Unveiling the interdependency of cryptocurrency and Indian stocks through wavelet and nonlinear time series analysis: An Econophysics approach

The study examines the dynamic interdependencies between the cryptocurrency market and the Indian stock market, focusing on India’s high exposure to crypto fluctuations due to its large investor base. This study uses both nonlinear and linear time series models such as wavelet coherence, wavelet-based Granger causality, transfer entropy, and DCC-GARCH to analyse information transfer, causality, and volatility spillovers, and phase portrait and complexity analyses were used to assess market stability and structural differences. The findings reveal a significant unidirectional influence from the cryptocurrency market to the Indian stock market, with no reverse causality. Transfer entropy measures identified unidirectional information flow from crypto market to Indian stock market. Wavelet coherence further identifies periods of strong synchronization, particularly during global events like the COVID-19 pandemic. The DCC-GARCH model confirms interconnected volatility dynamics, while phase portrait and complexity studies reveal the crypto market’s instability versus the stock market’s resilience. Investors should track crypto market trends, as the unidirectional information flow and persistent volatility spillovers suggest that stock market fluctuations can be projected by analyzing crypto market movements. Policymakers should implement measures to reduce crypto-induced market instability, particularly during economic downturns, to ensure that excessive volatility does not disrupt Indian stock market stability.