Multi-Regime Smooth Transition Stochastic Volatility Models for Financial Time Series
Stochastic volatility (SV) models effectively capture the time-varying variance in financial time series, and regime-switching SV models further enhance flexibility by adapting to changing market conditions. However, these models often fail to account for the asymmetric response of volatility to lar...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Data Science in Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/26941899.2025.2517013 |
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| Summary: | Stochastic volatility (SV) models effectively capture the time-varying variance in financial time series, and regime-switching SV models further enhance flexibility by adapting to changing market conditions. However, these models often fail to account for the asymmetric response of volatility to large negative versus positive asset returns. To address this limitation, we propose a novel regime-switching autoregressive SV framework featuring smooth transitions between regimes conditioned on auxiliary covariates such as trading volume. Our approach combines the adaptability of regime-switching models with the nuanced dynamics of smooth transitions, offering improved modeling of volatility asymmetry. Through a comprehensive simulation study and an empirical application to British Petroleum’s log stock returns, we demonstrate the effectiveness and flexibility of the proposed method. |
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| ISSN: | 2694-1899 |