A Predictive Framework Integrating Multi-Scale Volatility Components and Time-Varying Quantile Spillovers: Evidence from the Cryptocurrency Market
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913965863337984 |
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| author | Fu, Sicheng Zhu, Fangfang Liu, Xiangdong |
| author_facet | Fu, Sicheng Zhu, Fangfang Liu, Xiangdong |
| contents | This paper investigates the dynamics of risk transmission in cryptocurrency markets and proposes a novel framework for volatility forecasting. The framework uncovers two key empirical facts: the asymmetric amplification of volatility spillovers in both tails, and a structural decoupling between market size and systemic importance. Building on these insights, we develop a state-adaptive volatility forecasting model by extracting time-varying quantile spillover features across different volatility components. These features are embedded into an extended Log-HAR structure, resulting in the SA-Log-HAR model. Empirical results demonstrate that the proposed model outperforms benchmark alternatives in both in-sample fitting and out-of-sample forecasting, particularly in capturing extreme volatility and tail risks with greater robustness and explanatory power. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Predictive Framework Integrating Multi-Scale Volatility Components and Time-Varying Quantile Spillovers: Evidence from the Cryptocurrency Market Fu, Sicheng Zhu, Fangfang Liu, Xiangdong General Economics Economics This paper investigates the dynamics of risk transmission in cryptocurrency markets and proposes a novel framework for volatility forecasting. The framework uncovers two key empirical facts: the asymmetric amplification of volatility spillovers in both tails, and a structural decoupling between market size and systemic importance. Building on these insights, we develop a state-adaptive volatility forecasting model by extracting time-varying quantile spillover features across different volatility components. These features are embedded into an extended Log-HAR structure, resulting in the SA-Log-HAR model. Empirical results demonstrate that the proposed model outperforms benchmark alternatives in both in-sample fitting and out-of-sample forecasting, particularly in capturing extreme volatility and tail risks with greater robustness and explanatory power. |
| title | A Predictive Framework Integrating Multi-Scale Volatility Components and Time-Varying Quantile Spillovers: Evidence from the Cryptocurrency Market |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2507.22409 |