Assessing and hedging the cost of unseasonal weather: Case of the apparel sector (2015)

Retail activities are increasingly exposed to unseasonable weather conditions that lead to losses in sales and profits, as climate change exacerbates climate variability. Although research has improved our understanding of the role of weather conditions on consumption, little is known about the precise relationship between weather conditions and sales for strategic and financial decision-making. Using clothing as an illustration for all seasons, we estimate the impact on sales caused by unexpected deviations of daily temperature from seasonal patterns. We apply seasonal trend decomposition using Loess to isolate changes in sales volumes. We use linear regression to find the relationship between temperature and sales anomalies, and we build the historical distribution to determine at-risk sales due to unseasonable weather conditions. We show how to use weather derivatives to offset potential loss. Our contribution is twofold: we provide a new general method enabling managers to understand how their performance is linked to weather conditions, and we propose a bespoke weather derivatives model to mitigate this risk.