COVID-19 and cryptocurrency volatility: Evidence from asymmetric modelling

Area of Science:

• * Financial Econometrics
• * Cryptocurrency Market Analysis
• * Pandemic Impact Studies

Background:

• * The COVID-19 pandemic introduced unprecedented economic uncertainty globally.
• * Cryptocurrencies, as a nascent asset class, exhibit unique responses to market shocks.
• * Understanding volatility dynamics is crucial for risk management in digital asset markets.

Purpose of the Study:

• * To investigate the impact of the COVID-19 crisis on cryptocurrency returns' conditional volatility.
• * To assess the effectiveness of asymmetric GARCH models in capturing this impact.
• * To determine if explicitly modeling the pandemic improves volatility forecasting accuracy.

Main Methods:

• * Employed an asymmetric Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling framework.
• * Analyzed a dataset comprising eight major cryptocurrencies.
• * Incorporated the COVID-19 pandemic as a specific event in the volatility models.

Main Results:

• * The COVID-19 pandemic demonstrated a statistically significant positive effect on cryptocurrency returns' conditional volatility.
• * Asymmetric GARCH models effectively captured the heightened volatility during the crisis period.
• * Models that explicitly included the pandemic event showed superior performance in forecasting volatility.

Conclusions:

• * The COVID-19 crisis acted as a significant shock, increasing volatility across major cryptocurrencies.
• * Asymmetric GARCH models provide a robust framework for analyzing and forecasting cryptocurrency volatility during crises.
• * Accounting for crisis events like the pandemic is essential for accurate financial risk assessment in digital asset markets.