Global Stock Market Volatility Forecasting Incorporating Dynamic Graphs and All Trading Days
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916951260921856 |
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| author | Chi, Zhengyang Gao, Junbin Wang, Chao |
| author_facet | Chi, Zhengyang Gao, Junbin Wang, Chao |
| contents | This paper introduces a global stock market volatility forecasting model that enhances forecasting accuracy and practical utility in real-world financial decision-making by integrating dynamic graph structures and encompassing all active trading days of different stock markets. The model employs a spatial-temporal graph neural network architecture to capture the volatility spillover effect, where shocks in one market spread to others through the interconnective global economy. By calculating the volatility spillover index to depict the volatility network as graphs, the model effectively mirrors the volatility dynamics for the chosen stock market indices. In the empirical analysis covering 8 global market indices, the realized volatility forecasting performance of the proposed model surpasses the baseline models in all forecasting scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15320 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Global Stock Market Volatility Forecasting Incorporating Dynamic Graphs and All Trading Days Chi, Zhengyang Gao, Junbin Wang, Chao General Finance General Economics Economics This paper introduces a global stock market volatility forecasting model that enhances forecasting accuracy and practical utility in real-world financial decision-making by integrating dynamic graph structures and encompassing all active trading days of different stock markets. The model employs a spatial-temporal graph neural network architecture to capture the volatility spillover effect, where shocks in one market spread to others through the interconnective global economy. By calculating the volatility spillover index to depict the volatility network as graphs, the model effectively mirrors the volatility dynamics for the chosen stock market indices. In the empirical analysis covering 8 global market indices, the realized volatility forecasting performance of the proposed model surpasses the baseline models in all forecasting scenarios. |
| title | Global Stock Market Volatility Forecasting Incorporating Dynamic Graphs and All Trading Days |
| topic | General Finance General Economics Economics |
| url | https://arxiv.org/abs/2409.15320 |