LONG-TERM EQUILIBRIUM RELATIONSHIPS BETWEEN CRYPTOCURRENCIES AND THE ENERGY MARKET

Abstract

This study examines the long-run equilibrium relationships between cryptocurrency prices and energy futures by employing a comprehensive cointegration analysis. Using daily closing prices from 401 cryptocurrency series and 10 energy futures contracts, the investigation focuses on the systematic evaluation of long-term interdependencies across these distinct asset classes. Prior to analysis, stationarity was confirmed via the Augmented Dickey-Fuller test, ensuring that only series integrated of order one were considered. A series of pairwise cointegration tests was conducted, with the resulting statistics stored in a PostgreSQL database for subsequent retrieval and analysis.

The Johansen cointegration framework, a multivariate method capable of detecting multiple cointegrating relationships, was applied to the data. The analysis modeled the dynamics of asset prices in an error correction form, and the cointegration rank was determined using eigenvalue decomposition. Two test statistics, the trace and maximum eigenvalue statistics, were computed and compared against 95% critical values. The findings indicate that 1965 statistically significant cointegrated pairs were identified, corresponding to an overall cointegration rate of 49%. Notably, RBOB gasoline futures exhibited cointegration with nearly 100% of the cryptocurrency series, while the degree of cointegration varied across cryptocurrency categories, with metaverse tokens showing 100% cointegration, NFT/Gaming tokens 65.52%, stablecoins 63.46%, utility tokens 58.06%, and payment tokens 55.45%.

These results provide robust empirical evidence that, despite high short-term volatility, cryptocurrency prices are intrinsically linked to energy market dynamics. The differential cointegration across cryptocurrency categories further highlights the heterogeneous nature of digital asset markets. The findings challenge the prevailing view of cryptocurrencies as isolated speculative instruments and underscore the influence of fundamental economic factors, particularly energy costs, on long-run price behavior. The implications of this study extend to risk management, asset allocation, and the development of predictive models, thereby contributing to a more integrated understanding of digital and traditional asset markets.