EventsThe 2nd International Online Conference on Risk and Financial Management
Published
This submission belongs to the session S3. Blockchain and Digital Assets: Frontier Analytics of the event The 2nd International Online Conference on Risk and Financial Management
Published date
28 Oct, 2026
Academic Editor
author-avatarAlessio Faccia
Citation
Rosella Castellano, Federico Cini, Annalisa Ferrari, Energy Risk and Bitcoin Valuation Across Protocol-Defined Regimes, in Proceedings of The 2nd International Online Conference on Risk and Financial Management, 2 November–4 November 2026, MDPI: Basel, Switzerland
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Energy Risk and Bitcoin Valuation Across Protocol-Defined Regimes

Rosella Castellano 1
1. Department of Law and Economics, University of Rome UnitelmaSapienza, Piazza Sassari 4, Roma, 00161,Italy.
2. Department of Social Sciences and Economics, Sapienza University of Rome, Piazzale Aldo Moro 5, Roma, 00185,Italy.
3. Department of Law and Digital Society, University of Rome UnitelmaSapienza, Piazza Sassari 4, Roma, 00161,Italy
Abstract

This study examines Bitcoin valuation within a regime-dependent framework that combines macro-financial conditions, investor attention, and energy-related drivers. We develop a semi-structural representation in which Bitcoin market capitalization is linked to an equilibrium-consistent set of fundamentals. The analysis is conducted separately across four halving regimes, allowing the valuation mapping to evolve with Bitcoin’s protocol and market maturation. Long-run relations are estimated using Dynamic Ordinary Least Squares (DOLS) as a linear benchmark and complemented by XGBoost as a flexible nonlinear in-sample approximator. In the short run, residual-based valuation gaps are incorporated into an error-correction framework to assess whether deviations from the estimated benchmark help explain subsequent adjustments in Bitcoin market capitalization. The results show that Bitcoin valuation is regime-dependent. Equity-market conditions provide the most stable valuation anchor across specifications, pointing to a growing integration of Bitcoin into the broader financial system. Investor attention is more relevant in the early regimes, while energy-related variables become more informative when they are linked to production-side incentives. In particular, mining profitability emerges as the strongest empirical energy-related signal. In the short run, valuation gaps display mean-reverting dynamics, and nonlinear gaps yield stronger and faster adjustment signals than the linear benchmark. Bitcoin valuation is best understood through a regime-specific framework combining macro-financial integration, protocol-driven scarcity, and miner-related incentives.

Keywords
Bitcoin valuation
energy risk
halving regimes
error-correction model
machine learning
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