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Uncertainty and Robustness in Weather Derivative Models

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Book cover Monte Carlo and Quasi-Monte Carlo Methods

Part of the book series: Springer Proceedings in Mathematics & Statistics ((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.

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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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Author information

Authors and Affiliations

  1. Xian Jiaotong Liverpool University, Suzhou, 215123, China

    Ahmet Göncü

  2. Hydrogeology Department, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

    Yaning Liu

  3. Florida State University, Tallahassee, FL, 32306, USA

    Giray Ökten & M. Yousuff Hussaini

Authors
  1. Ahmet Göncü
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  2. Yaning Liu
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  3. Giray Ökten
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  4. M. Yousuff Hussaini
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Corresponding author

Correspondence to Giray Ökten .

Editor information

Editors and Affiliations

  1. Department of Computer Science, KU Leuven, Heverlee, Belgium

    Ronald Cools

  2. Department of Computer Science, KU Leuven, Heverlee, Belgium

    Dirk Nuyens

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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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