Volatility Dynamics of Climate-Driven Health Risks: Applications of Garch Family Models

Climate change has emerged as one of the most pressing global challenges, with significant implications for human health. Variations in temperature, rainfall, and extreme weather events are increasingly linked to outbreaks of vector-borne diseases, respiratory illnesses, and heat-related morbidity. While prior research has primarily focused on linear relationships between climate and health outcomes, limited attention has been given to the volatility dynamics underlying these interactions. This study addresses this gap by applying the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) family of models to investigate the persistence, clustering, and asymmetry of climate-induced health risks in Nigeria. Monthly climate data (temperature anomalies and rainfall deviations) were obtained from the Nigerian Meteorological Agency (NIMET) and the Climate Research Unit (CRU), while health data (malaria and respiratory infections) were sourced from the Nigerian Centre for Disease Control (NCDC), covering the period January 2000 to December 2022. Following logarithmic transformation of health outcomes to stabilize variance, GARCH (1,1), EGARCH (1,1), and TGARCH (1,1) models were estimated. The findings indicate strong volatility persistence in malaria incidence driven by rainfall anomalies, alongside asymmetric effects in respiratory infections associated with temperature shocks. Volatility clustering was evident in both diseases, highlighting the nonlinear and unpredictable nature of climate–health interactions. This study contributes to the literature by moving beyond correlation-based frameworks and introducing volatility modeling to the climate–health nexus. The results provide evidence that extreme climate shocks exert disproportionate impacts on health risks, suggesting the need for early warning systems and adaptive public health planning. By highlighting volatility persistence and asymmetry, the findings support the integration of advanced statistical modeling into disease surveillance and climate adaptation strategies. Keywords: climate change, health risks, volatility dynamics, GARCH models, Nigeria