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Interconnectedness between cryptocurrencies and traditional financial assets: a multi-model time-frequency analysis.

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Titel: Interconnectedness between cryptocurrencies and traditional financial assets: a multi-model time-frequency analysis.
Autoren: Nițoi, Mihai1 (AUTHOR), Stanciu, Cristian Valeriu2 (AUTHOR) cristian.stanciu@feaa.ucv.ro, Clichici, Dorina1 (AUTHOR)
Quelle: Applied Economics. Nov2025, p1-19. 19p. 9 Illustrations.
Schlagwörter: *CRYPTOCURRENCIES, *PORTFOLIO diversification, *INVESTMENT management, *FORECASTING, *FINANCIAL risk management, TIME-frequency analysis
Abstract: This article investigates the dynamic interconnectedness between cryptocurrencies and traditional financial assets, exploring how volatility spillovers and predictive power of past returns evolve across time, frequency, and market conditions. Using daily data from January 2019 to April 2023, the analysis covers 21 cryptocurrencies, alongside key equity, bond, forex, and commodity markets. The empirical framework combines the connectedness models with complementary non-linear and dynamic approaches, namely Random Forest and Time-Varying Parameter models. Results reveal that cryptocurrencies act as net transmitters of volatility to traditional assets, particularly on short-term frequency during major stress episodes. The non-linear and dynamic models provide complementary insights, revealing structural changes and evolving dependencies that extend beyond linear connectedness patterns. Also, non-linear specifications unveil that traditional markets are the primary source of predictive information for the entire network. Overall, the findings emphasize the growing systemic relevance of crypto markets and provide actionable insights for investors, regulators, and financial institutions in monitoring risk transmission and designing diversification and stress-testing strategies. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
Beschreibung
Abstract:This article investigates the dynamic interconnectedness between cryptocurrencies and traditional financial assets, exploring how volatility spillovers and predictive power of past returns evolve across time, frequency, and market conditions. Using daily data from January 2019 to April 2023, the analysis covers 21 cryptocurrencies, alongside key equity, bond, forex, and commodity markets. The empirical framework combines the connectedness models with complementary non-linear and dynamic approaches, namely Random Forest and Time-Varying Parameter models. Results reveal that cryptocurrencies act as net transmitters of volatility to traditional assets, particularly on short-term frequency during major stress episodes. The non-linear and dynamic models provide complementary insights, revealing structural changes and evolving dependencies that extend beyond linear connectedness patterns. Also, non-linear specifications unveil that traditional markets are the primary source of predictive information for the entire network. Overall, the findings emphasize the growing systemic relevance of crypto markets and provide actionable insights for investors, regulators, and financial institutions in monitoring risk transmission and designing diversification and stress-testing strategies. [ABSTRACT FROM AUTHOR]
ISSN:00036846
DOI:10.1080/00036846.2025.2591003