A Quantile Spillover-Driven Markov Switching Model for Volatility Forecasting: Evidence from the Cryptocurrency Market

Fangfang Zhu, Sicheng Fu () and Xiangdong Liu
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Fangfang Zhu: School of Economics, Jinan University, No. 601, West Huangpu Boulevard, Tianhe District, Guangzhou 510632, China
Sicheng Fu: School of Economics, Jinan University, No. 601, West Huangpu Boulevard, Tianhe District, Guangzhou 510632, China
Xiangdong Liu: School of Economics, Jinan University, No. 601, West Huangpu Boulevard, Tianhe District, Guangzhou 510632, China

Mathematics, 2025, vol. 13, issue 15, 1-37

Abstract: This paper develops a novel modeling framework that integrates time-varying quantile-based spillover effects into a regime-switching realized volatility model. A dynamic spillover factor is constructed by identifying the most influential contributors to Bitcoin’s realized volatility across different quantile levels. This quantile-layered structure enables the model to capture heterogeneous spillover paths under varying market conditions at a macro level while also enhancing the sensitivity of volatility regime identification via its incorporation into a time-varying transition probability (TVTP) Markov-switching mechanism at a micro level. Empirical results based on the cryptocurrency market demonstrate the superior forecasting performance of the proposed TVTP-MS-HAR model relative to standard benchmark models. The model exhibits strong capability in identifying state-dependent spillovers and capturing nonlinear market dynamics. The findings further reveal an asymmetric dual-tail amplification and time-varying interconnectedness in the spillover effects, along with a pronounced asymmetry between market capitalization and systemic importance. Compared to decomposition-based approaches, the X-RV type of models—especially when combined with the proposed quantile-driven factor—offers improved robustness and predictive accuracy in the presence of extreme market behavior. This paper offers a coherent approach that bridges phenomenon identification, source localization, and predictive mechanism construction, contributing to both the academic understanding and practical risk assessment of cryptocurrency markets.

Keywords: quantile spillover; dynamic quantile-driven factor; cryptocurrency market; realized volatility (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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