This study examines the dynamic and multiscale connectedness among cryptocurrencies, energy markets, macro-financial variables, and environmental indicators in the United States from January 2014 to June 2025. Using a hybrid framework that combines wavelet decomposition with a time-varying parameter vector autoregression (TVP-VAR), we assess spillovers across short-, medium-, and long-term horizons. The results reveal a persistently high level of systemic integration, with the Total Connectedness Index (TCI) ranging between 70 % and 90 % and reaching about 93 % at long horizons. Three contagion regimes are identified: energy-crypto dominance before 2020, financial synchronization during the COVID-19 crisis, and a macro-energy-environmental phase after 2021 that evolves into a digital-sustainability regime by 2025. The multiscale decomposition uncovers hidden directional reversals, Bitcoin price shifts from a short-term volatility transmitter to a long-run structural influencer, while transaction and capitalization variables move from emitters to receivers as horizons lengthen. Robustness tests using a rolling-window VAR confirm the persistence of these dynamics. Overall, the evidence shows that short-term contagion is speculative and energy-driven, whereas long-term connectedness is anchored in inflation, industrial production, and electricity prices. These findings offer actionable insights for investors and policymakers seeking to manage systemic risk and design sustainable strategies at the intersection of digital finance, energy markets, and environmental policy.
Zahra Zahedi, Mohammad Mehdi Arefi, Alireza Khayatian, Hamidreza Modares
In this paper, a solution for Nash equilibrium seeking problem for N-players static non-cooperative games with non-quadratic payoff functions is proposed. The proposed solution is a non-model based approach, in the sense that the players do not need any knowledge about the agent's model or other players' actions, and can attain the Nash equilibrium using only measurements of payoff values. To overcome the shortcoming of existing non-model based algorithms, for which the Nash equilibrium stays within a small neighborhood and oscillates, the proposed approach adjusts the classical extremum seeking algorithms so that the amplitude of excitation sinusoidal signal converges to zero locally and exponentially. Therefore, with removing steady-state oscillation, not only the deleterious effects of steady-state oscillation is eliminated but also Nash equilibrium is achieved faster. The details of proof and the analysis for stability and convergence are provided. Finally, the efficiency and effectiveness of the algorithm are illustrated with a numerical example and simulation.