Uncertainty and robustness in weather derivative models. (English) Zbl 1354.91148
Cools, Ronald (ed.) et al., Monte Carlo and quasi-Monte Carlo methods. MCQMC. Proceedings of the 11th international conference on ‘Monte Carlo and quasi-Monte Carlo methods in scientific computing’, Leuven, Belgium, April 6–11, 2014. Cham: Springer (ISBN 978-3-319-33505-6/hbk; 978-3-319-33507-0/ebook). Springer Proceedings in Mathematics & Statistics 163, 351-365 (2016).
Summary: Pricing of weather derivatives often requires a model for the underlying temperature process that can characterize the dynamic behavior of daily average temperatures. The comparison of different stochastic models with a different number of model parameters is not an easy task, especially in the absence of a liquid weather derivatives market. In this study, we consider four widely used temperature models in pricing temperature-based weather derivatives. The price estimates obtained from these four models are relatively similar. However, there are large variations in their estimates with respect to changes in model parameters. To choose the most robust model, i.e., the model with smaller sensitivity with respect to errors or variation in model parameters, the global sensitivity analysis of Sobol’ is employed. An empirical investigation of the robustness of models is given using temperature data.
For the entire collection see [Zbl 1347.65003].
For the entire collection see [Zbl 1347.65003].
MSC:
| 91G20 | Derivative securities (option pricing, hedging, etc.) |
| 91G70 | Statistical methods; risk measures |
| 86A32 | Geostatistics |
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