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Bitcoin: From the exchange equation to inverse leverage volatility forecasting

Author

Listed:
  • Krupochkin, A.

    (Plekhanov Russian University of Economics, Moscow, Russia)

  • Khominich, I.

    (Plekhanov Russian University of Economics, Moscow, Russia)

Abstract

This study aims to quantify the impact of Bitcoin's macro-network metrics - interpreted within a modified Fisher equation of exchange - on its market price, and to build a forecasting model for both the price and conditional volatility of Bitcoin. The empirical base consists of daily data from 2020 to 2024 (1,827 observations) covering total coin supply (TS), velocity of circulation (Vel), U.S.-dollar transaction volume (TV), and market price. Using principal component analysis (PCA), two orthogonal latent factors were extracted from these on-chain indicators, jointly explaining 91.1% of their total variation. The first factor is interpreted as a supply-scarcity factor (dominant loading from TS) and exerts a strong positive effect on price. The second factor, capturing transaction activity (associated with velocity and transaction volume), exhibits a statistically significant negative impact, thereby empirically confirming the "HODL effect" and the primacy of Bitcoin's investment function over its medium-of-exchange role. To account for temporal dynamics and volatility clustering, a combined SARIMA(1,1,0) (1,1,0)(0,1,0,90)-EGARCH(1,1) model with Student's t-distribution was applied. Within the EGARCH-t framework, a significant positive "inverse leverage effect" (asymmetry coefficient ? = 0.0317, p-value = 0.041) is identified, indicating that positive price shocks increase future volatility stronger than negative shocks of equal magnitude. The obtained results deepen the understanding of specific market risks of digital assets and offer a theoretically grounded toolkit for their forecasting.

Suggested Citation

  • Krupochkin, A. & Khominich, I., 2026. "Bitcoin: From the exchange equation to inverse leverage volatility forecasting," Journal of the New Economic Association, New Economic Association, vol. 71(2), pages 103-126.
    • Handle: RePEc:nea:journl:y:2026:i:71:p:103-126
    DOI: 10.31737/22212264_2026_2_103-126
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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