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 proceedingConference contributionpeer-review

2 Citations (Scopus)


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 languageEnglish
Title of host publicationMonte Carlo and Quasi-Monte Carlo Methods - MCQMC 2014
EditorsRonald Cools, Dirk Nuyens
PublisherSpringer New York LLC
Number of pages15
ISBN (Print)9783319335056
Publication statusPublished - 2016
Externally publishedYes
Event11th International Conference on Monte Carlo and Quasi Monte Carlo Methods in Scientific Computing, MCQMC 2014 - Leuven, Belgium
Duration: 6 Apr 201411 Apr 2014

Publication series

NameSpringer Proceedings in Mathematics and Statistics
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017


Conference11th International Conference on Monte Carlo and Quasi Monte Carlo Methods in Scientific Computing, MCQMC 2014

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.


  • Model robustness
  • Sobol’ sensitivity analysis
  • Weather derivatives


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