Google Trends and Bitcoin volatility forecast

Since the introduction of Bitcoin in 2008, the size of the cryptocurrency market is becoming increasingly important for investors. Thus, the forecast of cryptocurrency price volatility is of particular interest to portfolio investors, as they are interested in accurately estimating the standard deviation of their portfolios to calculate the Value-at-R isk (VaR) as a risk measure for more optimal portfolio management. The HAR-RV model introduced by F. Corsi (in 2009) became more effective than the traditional GARCH type models in forecasting in the volatility of financial assets. In the last decade, cryptocurrencies started to dominate both the social media and the financial press. At the same time, some academic papers use social media data to enhance the cryptocurrency volatility forecasting models. In our paper, we study how the use of Google Trends data could improve the precision of one-day-ahead of Bitcoin price volatility models forecasts. We use three different measures of the forecast precision. All models are estimated in rolling windows in order to control for possible structural breaks. Also, we estimate the optimal length of rolling windows to provide the best forecast precision on the historical Bitcoin price data from January 1, 2018 to December 31, 2022. We verify that the predictive power of the chosen model statistically differs from other models via MCS-test.