Bitcoin Forecasting with Classical Time Series Models on Prices and Volatility

ArXiv ID: 2511.06224 “View on arXiv”

Authors: Anmar Kareem, Alexander Aue

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.

Keywords: Time Series Forecasting, ARIMA, GARCH, EGARCH, Bitcoin Volatility

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail
  • Why: The paper uses advanced time series models (ARIMA, SARIMA, GARCH, EGARCH) requiring statistical theory and model diagnostics, scoring high on math. It also demonstrates strong empirical rigor with a real dataset (Bitcoin 2010-2020), a clear train/test split, and standard forecasting metrics (MAE, RMSE, AIC, BIC).
  flowchart TD
    A["Research Goal:<br>Forecast Bitcoin Prices & Volatility<br>using Classical Time Series Models"] --> B["Data Input:<br>Daily Bitcoin Prices (2010-2020)<br>Train: 90% | Test: 10%"]
    B --> C["Methodology:<br>ARIMA, SARIMA, GARCH, EGARCH"]
    C --> D["Evaluation Metrics:<br>MAE, RMSE, AIC, BIC"]
    D --> E["Key Findings / Outcomes"]
    E --> F["Price Forecasting:<br>ARIMA best for<br>short-run log-prices"]
    E --> G["Volatility Modeling:<br>EGARCH best fit<br>(captures asymmetry)"]
    E --> H["Conclusion:<br>Classical models remain<br>valuable despite volatility"]