TITLE:
Weather Derivatives with Applications to Canadian Data
AUTHORS:
Anatoliy Swishchuk, Kaijie Cui
KEYWORDS:
Weather Derivatives; Mean-Reverting Process; Lévy Process; Continuous Autoregressive Model; Fast Fourier Transform
JOURNAL NAME:
Journal of Mathematical Finance,
Vol.3 No.1,
February
26,
2013
ABSTRACT: We applied two daily average temperature models to Canadian cities data and derived their derivative pricing applications. The first model is characterized by mean-reverting Ornstein-Uhlenbeck process driven by general Lévy process with seasonal mean and volatility. As an extension to the first model, Continuous Autoregressive (CAR) model driven by Lévy process is also considered and calibrated to Canadian data. It is empirically proved that the proposed dynamics fitted CalgaryandTorontotemperature data successfully. These models are also applied to derivation of an explicit price of CAT futures, and numerical prices of CDD and HDD futures using fast Fourier transform. The novelty of this paper lies in the applications of daily average temperature models to Canadian cities data and CAR model driven by Lévy process, futures pricing of CDD and HDD indices.