TLT

8. SCOPE OF THE STUDY
 The period from January 01, 2019 to December 31, 2019 is considered as pre-
COVID-19 pandemic period, while the period from January 01, 2020 to
December 31, 2020 is during COVID-19 pandemic.
 The pandemic period is just considered the 2020 year to ensure sample sizes
for pre-pandemic and during pandemic periods are comparable for the
current analysis.
 Daily closing prices of the ten most traded cryptocurrencies, daily prices of
Gold, and WTI, and BRENT Crude Oil prices are collected for a period from
January 01, 2019 to December 31, 2020.
 The cryptocurrencies studied in this paper are Tether, Bitcoin, Ethereum,
Ripple, Litecoin, Bitcoin Cash, EOS, Chainlink, Cardano, and Monero.

9. METHODOLOGY
 Research Design- Quantitative research
 Data Collection method- Secondary Data
 Data analysis tools- R studio, IBM SPSS, Microsoft Excel
 EGARCH-M is used to examine the return-volatility relationship
 Autoregressive Moving Average (ARMA) model is used to estimate mean
returns.

10. REFERENCES
 Bjerg, O. (2016). How is bitcoin money? Theory, Culture & Society, 33.
 Corbet, S. A. (2018). Exploring the dynamic relationships between cryptocurrencies and other
financial assets. Economics Letters, 165. Retrieved from
https://www.sciencedirect.com/science/article/abs/pii/S0165176518300041?via%3Dihub
 Cryptocompare. (2022). Retrieved from Cryptocompare: https://www.cryptocompare.com/
 Kakinaka, S. U. (2021). Exploring asymmetric multifractal cross-correlations of price–volatility
and asymmetric volatility dynamics in cryptocurrency markets. Physica A, 581.
 Katsiampa, P. (2017). Volatility estimation for Bitcoin: A comparison of GARCH models.
Econom. Lett, 158.
 Parisa Foroutan, S. L. (2022). The effect of COVID-19 pandemic on return-volume and return-
volatility relationships in cryptocurrency markets. Chaos, Solitons and Fractals.
 Slickcharts. (2022). Slickcharts. Retrieved from Slickcharts:
https://www.slickcharts.com/currency