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UM E-Theses Collection (澳門大學電子學位論文庫)

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Title

Pricing weather derivatives under mean reverting jump process

English Abstract

In the temperature derivatives market, there is a problem about how to choose an arbitrage free price. This thesis mathematically defines different models of the temperature index, and builds models for temperature derivatives. After the careful analysis of seasonal model, this thesis proposes an Ornstein-Uhlenbeck process with dynamic volatility and jump for the temperature evolution in time, and fits this model with the transition density function maximum likelihood estimation method to data observed in Macau, China. The proposed model has been proved that, in Lemma 3, satisfies the crucial condition that the expectation must equal to seasonal model. Using the maximum likelihood estimation method with the transition density function, the evaluation result of parameters is fast, accurate and trusted. Futures price for contracts on CAT index is derived. In the Monte Carlo experiment, generation method of jump is significantly optimized, thus the simulation is more realistic and more stable.

Issue date

2015

Author

Cao, Zhi Jie

Faculty

Faculty of Science and Technology

Degree

M.Sc.

Subject

Weather derivatives

Mathematics -- Department of Mathematics

Supervisor

Ding, Deng

Files In This Item

Full-text (Intranet)

Location
1/F Zone C
Library URL
991000744989706306