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Enhancing Bitcoin Price Volatility Estimator Predictions: A Four-Step Methodological Approach Utilizing Elastic Net Regression

Author

Listed:
  • Georgia Zournatzidou

    (Department of Accounting and Finance, Hellenic Mediterranean University, 71410 Heraklion, Greece)

  • Ioannis Mallidis

    (Department of Statistical and Insurance Science, University of Western Macedonia, 50100 Kozani, Greece)

  • Dimitrios Farazakis

    (Institute of Applied and Computational Mathematics, Foundation for Research and Technology Hellas (FORTH), 71110 Heraklion, Greece
    Department of Mathematics, University of Western Macedonia, 52100 Kastoria, Greece)

  • Christos Floros

    (Department of Accounting and Finance, Hellenic Mediterranean University, 71410 Heraklion, Greece)

Abstract

This paper provides a computationally efficient and novel four-step methodological approach for predicting volatility estimators derived from bitcoin prices. In the first step, open, high, low, and close bitcoin prices are transformed into volatility estimators using Brownian motion assumptions and logarithmic transformations. The second step determines the optimal number of time-series lags required for converting the series into an autoregressive model. This selection process utilizes random forest regression, evaluating the importance of each lag using the Mean Decrease in Impurity (MDI) criterion and optimizing the number of lags considering an 85% cumulative importance threshold. The third step of the developed methodological approach fits the Elastic Net Regression (ENR) to the volatility estimator’s dataset, while the final fourth step assesses the predictive accuracy of ENR, compared to decision tree (DTR), random forest (RFR), and support vector regression (SVR). The results reveal that the ENR prevails in its predictive accuracy for open and close prices, as these prices may be linear and less susceptible to sudden, non-linear shifts typically seen during trading hours. On the other hand, SVR prevails for high and low prices as these prices often experience spikes and drops driven by transient news and intra-day market sentiments, forming complex patterns that do not align well with linear modelling.

Suggested Citation

  • Georgia Zournatzidou & Ioannis Mallidis & Dimitrios Farazakis & Christos Floros, 2024. "Enhancing Bitcoin Price Volatility Estimator Predictions: A Four-Step Methodological Approach Utilizing Elastic Net Regression," Mathematics, MDPI, vol. 12(9), pages 1-15, May.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1392-:d:1387835
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    References listed on IDEAS

    as
    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    3. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    4. Mahdi Roozbeh & Mohammad Arashi, 2016. "Shrinkage ridge regression in partial linear models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(20), pages 6022-6044, October.
    5. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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