4 research outputs found
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Seasonal time-series modeling and forecasting of monthly mean temperature for decision making in the Kurdistan Region of Iraq
A generalized structural time-series modeling framework was used to analyze the monthly records of mean temperature, one of the most important environmental parameters, using classical stochastic processes. In this article we are using the SARIMA Box–Jenkins model and obtain a medium-term (10 years) forecast of the mean temperature in Erbil. A prediction of the monthly mean temperature during the past 287 months ((Formula presented.)24 years) using the SARIMA(0,1,2)(0,1,1)12 model predicts that the average temperature in the governorate of Erbil, Iraq, will be stable for the next 10 years. The evaluation of prediction accuracy shows that our model performs equally well when applying it to different periods of time for which data is available. The method used here could easily be applied by the decision makers responsible for providing water and electricity in the Kurdistan Region
Temperature stochastic modeling and weather derivatives pricing: empirical study with Moroccan data
The main objective of this paper is to present a technique for pricing weather derivatives with payout depending on temperature. We start by using the Principle Component Analysis method to fill missing temperature data. Consequently,
the cold and the warm periods were determined on the basis of a “clean” data by using a statistical approach. After that, we use historical data over a sufficient period to apply a stochastic process that describes the evolution of the temperature. A numerical example of a swap contract pricing is presented, using an approximation formula as well as Monte Carlo simulations.
Keywords: Weather derivatives, temperature stochastic model, Monte Carlo simulation