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Uncertainty and Robustness in Weather Derivative Models

Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS,volume 163)

Abstract

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.

Keywords

  • Weather derivatives
  • Sobol’ sensitivity analysis
  • Model robustness

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Notes

  1. 1.

    Daily average temperatures are measured by the Earth Satellite Corporation and our dataset is provided by the Chicago Mercantile Exchange (CME).

  2. 2.

    Our historical data starts from 1/1/1997, which corresponds to \(t=1\). The date we price the option, December 31, 2012 corresponds to \(t=5475\).

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Correspondence to Giray Ökten .

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Göncü, A., Liu, Y., Ökten, G., Hussaini, M.Y. (2016). Uncertainty and Robustness in Weather Derivative Models. In: Cools, R., Nuyens, D. (eds) Monte Carlo and Quasi-Monte Carlo Methods. Springer Proceedings in Mathematics & Statistics, vol 163. Springer, Cham. https://doi.org/10.1007/978-3-319-33507-0_17

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