article · The Quarterly Review of Economics and Finance
Daily return volatility across 28 advanced and developing equity markets can be predicted using monthly energy-related uncertainty indexes, measured both globally and at the country level. By evaluating data at original frequencies to prevent aggregation bias, analysis shows that rising energy-related uncertainty consistently increases stock return volatility during in-sample evaluations. Furthermore, this predictive ability remains robust across various out-of-sample forecasting horizons. When using a GARCH-MIDAS modelling framework, forecasting precision improves more notably when incorporating global energy-related uncertainty metrics than when relying solely on country-specific measures. These relationships remain consistent across alternative choices of uncertainty indexes and different sample definitions, providing relevant empirical insight for financial institutions and international policymakers seeking to anticipate financial market instability driven by energy sector disruptions.
Fluctuations in global energy markets can trigger widespread instability across international financial markets. Understanding how energy-related uncertainty shapes stock market volatility helps central banks, regulators, and institutional investors anticipate systemic market shocks. By capturing shifts in market risk early, stakeholders can formulate better responses to maintain the stability of broader economic and financial systems during periods of intense energy disruption.
The predictive framework demonstrates an applied quantitative approach that could be integrated into market risk monitoring systems, financial forecasting software, or portfolio management tools used by investment managers and regulatory bodies. Because the study validates model performance using out-of-sample data across multiple international markets, the methodology appears tested and ready for analytical deployment by financial data providers seeking to enhance economic forecasting platforms.
AI-generated from the published abstract. Always read the original work before citing.
This paper predicts the daily return volatility of 28 advanced and developing stock markets using monthly metrics of the corresponding country and global energy-related uncertainty indexes (EUIs) recently proposed in the literature. Using data in their “natural” frequencies to avoid aggregation bias, the results show that country-specific and global EUIs have predictive powers for stock returns volatility for the in-sample periods, with increased levels of EUIs exhibiting the tendency to heighten volatility. This predictability also withstands various out-of-sample forecast horizons, implying that EUI is a statistically relevant predictor in the out-of-sample analysis. The forecast precision of the GARCH-MIDAS model is improved by incorporating global EUIs relatively more than country-specific EUIs. The robustness of the findings with respect to the choice of EUI and sample definition is further confirmed. The outcomes have important policy implications for the concerned stakeholders who are concerned with stability in the global financial system and economy.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.qref.2024.04.005
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
