Forecasting global stock market volatility: The impact of volatility spillover index in spatial‐temporal graph‐based model

Bumho Son, Yunyoung Lee, Seongwan Park and Jaewook Lee

Journal of Forecasting, 2023, vol. 42, issue 7, 1539-1559

Abstract: The shocks on certain market spread to other markets due to the financial linkages of global economy, which is known as volatility spillover effect. In this study, we propose a volatility forecasting model for global market indices using the spatial‐temporal graph neural network (GNN). The volatility spillover between markets are reflected in the model by estimating the linkage between markets, which is the input of GNN, using the volatility spillover index. An empirical analysis is conducted on eight representative global market indices. From the out‐of‐sample results, we found the following features. First, the proposed spatial‐temporal GNN spillover model outperforms the benchmark models in short‐ and mid‐term forecasting. Second, the forecasting accuracy highly depends on the inclusion of the market index with a high volatility spillover effect. Including S&P500, which contains the highest net spillover index, effectively helps to forecast the volatilities of other markets. Third, the investor can gain economic gain by using predicted volatility from proposed model in the mean‐variance framework.

Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/for.2975

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:42:y:2023:i:7:p:1539-1559

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().