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Regime Switching models on Temperature Dynamics
Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Mathematics, Computational Mathematics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-2681-8965
Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-9896-4438
Department od Mathematics, University of Dar el Salaam, Tanzania.
2017 (English)In: International Journal of Applied Mathematics and Statistics, ISSN 0973-1377, E-ISSN 0973-7545, Vol. 56, no 2Article in journal (Refereed) Published
Abstract [en]

Two regime switching models for predicting temperature dynamics are presented in this study for the purpose to be used for weather derivatives pricing. One is an existing model in the literature (Elias model) and the other is presented in this paper. The new model we propose in this study has a mean reverting heteroskedastic process in the base regime and a Brownian motion in the shifted regime. The parameter estimation of the two models is done by the use expectation-maximization (EM) method using historical temperature data. The performance of the two models on prediction of temperature dynamics is compared using historical daily average temperature data from five weather stations across Sweden. The comparison is based on the heating degree days (HDDs), cooling degree days (CDDs) and cumulative average temperature (CAT) indices. The expected HDDs, CDDs and CAT of the models are compared to the true indices from the real data. Results from the expected HDDs, CDDs and CAT together with their corresponding daily average plots demonstrate that, our model captures temperature dynamics relatively better than Elias model.

Place, publisher, year, edition, pages
2017. Vol. 56, no 2
Keyword [en]
Weather derivatives, Regime switching, temperature dynamics, expectationmaximization, temperature indices
National Category
Probability Theory and Statistics Computational Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-135541OAI: oai:DiVA.org:liu-135541DiVA: diva2:1083775
Available from: 2017-03-22 Created: 2017-03-22 Last updated: 2017-07-07
In thesis
1. Modelling Weather Dynamics for Weather Derivatives Pricing
Open this publication in new window or tab >>Modelling Weather Dynamics for Weather Derivatives Pricing
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis focuses on developing an appropriate stochastic model for temperature dynamics as a means of pricing weather derivative contracts based on temperature. There are various methods for pricing weather derivatives ranging from simple one like historical burn analysis, which does not involve modeling the underlying weather variable to complex ones that require Monte Carlo simulations to achieve explicit weather derivatives contract prices, particularly the daily average temperature (DAT) dynamics models. Among various DAT models, appropriate regime switching models are considered relative better than single regime models due to its ability to capture most of the temperature dynamics features caused by urbanization, deforestation, clear skies and changes of measurement station. A new proposed model for DAT dynamics, is a two regime switching models with heteroskedastic mean-reverting process in the base regime and Brownian motion with nonzero drift in the shifted regime. Before using the model for pricing temperature derivative contracts, we compare the performance of the model with a benchmark model proposed by Elias et al. (2014), interms of the HDDs, CDDs and CAT indices. Using ve data sets from dierent measurement locations in Sweden, the results shows that, a two regime switching models with heteroskedastic mean-reverting process gives relatively better results than the model given by Elias et al. We develop mathematical expressions for pricing futures and option contracts on HDDs, CDDs and CAT indices. The local volatility nature of the model in the base regime captures very well the dynamics of the underlying process, thus leading to a better pricing processes for temperature derivatives contracts written on various index variables. We use the Monte Carlo simulation method for pricing weather derivatives call option contracts.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. 21 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1784
National Category
Mathematics Computational Mathematics
Identifiers
urn:nbn:se:liu:diva-139253 (URN)10.3384/lic.diva-139253 (DOI)9789176854730 (ISBN)
Presentation
2017-08-17, BL32 (Nobel), Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2017-07-07 Created: 2017-07-07 Last updated: 2017-08-23Bibliographically approved

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