Stochastic modelling of temperature for weather derivatives

Prelat, Nicolas
(2020)

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Prélat_63361200_2020.pdf
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Details

Supervisors
Hainaut, Donatien
Faculty
Faculté des sciences
Degree label
Master [120] en sciences actuarielles
Abstract
This thesis proposes and compares continuous-time stochastic models for temperature with the purpose of pricing weather derivatives. A Lévy driven Ornstein–Uhlenbeck process with time-dependent parameters and a convolution-closed Generalized Hyperbolic distribution is developed and then used to price derivatives on CAT, HDD and CDD indices. The models are implemented in Python and C++ with temperature data from Stockholm, Sweden.