Modelling Environment Changes for Pricing Weather Derivatives

Abstract This paper focuses on modelling environment changes in a way that allows to price weather derivatives in a flexible and efficient way. Applications and importance of climate and weather contracts extends beyond financial markets and hedging as they can be used as complementary tools for risk assessment. In addition, option-based approach toward resource management can offer very special insights on rare-events and allow to reuse derivative pricing methods to improve natural resources management. To demonstrate this general concept, we use Monte Carlo and stochastic modelling of temperatures to evaluate weather options. Research results are accompanied by R and Python code.

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Computational probability modeling and Bayesian inference

Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées ◽

10.46298/arima.1917 ◽

2008 ◽

Vol Volume 9, 2007 Conference in... ◽

Author(s):

Fabien Campillo ◽

Rivo Rakotozafy ◽

Vivien Rossi

Keyword(s):

Monte Carlo ◽

Bayesian Inference ◽

Sequential Monte Carlo ◽

Natural Resources Management ◽

Great Success ◽

Resources Management ◽

The Past ◽

Probability Modeling ◽

Ressources Naturelles ◽

International Audience

International audience Computational probabilistic modeling and Bayesian inference has met a great success over the past fifteen years through the development of Monte Carlo methods and the ever increasing performance of computers. Through methods such as Monte Carlo Markov chain and sequential Monte Carlo Bayesian inference effectively combines with Markovian modelling. This approach has been very successful in ecology and agronomy. We analyze the development of this approach applied to a few examples of natural resources management. La modélisation probabiliste et l'inférence bayésienne computationnelles rencontrent un très grand succès depuis une quinzaine d'années grâce au développement des méthodes de Monte Carlo et aux performances toujours croissantes des moyens de calcul. Au travers d'outils comme les méthodes de Monte Carlo par chaîne de Markov et les méthodes de Monte Carlo séquentielles, l'inférence bayésienne se combine efficacement à la modélisation markovienne. Cette approche est également très répandue dans le domaine de l'écologie et l'agronomie. Nous faisons le point sur les développements de cette approche appliquée à quelques exemples de gestion de ressources naturelles.

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Regional Policy in the Field of Natural Resources Management in the Far North

Voprosy Ekonomiki ◽

10.32609/0042-8736-2003-10-108-116 ◽

2003 ◽

pp. 108-116

Author(s):

A. Bykov

Keyword(s):

Economic Development ◽

Natural Resources ◽

Regional Policy ◽

Natural Resources Management ◽

Economic Factors ◽

Resource Saving ◽

Resources Management ◽

Legal Norms ◽

Far North ◽

The Russian Federation

According to the legal norms of the Russian Federation in the ownership, usage and disposal of natural resources the author analyses interaction between natural resources users and local authorities. The interaction is based upon ecological and economic factors, which cause the peculiarities of requirements put before natural resource users in the Far North. The strategic directions of resource saving economic development of these regions are considered.

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