A Stochastic Weather Model: A Case of Bono Region of Ghana

TL;DR

This paper models Ghana's Bono region temperature using a seasonal Ornstein-Uhlenbeck process with stochastic volatility, providing insights for weather derivatives and agricultural insurance design.

Contribution

It introduces a modified Ornstein-Uhlenbeck model with seasonal mean and volatility for temperature modeling in Ghana, capturing 50% of temperature variation.

Findings

Region experiences warm temperatures up to 32.67°C

Temperature reverts to ~26°C at 18.72% rate

Model explains about 50% of temperature variation

Abstract

The paper sought to fit an Ornstein Uhlenbeck model with seasonal mean and volatility, where the residuals are generated by a Brownian motion for Ghanian daily average temperature. This paper employed the modified Ornstein Uhlenbeck model proposed by Bhowan which has a seasonal mean and stochastic volatility process. The findings revealed that, the Bono region experiences warm temperatures and maximum precipitation up to 32.67 degree celsius and 126.51mm respectively. It was observed that the Daily Average Temperature (DAT) of the region reverts to a temperature of approximately 26 degree celsius at a rate of 18.72% with maximum and minimum temperatures of 32.67degree celsius and 19.75degree celsius respectively. Although the region is in the middle belt of Ghana, it still experiences warm(hot) temperatures daily and experiences dry seasons relatively more than wet seasons in the number of years considered for our analysis. Our model explained approximately 50% of the variations in the daily average temperature of the region which can be regarded as relatively a good model. The findings of this paper are relevant in the pricing of weather derivatives with temperature as an underlying variable in the Ghanaian financial and agricultural sector. Furthermore, it would assist in the development and design of tailored agriculture/crop insurance models which would incorporate temperature dynamics rather than extreme weather conditions/events such as floods, drought and wildfires.