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Can Ethereum predict Bitcoin’s volatility?

Author

Listed:
  • Teterin, Maksim

    (HSE University, Moscow, Russian Federation,)

  • Peresetsky, Anatoly

    (HSE University, Moscow, Russian Federation)

Abstract

Bitcoin and Ethereum are the two world’s largest cryptocurrencies. Their market capitalizations have recently peaked, making them more attractive to portfolio investors. As the cryptocurrency market is known for its high volatility nature, government institutions are also interested in this segment of the financial market for more comprehensive regulation. Volatility forecasting is a crucial part of both effective risk management and policy making process, particularly in the cryptocurrency market. This study focuses on using volatility measurements of one cryptocurrency as input for a realized volatility predictive model of the other, namely Bitcoin’s data for Ethereum and vice versa. This approach helps us to identify potential spillover effects, which could be useful for more accurate forecasting. To this end, we use univariate HAR-RV models with additional exogenous variables based on data from another cryptocurrency and then extend our analysis using vector models to account for potential joint effects. We employ various loss functions to examine the accuracy of the forecasts. All models are estimated using rolling windows to account for potential structural breaks, resulting in over 2000 out-of-sample forecast exercises for each model and cryptocurrency, based on historical high-frequency Bitcoin and Ethereum trading data from January 1, 2018 to June 23, 2024. We also employ the MCS procedure to study which model provides statistically smaller prediction errors.

Suggested Citation

  • Teterin, Maksim & Peresetsky, Anatoly, 2025. "Can Ethereum predict Bitcoin’s volatility?," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 77, pages 74-90.
    • Handle: RePEc:ris:apltrx:0516
    as

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    More about this item

    Keywords

    Bitcoin; Ethereum; realized volatility; volatility prediction; cryptocurrency; HAR-RV model; realized covariance;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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