Evaluating Sectoral Vulnerability to Natural Disasters in the US Stock Market: Sectoral Insights from DCC-GARCH Models with Generalized Hyperbolic Innovations

Adriana AnaMaria Davidescu (), Eduard Mihai Manta, Margareta-Stela Florescu, Robert-Stefan Constantin and Cristina Manole
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Adriana AnaMaria Davidescu: Department of Statistics and Econometrics, The Bucharest University of Economic Studies, 010552 Bucharest, Romania
Eduard Mihai Manta: Department of Statistics and Econometrics, The Bucharest University of Economic Studies, 010552 Bucharest, Romania
Margareta-Stela Florescu: Department of Administration and Public Management, Bucharest University of Economic Studies, 010374 Bucharest, Romania
Robert-Stefan Constantin: Faculty of Economic Cybernetics, Statistics and Informatics, Bucharest University of Economic Studies, Calea Dorobanți 15–17, 010552 Bucharest, Romania
Cristina Manole: Department of Administration and Public Management, Bucharest University of Economic Studies, 010374 Bucharest, Romania

Sustainability, 2025, vol. 17, issue 18, 1-35

Abstract: The escalating frequency and severity of natural disasters present significant challenges to the stability and sustainability of global financial systems, with the US stock market being especially vulnerable. This study examines sector-level exposure and contagion dynamics during climate-related disaster events, providing insights essential for sustainable investing and resilient financial planning. Using an advanced econometric framework—dynamic conditional correlation GARCH (DCC-GARCH) augmented with Generalized Hyperbolic Processes (GHPs) and an asymmetric specification (ADCC-GARCH)—we model daily stock returns for 20 publicly traded US companies across five sectors (insurance, energy, automotive, retail, and industrial) between 2017 and 2022. The results reveal considerable sectoral heterogeneity: insurance and energy sectors exhibit the highest vulnerability, with heavy-tailed return distributions and persistent volatility, whereas retail and selected industrial firms demonstrate resilience, including counter-cyclical behavior during crises. GHP-based models improve tail risk estimation by capturing return asymmetries, skewness, and leptokurtosis beyond Gaussian specifications. Moreover, the ADCC-GHP-GARCH framework shows that negative shocks induce more persistent correlation shifts than positive ones, highlighting asymmetric contagion effects during stress periods. The results present the insurance and energy sectors as the most exposed to extreme events, backed by the heavy-tailed return distributions and persistent volatility. In contrast, the retail and select industrial firms exhibit resilience and show stable, and in some cases, counter-cyclical, behavior in crises. The results from using a GHP indicate a slight improvement in model specification fit, capturing return asymmetries, skewness, and leptokurtosis indications, in comparison to standard Gaussian models. It was also shown with an ADCC-GHP-GARCH model that negative shocks result in a greater and more durable change in correlations than positive shocks, reinforcing the consideration of asymmetry contagion in times of stress. By integrating sector-specific financial responses into a climate-disaster framework, this research supports the design of targeted climate risk mitigation strategies, sustainable investment portfolios, and regulatory stress-testing approaches that account for volatility clustering and tail dependencies. The findings contribute to the literature on financial resilience by providing a robust statistical basis for assessing how extreme climate events impact asset values, thereby informing both policy and practice in advancing sustainable economic development.

Keywords: DCC-GARCH; ADCC-GARCH; generalized hyperbolic processes; tail risk; asymmetry; natural disasters; climate risk; financial contagion; US stock market (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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