Zapranis, A. and Alexandridis, A. (2011) Modeling and Forecasting CAT and HDD Indices For Weather Derivative Pricing. Neural Computing & Applications, 20 (6). pp. 787-801. ISSN 0941-0643.
|The full text of this publication is not available from this repository. (Contact us about this Publication)|
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 and time-varying speed of mean reversion. We forecast up to 2 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 the Chicago Mercantile Exchange. Our results suggest that the proposed method outperforms alternative pricing methods, proposed in prior studies, in most cases. We find that wavelet networks can model the temperature process very well and consequently they constitute an accurate and efficient tool for weather derivatives pricing. Finally, we provide the pricing equations for temperature futures on Cooling and Heating Degree Day indices.
|Uncontrolled keywords:||Finance, Weather Derivatives, Temperature Derivatives, Wavelet Networks|
|Subjects:||H Social Sciences > HG Finance|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science|
|Depositing User:||Antonis Alexandridis|
|Date Deposited:||04 Apr 2012 11:55|
|Last Modified:||10 Apr 2012 13:18|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/29257 (The current URI for this page, for reference purposes)|
- Depositors only (login required):