Mathematical modeling of climate change impacts on stock market behavior: Evidence from Tanzania

Climate change poses systemic financial risks to developing economies, yet nonlinear transmission mechanisms remain understudied. This paper examines how climate stress affects Tanzania's stock market a climate-vulnerable economy where agriculture contributes 25% of GDP. We develop a Multi-Regime Climate-Finance Threshold (MCFT) model, integrating a novel Composite Climate Stress Index (CCSI) that synthesizes temperature, precipitation, and extreme weather data. Using monthly data (February 2007 to December 2022), our dual-threshold vector error correction approach identifies two significant stress thresholds (τ₁=-0.385,τ₂=-0.184), delineating low, moderate, and high climate regimes. Results reveal strong nonlinearity, moderate stress raises market volatility by 18%, while high stress triggers 12% price corrections in exposed sectors. Cumulative financial impacts under high stress are 2.2 times larger than under low stress, with effects persisting for 10 months far exceeding developed-market estimates. Responses are asymmetric where negative shocks have 1.8 times greater impact than positive ones during high-stress periods. These findings highlight acute vulnerabilities in Tanzania's financial system and the inadequacy of climate-blind strategies. We recommend: (1) regime-triggered macroprudential policies, (2) climate-indexed financial instruments, and (3) integration of the CCSI into climate-contingent capital buffers. This study provides the first empirical evidence of climate-stress thresholds in an African emerging market and offers a replicable framework for assessing climate finance dynamics in vulnerable economies.