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THE EFFECT OF COVID 19 ON THE RETURN-VOLATILITY.pptx

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THE EFFECT OF COVID 19 ON THE RETURN-VOLATILITY.pptx

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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.

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.

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THE EFFECT OF COVID 19 ON THE RETURN-VOLATILITY.pptx

  1. 1. THE EFFECT OF COVID 19 ON THE RETURN-VOLATILITY RELATIONSHIP IN CRYPTOCURRENCY MARKETS
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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

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