Sectoral Volatility under Political Uncertainty: An Extended GARCH-X Modeling Framework

Manaf Ahmed () and Said Khalil ()
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Manaf Ahmed: College of Computer Science and Mathematics, University of Mosul, Iraq
Said Khalil: INSEA - Institut National de Statistique et d’Economie Appliquée [Rabat]

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Abstract: This paper examines how political uncertainty shapes the volatility of U.S. sectoral stock returns, focusing on economic policy uncertainty (EPU) and changes in presidential party control. Relying on monthly data from 48 industry portfolios covering the period from 1985 to 2024, we estimate a comprehensive set of volatility models, including GARCH-X, EGARCH, GJR-GARCH, IGARCH, and Markov-Switching GARCH. Political indicators are incorporated as exogenous drivers of conditional variance in order to capture their impact more precisely. Our findings point to significant cross-sectoral heterogeneity. Sectors such as Computers, Smoke, and Guns exhibit pronounced sensitivity to political variables, while others remain largely unaffected. Models that embed political uncertainty, particularly EPU, systematically improve explanatory power and, in certain cases, enhance forecast accuracy. These results highlight the importance of political signals in modeling sector-level financial risk and provide useful insights for both volatility forecasting and the design of policy-sensitive investment strategies.

Keywords: Political Uncertainty; Sectoral Volatility; GARCH-X; GJR-GARCH; Markov Switching; Financial Risk.; 2020 Mathematics Subject Classification. 62M10 91G70 62P05 91B84 Political Uncertainty Sectoral Volatility GARCH-X GJR-GARCH Markov Switching Financial Risk. JEL classification: C22 C58 G12 G17 D81; 2020 Mathematics Subject Classification. 62M10; 91G70; 62P05; 91B84 Political Uncertainty; Financial Risk. JEL classification: C22; C58; G12; G17; D81 (search for similar items in EconPapers)
Date: 2025-05-27
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