Abstract
In this paper, we use wavelet neural networks in order to model a mean-reverting Ornstein-Uhlenbeck temperature process, with seasonality in the level and volatility. We forecast up to two months ahead out of sample daily temperatures and we simulate the corresponding Cumulative Average Temperature and Heating Degree Day indices. The proposed model is validated in 8 European and 5 USA cities all traded in Chicago Mercantile Exchange. Our results suggest that the proposed method outperforms alternative pricing methods proposed in prior studies in most cases. Our findings suggest that wavelet networks can model the temperature process very well and consequently they constitute a very accurate and efficient tool for weather derivatives pricing. Finally, we provide the pricing equations for temperature futures on Heating Degree Day index.
Keywords
- Weather Derivatives
- Pricing
- Forecasting
- Wavelet Networks
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Challis, S.: Bright Forecast for Profits, Reactions. June edn. (1999)
Hanley, M.: Hedging the Force of Nature. Risk Professional 1, 21–25 (1999)
Ceniceros, R.: Weather derivatives running hot. Business Insurance 40 (2006)
Jewson, S., Brix, A., Ziehmann, C.: Weather Derivative Valuation: The Meteorological, Statistical, Financial and Mathematical Foundations. Cambridge University Press, Cambridge (2005)
Zapranis, A., Alexandridis, A.: Modelling Temperature Time Dependent Speed of Mean Reversion in the Context of Weather Derivetive Pricing. Applied Mathematical Finance 15, 355–386 (2008)
Zapranis, A., Alexandridis, A.: Weather Derivatives Pricing: Modelling the Seasonal Residuals Variance of an Ornstein-Uhlenbeck Temperature Process With Neural Networks. Neurocomputing (accepted, to appear)
Alaton, P., Djehince, B., Stillberg, D.: On Modelling and Pricing Weather Derivatives. Applied Mathematical Finance 9, 1–20 (2000)
Zhang, Q., Benveniste, A.: Wavelet Networks. IEEE Trans. Neural Networks 3, 889–898 (1992)
Benth, F.E., Saltyte-Benth, J.: The volatility of temperature and pricing of weather derivatives. Quantitative Finance 7, 553–561 (2007)
Daubechies, I.: Ten Lectures on Wavelets. SIAM, Philadelphia (1992)
Mallat, S.G.: A Wavelet Tour of Signal Processing. Academic Press, San Diego (1999)
Zapranis, A., Alexandridis, A.: Wavelet analysis and weather derivatives pricing. HFFA, Thessaloniki (2006)
Oussar, Y., Dreyfus, G.: Initialization by Selection for Wavelet Network Training. Neurocomputing 34, 131–143 (2000)
Zapranis, A., Alexandridis, A.: Model Identification in Wavelet Neural Networks Framework. In: Iliadis, L., Vlahavas, I., Bramer, M. (eds.) Artificial Intelligence Applications and Innovations III. IFIP, vol. 296, pp. 267–277. Springer, New York (2009)
Cao, M., Wei, J.: Pricing the weather. In: Risk Weather Risk Special Report, Energy And Power Risk Management, pp. 67–70 (2000)
Davis, M.: Pricing weather derivatives by marginal value. Quantitative Finance 1, 1–4 (2001)
Dornier, F., Queruel, M.: Caution to the wind. Weather risk special report. In: Energy Power Risk Management, pp. 30–32 (2000)
Moreno, M.: Riding the temp. Weather Derivatives. FOW Special Support (2000)
Caballero, R., Jewson, S., Brix, A.: Long Memory in Surface Air Temperature: Detection Modelling and Application to Weather Derivative Valuation. Climate Research 21, 127–140 (2002)
Brody, C.D., Syroka, J., Zervos, M.: Dynamical Pricing of Weather Derivatives. Quantitave Finance 2, 189–198 (2002)
Benth, F.E., Saltyte-Benth, J.: Stochastic Modelling of Temperature Variations With a View Towards Weather Derivatives. Applied Mathematical Finance 12, 53–85 (2005)
Oussar, Y., Rivals, I., Presonnaz, L., Dreyfus, G.: Trainning Wavelet Networks for Nonlinear Dynamic Input Output Modelling. Neurocomputing 20, 173–188 (1998)
Zhang, Q.: Using Wavelet Network in Nonparametric Estimation. IEEE Trans. Neural Networks 8, 227–236 (1997)
Postalcioglu, S., Becerikli, Y.: Wavelet Networks for Nonlinear System Modelling. Neural Computing & Applications 16, 434–441 (2007)
Xu, J., Ho, D.W.C.: A Basis Selection Algorithm for Wavelet Neural Networks. Neurocomputing 48, 681–689 (2002)
Gao, R., Tsoukalas, H.I.: Neural-wavelet Methodology for Load Forecasting. Journal of Intelligent & Robotic Systems 31, 149–157 (2001)
Xu, J., Ho, D.W.C.: A constructive algorithm for wavelet neural networks. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 730–739. Springer, Heidelberg (2005)
Benth, F.E., Saltyte-Benth, J., Koekebakker, S.: Putting a price on temperature. Scandinavian Journal of Statistics 34, 746–767 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zapranis, A., Alexandridis, A. (2009). Modeling and Forecasting CAT and HDD Indices for Weather Derivative Pricing. In: Palmer-Brown, D., Draganova, C., Pimenidis, E., Mouratidis, H. (eds) Engineering Applications of Neural Networks. EANN 2009. Communications in Computer and Information Science, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03969-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-03969-0_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03968-3
Online ISBN: 978-3-642-03969-0
eBook Packages: Computer ScienceComputer Science (R0)