Multi-Market Systemic Risk Spillover Dataset with Network and Machine Learning Features

It covers twelve major stock market indices across North America, Europe, Asia, and Latin America. Researchers can use the dataset to examine systemic risk spillovers across global equity markets through network analysis and machine learning.

The data combine market-level financial indicators with spillover measures derived from the Diebold and Yilmaz framework, which supports analysis of financial contagion, risk transmission, and predictive modeling. Each observation includes daily returns, conditional volatility, spillovers transmitted, spillovers received, and net spillover positions. Lagged variables extend these indicators to capture short-term temporal dependence and create a high-dimensional feature set for machine learning models.