No abstract

Cryptocurrency investment especially Bitcoin, has become favourable over recent years due to promising returns in the future. However, the movement of price is mainly speculation-based as these currencies are still new in the market. The COVID-19 outbreak boosted research interest in predicting the price fluctuation of Bitcoin since cryptocurrency trading produced many millionaires. Five lexicon-based Twitter sentiment analysis approaches are examined to see the effect of Tweets on the price of Bitcoin during the pre- and post- COVID-19 period. Results show that negative Twitter sentiments affected the price of Bitcoin pre- COVID-19 and the second year of post- COVID-19 when Elon Musk actively criticised Bitcoin on Twitter.

During the COVID-19 pandemic, cryptocurrency prices showed abnormal volatility that attracted the participation of many investors. Studying the behaviour of volatility for the prices of cryptocurrency is an interesting problem to be investigated. This research implements the state space model framework for volatility incorporating the Kalman filter. This method directly forecasts the conditional volatility of five cryptocurrency prices (Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC) and Bitcoin Cash (BCH)) for 10,000 consecutive hours, i.e., approximately 417 days during the COVID-19 pandemic from 26 February 2020, 00:00 h until 18 April 2021, 00:00 h. The performance of this model is compared to the GARCH (1,1) model and the neural network autoregressive (NNAR) based on root mean square error (RMSE), mean absolute error (MAE) and the volatility plot. The autocorrelation function plot, histogram and the residuals plot are used to examine the model adequacy. Among the three models, the state space model gives the best fit. The state space model gives the narrowest confidence interval of volatility and value-at-risk forecasts among the three models.