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Tracking ‘Pure’ Systematic Risk with Realized Betas for Bitcoin and Ethereum

Author

Listed:
  • Bilel Sanhaji

    (LED - Laboratoire d'Economie Dionysien - UP8 - Université Paris 8)

  • Julien Chevallier

    (LED - Laboratoire d'Economie Dionysien - UP8 - Université Paris 8)

Abstract

Using the capital asset pricing model, this article critically assesses the relative importance of computing ‘realized' betas from high-frequency returns for Bitcoin and Ethereum—the two major cryptocurrencies—against their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present until 4 min in the BTC and ETH high-frequency data. Therefore, we opt for a conservative choice with a 60 min sampling frequency. Considering 250 trading days as a rolling-window size, we obtain rolling betas 0.8 for BTC (β > 0.65 for ETH). The weekly frequency is thus revealed as being less precise for capturing the ‘pure' systematic risk for Bitcoin and Ethereum. For Ethereum in particular, the availability of high-frequency data tends to produce, on average, a more reliable inference. In the age of financial data feed immediacy, our results strongly suggest to pension fund managers, hedge fund traders, and investment bankers to include ‘realized' versions of CAPM betas in their dashboard of indicators for portfolio risk estimation. Sensitivity analyses cover jump detection in BTC/ETH high-frequency data (up to 25%). We also include several jump-robust estimators of realized volatility, where realized quadpower volatility prevails.

Suggested Citation

  • Bilel Sanhaji & Julien Chevallier, 2023. "Tracking ‘Pure’ Systematic Risk with Realized Betas for Bitcoin and Ethereum," Post-Print hal-04218488, HAL.
    • Handle: RePEc:hal:journl:hal-04218488
    DOI: 10.3390/econometrics11030019
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    Cited by:

    1. Doan, Bao & Jayasuriya, Dulani & Lee, John B. & Reeves, Jonathan J., 2024. "Cryptocurrency systematic risk dynamics," Economics Letters, Elsevier, vol. 241(C).
    2. Jian Zhou, 2025. "Forecasting Beta Using Ultra High Frequency Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 485-496, March.
    3. Julien Chevallier & Bilel Sanhaji, 2023. "Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices," Stats, MDPI, vol. 6(4), pages 1-32, December.

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