The Relationship Between Spot and Future Cryptocurrencies: A VECM Approach

Background and Objectives: The rapid expansion of cryptocurrency derivatives markets has fundamentally reshaped price formation, risk transmission, and informational efficiency in digital asset ecosystems. Among these assets, Bitcoin occupies a dominant position, with spot and futures markets jointly influencing trading behavior, volatility dynamics, and inflationary spillovers into broader financial systems. While economic theory predicts a close linkage between underlying assets and their derivatives, the nature of this relationship in cryptocurrency markets remains complex due to extreme volatility, fragmented trading venues, and the absence of a centralized regulatory framework. Existing empirical studies provide mixed evidence on whether Bitcoin futures stabilize spot markets through improved price discovery or amplify volatility through speculative trading, particularly during crisis episodes. Moreover, much of the literature examines long-run equilibrium, short-run dynamics, or volatility spillovers in isolation, without integrating these dimensions within a unified analytical framework. Against this background, this study aims to investigate the short- and long-run relationships between Bitcoin spot and futures markets, to assess their roles in price discovery and volatility transmission, and to examine how major crisis episodes—specifically the COVID-19 pandemic and the Silicon Valley Bank (SVB) event—affect market connectedness. Methodology: The study employs daily data on Bitcoin spot and futures prices spanning the period from December 29, 2017, to February 18, 2025. The empirical analysis follows a multi-stage econometric strategy. First, unit root and cointegration tests are conducted to establish the time-series properties of the data and the existence of a long-run equilibrium relationship. A Vector Error Correction Model (VECM) is then estimated to capture both long-run cointegrating relationships and short-run adjustment dynamics, allowing for an explicit assessment of price discovery roles between spot and futures markets. To further explore time-varying interdependence and volatility spillovers, a two-step volatility framework is adopted. In the first step, a BEKK-GARCH model is used to model the dynamic variance–covariance structure and to extract conditional correlations between spot and futures returns. In the second step, these correlations are analyzed using a GJR-GARCH specification to capture asymmetric volatility effects and to quantify the impact of crisis episodes through event-specific dummy variables. This integrated approach enables a comprehensive examination of mean dynamics, volatility transmission, and crisis sensitivity within a single analytical framework. Key Findings: The empirical results provide strong evidence of a stable long-run cointegrating relationship between Bitcoin spot and futures prices, indicating that the two markets are closely linked over time. Short-run dynamics reveal a clear asymmetry in adjustment behavior: deviations from the long-run equilibrium are primarily corrected through movements in the spot market, while the futures market plays a leading informational role, consistent with its function in price discovery. Volatility analysis uncovers significant and persistent bidirectional spillovers between spot and futures markets, suggesting a high degree of dynamic interdependence. The estimated GARCH parameters indicate strong volatility persistence and pronounced asymmetric effects, whereby negative shocks exert a larger and more persistent influence on market connectedness than positive shocks of similar magnitude. Crisis-specific analysis shows that the COVID-19 pandemic significantly weakened spot–futures connectedness, reflecting heightened uncertainty and structural disruption during periods of systemic stress. In contrast, the SVB episode does not exhibit a statistically significant impact on market interdependence, suggesting that not all fin