Bitcoin Forecasting with Classical Time Series Models on Prices and Volatility

TL;DR

This study assesses classical time series models like ARIMA and EGARCH for Bitcoin price and volatility forecasting, demonstrating their effectiveness in short-term predictions despite cryptocurrency's high volatility.

Contribution

It provides a comprehensive evaluation of classical models on Bitcoin data, highlighting their strengths in short-term forecasting and volatility modeling.

Findings

ARIMA outperforms in short-term price prediction

EGARCH captures volatility asymmetry effectively

Classical models remain valuable despite crypto volatility

Abstract

This paper evaluates the performance of classical time series models in forecasting Bitcoin prices, focusing on ARIMA, SARIMA, GARCH, and EGARCH. Daily price data from 2010 to 2020 were analyzed, with models trained on the first 90 percent and tested on the final 10 percent. Forecast accuracy was assessed using MAE, RMSE, AIC, and BIC. The results show that ARIMA provided the strongest forecasts for short-run log-price dynamics, while EGARCH offered the best fit for volatility by capturing asymmetry in responses to shocks. These findings suggest that despite Bitcoin's extreme volatility, classical time series models remain valuable for short-run forecasting. The study contributes to understanding cryptocurrency predictability and sets the stage for future work integrating machine learning and macroeconomic variables.