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- Title
Putting a price tag on temperature.
- Authors
Xiong, Heng; Mamon, Rogemar
- 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.
- Subjects
LOW temperatures; WEATHER; PRICE marks; ALGORITHMS; NUMERICAL analysis
- Publication
Computational Management Science, 2018, Vol 15, Issue 2, p259
- ISSN
1619-697X
- Publication type
Article
- DOI
10.1007/s10287-017-0291-8