Daily Temperature Modelling for Weather Derivative Pricing - A Comparative Index Forecast Analysis of Adapted Popular Temperature Models

Abstract

This study aims to construct improved daily air temperature models to obtain more precise index values for New York LaGuardia and, thus, more accurate weather derivative prices for contracts written on that location. The study shows that dynamic temperature submodels using a quadratic trend on a 50-year dataset generally produce more accurate forecast results than the studied models that do not. Moreover, the market model outperforms all other models up to 19 months ahead in the future.