Putting a price tag on temperature
Heng Xiong and
Rogemar Mamon ()
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Heng Xiong: University of Western Ontario
Rogemar Mamon: University of Western Ontario
Computational Management Science, 2018, vol. 15, issue 2, 259-296
Abstract A model for the evolution of daily average temperatures (DATs) is put forward to support the analysis of weather derivatives. The goal is to capture simultaneously the stochasticity, mean-reversion, and seasonality patterns of the DATs process. An Ornstein–Uhlenbeck (OU) process modulated by a hidden Markov chain (HMC) is proposed to model both the mean-reversion and stochasticity of a deseasonalised component. The seasonality part is modelled by a combination of linear and sinusoidal functions. Modified and more efficient OU–HMM filtering algorithms relative to the current ones in the literature are presented for the evolution of adaptive and switching model parameter estimates. Numerical implementation of the estimation technique using a 4-year Toronto temperature data set compiled by the National Climatic Data Center was conducted. A sensitivity analysis of the option prices with respect to model parameters is included.
Keywords: Temperature-based derivatives; Regime-switching model; Maximum likelihood; Filtering; Change of reference probability measure; Online parameter estimation (search for similar items in EconPapers)
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