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Weather Derivatives Pricing: Modeling the Seasonal Residual Variance of an Ornstein-Uhlenbeck Temperature Process with Neural Network

Zapranis, Achilleas, Alexandridis, Antonis (2009) Weather Derivatives Pricing: Modeling the Seasonal Residual Variance of an Ornstein-Uhlenbeck Temperature Process with Neural Network. Neurocomputing, 73 (1-3). pp. 37-48. ISSN 0925-2312. (doi:10.1016/j.neucom.2009.01.018) (KAR id:29258)

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http://dx.doi.org/10.1016/j.neucom.2009.01.018

Abstract

In this paper, we use neural networks in order to model the seasonal component of the residual variance of a mean-reverting Ornstein–Uhlenbeck temperature process, with seasonality in the level and volatility. This approach can be easily used for pricing weather derivatives by performing Monte Carlo simulations. Moreover, in synergy with neural networks we use wavelet analysis to identify the seasonality component in the temperature process as well as in the volatility of the temperature anomalies. Our model is validated on more than 100 years of data collected from Paris, one of the European cities traded at Chicago Mercantile Exchange. Our results show a significant improvement over more traditional alternatives, regarding the statistical properties of the temperature process. This is important since small misspecifications in the temperature process can lead to large pricing errors.

Item Type: Article
DOI/Identification number: 10.1016/j.neucom.2009.01.018
Uncontrolled keywords: Weather derivatives; Wavelet analysis; Neural networks
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.87 Neural computers, neural networks
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Antonis Alexandridis
Date Deposited: 04 Apr 2012 11:58 UTC
Last Modified: 16 Nov 2021 10:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/29258 (The current URI for this page, for reference purposes)
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