Modelling the Temperature Time‐dependent Speed of Mean Reversion in the Context of Weather Derivatives Pricing

Zapranis, Achilleas; Alexandridis, Antonis

doi:10.1080/13504860802006065

Cited by 64 publications

(60 citation statements)

References 20 publications

(25 reference statements)

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“…6. The same conclusion was reached in previous studies for daily models for both temperature and wind, (Alexandridis 2010;Zapranis and Alexandridis 2008 …”

Section: The Linear Arma Modelsupporting

confidence: 70%

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Wind Derivatives: Modeling and Pricing

Alexandridis

Zapranis

2012

Comput Econ

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Abstract.Wind is considered to be a free, renewable and environmentally friendly source of energy. However, wind farms are exposed to excessive weather risk since the power production depends on the wind speed and the wind direction. This risk can be successfully hedged using a financial instrument called weather derivatives. In this study the dynamics of the wind generating process are modeled using a non-parametric non-linear wavelet network. Our model is validated in New York. The proposed methodology is compared against alternative methods, proposed in prior studies. We find that wavelet networks can model the wind process very well and consequently they constitute an accurate and efficient tool for wind derivatives pricing. Finally, we provide the pricing equations for wind futures.

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“…6. The same conclusion was reached in previous studies for daily models for both temperature and wind, (Alexandridis 2010;Zapranis and Alexandridis 2008 …”

Section: The Linear Arma Modelsupporting

confidence: 70%

“…2 the histogram of the transformed data can be found while the second row of Table 1 shows the descriptive statistics of the transformed data. (Zapranis and Alexandridis 2008 for the case of temperature process, the seasonal effects are modeled by a truncated Fourier series given by:…”

Section: Wind Speed Modelingmentioning

confidence: 99%

Wind Derivatives: Modeling and Pricing

Alexandridis

Zapranis

2012

Comput Econ

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“…In [10] and [11] we give a concise treatment of wavelet theory. Here the emphasis is in presenting the theory and mathematics of wavelet neural networks.…”

Section: Wavelet Neural Network For Multivariate Process Modelingmentioning

confidence: 99%

Model Identification in Wavelet Neural Networks Framework

Zapranis

Alexandridis

2009

IFIP Advances in Information and Communication Technology

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The scope of this study is to present a complete statistical framework for model identification of wavelet neural networks (WN). In each step in WN construction we test various methods already proposed in literature. In the first part we compare four different methods for the initialization and construction of the WN. Next various information criteria as well as sampling techniques proposed in previous works were compared to derive an algorithm for selecting the correct topology of a WN. Finally, in variable significance testing the performance of various sensitivity and model-fitness criteria were examined and an algorithm for selecting the significant explanatory variables is presented.

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“…In [10] and [11] we give a concise treatment of wavelet theory. Here the emphasis is in presenting the theory and mathematics of wavelet neural networks.…”

Section: Wavelet Neural Network For Multivariate Process Modelingmentioning

confidence: 99%

Artificial Intelligence Applications and Innovations III

Iliadis¹,

Maglogiann²,

Tsoumakasis³

et al. 2009

IFIP Advances in Information and Communication Technology

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-The scope of this study is to present a complete statistical framework for model identification of wavelet neural networks (WN). In each step in WN construction we test various methods already proposed in literature. In the first part we compare four different methods for the initialization and construction of the WN. Next various information criteria as well as sampling techniques proposed in previous works were compared in order to derive an algorithm for selecting the correct topology of a WN. Finally, In variable significance testing the performance of various sensitivity and model-fitness criteria were examined and an algorithm for selecting the significant explanatory variables is presented.

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Modelling the Temperature Time‐dependent Speed of Mean Reversion in the Context of Weather Derivatives Pricing

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 64 publications

References 20 publications

Wind Derivatives: Modeling and Pricing

Wind Derivatives: Modeling and Pricing

Model Identification in Wavelet Neural Networks Framework

Artificial Intelligence Applications and Innovations III

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