A regime switching model for temperature modeling and applications to weather derivatives pricing

Date

2020-01-01

Author

Turkvatan, Aysun
Hayfavi, Azize
Omay, Tolga

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

212
views

0
downloads

In this study, we propose a regime-switching model for temperature dynamics, where the parameters depend on a Markov chain. We improve upon the traditional models by modeling jumps in temperature dynamics via the chain itself. Moreover, we compare the performance of the proposed model with the existing models. The results indicate that the proposed model outperforms in the short time forecast horizon while the forecast performance of the proposed model is in line with the existing models for the long time horizon. It is shown that the proposed model is a relatively better representation of temperature dynamics compared to the existing models. Furthermore, we derive prices of weather derivatives written on several temperature indices.

Subject Keywords

Regime-switching, Markov chain, Expectation-maximization algorithm, Pricing, Weather derivatives

URI

https://hdl.handle.net/11511/29948

Journal

MATHEMATICS AND FINANCIAL ECONOMICS

DOI

https://doi.org/10.1007/s11579-019-00242-0

Collections

Graduate School of Applied Mathematics, Article

Suggestions

OpenMETU
Core

An fMRI segmentation method under markov random fields for brain decoding Aksan, Emre; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2015) In this study, a specially tailored segmentation method for partitioning the fMRI data into a set of "homogenous" regions with respect to a predefined cost function is proposed. The proposed method, referred as f-MRF, employs univariate and multivariate fMRI data analysis techniques under Markov Random Fields to estimate the segments by resolving a mixture density. The univariate approach helps identifying activation pattern of a voxel independently from other voxels. In order to capture local interactions ...
A generative model for multi class object recognition and detection Ulusoy, İlkay (2006-01-01) In this study, a generative type probabilistic model is proposed for object recognition. This model is trained by weakly labelled images and performs classification and detection at the same time. When test on highly challenging data sets, the model performs good for both tasks (classification and detection).
A LINEAR MATHEMATICAL-MODEL FOR THE SEISMIC INPLANE BEHAVIOR OF BRICK MASONRY WALLS .2. DETERMINATION OF MODEL PARAMETERS THROUGH OPTIMIZATION USING EXPERIMENTAL-DATA Sucuoğlu, Haluk; McNiven, Hugh (Wiley, 1984-01-01) The parameters appearing in the mixture and effective modulus models proposed in Part 1 are determined through optimization by matching theoretical and experimental responses. The optimization analysis is performed in frequency space. The response quantities chosen to be matched are the complex frequency response functions (experimental and theoretical) relating the Fourier transforms of top and base accelerations of the wall. Computations in optimization analysis are carried out by introducing an object (e...
An Evolutionary Algorithm for the Multi-objective Multiple Knapsack Problem SOYLU, Banu; Köksalan, Mustafa Murat (2009-06-26) In this study, we consider the multi-objective multiple knapsack problem (MMKP) and we adapt our favorable weight based evolutionary algorithm (FWEA) to approximate the efficient frontier of MMKP. The algorithm assigns fitness to solutions based on their relative strengths as well as their non-dominated frontiers. The relative strength is measured based on a weighted Tchebycheff distance from the ideal point where each Solution chooses its own weights that minimize its distance from the ideal point. We carr...
A Hilbert Space of Probability Mass Functions and Applications on the Sum-Product Algorithm Bayramoglu, Muhammet Fatih; Yılmaz, Ali Özgür (2008-09-05) In this paper a Hilbert space structure of probability mass functions (PMF) will be presented. The tools provided by the Hilbert space, specifically the norm and the inner product, may be useful while analyzing and improving the sum-product algorithm in many aspects. Our approach provides a metric distance between PMFs and a new point of a view of the log-likelihood ratio (LLR) such that the LLR representation is nothing but a Hilbert space representation.

Citation Formats

A. Turkvatan, A. Hayfavi, and T. Omay, “A regime switching model for temperature modeling and applications to weather derivatives pricing,” MATHEMATICS AND FINANCIAL ECONOMICS, pp. 1–42, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/29948.