Retail activities are increasingly exposed to unseasonal weather causing lost sales and profits, as climate change is aggravating climate variability. Although research has provided insights into the role of weather on consumption, little is known about the precise relationship between weather and sales for strategic and financial decision-making. Using apparel 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 a linear regression to find the relationship between temperature and sales anomalies and construct the historical distribution to determine sales-at-risk due to unseasonal weather. We show how to use weather derivatives to offset the potential loss. Our contribution is twofold. We provide a new general method for managers to understand how their performance is weather-related. We lay out a blueprint for tailor-made weather derivatives to mitigate this risk. 
Assessing and hedging the cost of unseasonal weather: Case of the apparel sector