Global Stock Market Volatility Forecasting Incorporating Dynamic Graphs and All Trading Days

Zhengyang Chi, Junbin Gao and Chao Wang

Papers from arXiv.org

Abstract: This study 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 the union of active trading days of different stock markets. The model employs a spatial-temporal graph neural network (GNN) 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, the proposed model surpasses the benchmark model in all forecasting scenarios and is shown to be sensitive to the underlying volatility interrelationships.

Date: 2024-09, Revised 2024-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
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