Journal of the Iranian Statistical Society

Stochastic Models for Pricing Weather Derivatives using Constant Risk Premium

Authors

1 University of Manitoba, Canada

2 University of Connecticut, USA

10.29252/jirss.17.2.4

Abstract

 ‎Pricing weather derivatives is becoming increasingly useful‎, ‎especially in developing economies‎. ‎We describe a statistical model based approach for pricing weather derivatives by modeling and forecasting daily average temperatures data which exhibits long-range dependence‎. ‎We pre-process the temperature data by filtering for seasonality and volatility and fit autoregressive fractionally integrated moving average (ARFIMA) models‎, ‎employing the preconditioned conjugate gradient (PCG) algorithm for fast computation of the likelihood function‎. ‎We illustrate our approach using daily temperatures data from 1970 to 2008 for cities traded on the Chicago Mercantile Exchange (CME)‎, ‎which we employ for pricing degree days futures contracts‎. ‎We compare the statistical approach with traditional burn analysis using a simple additive risk loading principle for pricing‎, ‎where‎ ‎the risk premium is estimated by the method of least squares using data on observed prices and the corresponding estimate of prices from the best model we fit to the temperatures data.

Keywords

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Journal of the Iranian Statistical Society
Volume 17, Issue 2
December 2018
Pages 37-55
  • Receive Date: 23 July 2022
  • Revise Date: 12 May 2024
  • Accept Date: 23 July 2022