Cryptocurrency Price Forecasting with GARCH Model and LS - SVM Regression: An Application on Bitcoin Price

The aim of this study is to determine whether successful predictions for cryptocurrencies such as Bitcoin can be obtained with different methods. The reason why Bitcoin prices (Bitcoin / $) are used in the study is that this cryptocurrency is still the most widely used cryptocurrency in the market, and the idea that it will successfully represent the overall state of the cryptocurrencies market. Financial market series may contain fluctuations for some reason, such as speculations. It also usually includes nonlinear changes. Such features lead to failures in obtaining forecasts for financial time series. In this study, with the GARCH model, one of the classicial time series models and LS - SVM method, a machine learning method, predictions of the Bitcoin price series were obtained, and model performances were compared. In the study, between January 01, 2017 and February 29, 2020, 1155 daily Bitcoin price series ( ) was used. In both models, the Bitcoin price series and the volatilities of this series were used, and external variables were not included in the models. For both models, forecasts were obtained for periods of 1 month, 2 months and 3 months. For GARCH and LS - SVM models, out of sample successful forecasting rates according to MAPE ratios were 98,0347% - 95,3423% for 1 month; 97,9544% - 96,1307% for 2 months and 98,1272% - 91,4874% for 3 months, respectively. The GARCH model has provided more successful results for all three periods. The finding of the study is that the GARCH model can be used to obtain forecasts for the crypto price series.

Keywords

Financial Time Series Analysis, Applied Statistics, Bitcoin, Forecasting, GARCH Model, LS - SVM Regression