Time Series Analysis of Climate Change & Its Relationship with Stock Price

Abstract

Climate change has recently become a critical global concern, and its potential impact on financial markets has attracted significant attention. The study investigates the relationship between rising temperatures and the S&P 500 index, aiming to understand the implications of temperature changes on stock market performance. This research applies the Autoregressive Integrated Moving Average (ARIMA) model to analyze the relationship, using the linear and dynamic regression models to forecast the S&P 500 according to the ARIMA-fitted values of temperature change in the future. The findings from the dynamic regression model indicate that the rising temperature positively impacts the S&P 500, while the linear regression models show no correlation between these two. The study's findings support investors and policymakers in gaining a more comprehensive insight into the relationship and applying it to business practices. Furthermore, the study offers guidance to develop risk mitigation strategies within the financial sector.