Uncertainty and robustness in weather derivative models

Ahmet Göncü; Yaning Liu; Giray Ökten; M. Yousuff Hussaini

doi:10.1007/978-3-319-33507-0_17

Uncertainty and robustness in weather derivative models

Ahmet Göncü, Yaning Liu, Giray Ökten^*, M. Yousuff Hussaini

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Monte Carlo and Quasi-Monte Carlo Methods - MCQMC 2014
Editors	Ronald Cools, Dirk Nuyens
Publisher	Springer New York LLC
Pages	351-365
Number of pages	15
ISBN (Print)	9783319335056
DOIs	https://doi.org/10.1007/978-3-319-33507-0_17
Publication status	Published - 2016
Externally published	Yes
Event	11th International Conference on Monte Carlo and Quasi Monte Carlo Methods in Scientific Computing, MCQMC 2014 - Leuven, Belgium Duration: 6 Apr 2014 → 11 Apr 2014

Publication series

Name	Springer Proceedings in Mathematics and Statistics
Volume	163
ISSN (Print)	2194-1009
ISSN (Electronic)	2194-1017

Conference

Conference	11th International Conference on Monte Carlo and Quasi Monte Carlo Methods in Scientific Computing, MCQMC 2014
Country/Territory	Belgium
City	Leuven
Period	6/04/14 → 11/04/14

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

Keywords

Model robustness
Sobol’ sensitivity analysis
Weather derivatives

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-319-33507-0_17

Cite this

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title = "Uncertainty and robustness in weather derivative models",

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{\textquoteright} is employed. An empirical investigation of the robustness of models is given using temperature data.",

keywords = "Model robustness, Sobol{\textquoteright} sensitivity analysis, Weather derivatives",

author = "Ahmet G{\"o}nc{\"u} and Yaning Liu and Giray {\"O}kten and Hussaini, {M. Yousuff}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 11th International Conference on Monte Carlo and Quasi Monte Carlo Methods in Scientific Computing, MCQMC 2014 ; Conference date: 06-04-2014 Through 11-04-2014",

year = "2016",

doi = "10.1007/978-3-319-33507-0_17",

language = "English",

isbn = "9783319335056",

series = "Springer Proceedings in Mathematics and Statistics",

publisher = "Springer New York LLC",

pages = "351--365",

editor = "Ronald Cools and Dirk Nuyens",

booktitle = "Monte Carlo and Quasi-Monte Carlo Methods - MCQMC 2014",

}

Göncü, A, Liu, Y, Ökten, G & Hussaini, MY 2016, Uncertainty and robustness in weather derivative models. in R Cools & D Nuyens (eds), Monte Carlo and Quasi-Monte Carlo Methods - MCQMC 2014. Springer Proceedings in Mathematics and Statistics, vol. 163, Springer New York LLC, pp. 351-365, 11th International Conference on Monte Carlo and Quasi Monte Carlo Methods in Scientific Computing, MCQMC 2014, Leuven, Belgium, 6/04/14. https://doi.org/10.1007/978-3-319-33507-0_17

Uncertainty and robustness in weather derivative models. / Göncü, Ahmet; Liu, Yaning; Ökten, Giray et al.
Monte Carlo and Quasi-Monte Carlo Methods - MCQMC 2014. ed. / Ronald Cools; Dirk Nuyens. Springer New York LLC, 2016. p. 351-365 (Springer Proceedings in Mathematics and Statistics; Vol. 163).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Uncertainty and robustness in weather derivative models

AU - Göncü, Ahmet

AU - Liu, Yaning

AU - Ökten, Giray

AU - Hussaini, M. Yousuff

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Model robustness

KW - Sobol’ sensitivity analysis

KW - Weather derivatives

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M3 - Conference contribution

AN - SCOPUS:84977536862

SN - 9783319335056

T3 - Springer Proceedings in Mathematics and Statistics

SP - 351

EP - 365

BT - Monte Carlo and Quasi-Monte Carlo Methods - MCQMC 2014

A2 - Cools, Ronald

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PB - Springer New York LLC

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Y2 - 6 April 2014 through 11 April 2014

ER -

Uncertainty and robustness in weather derivative models

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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