Derivative Pricing and Risk Management in Weather, Climate and Geological Risks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Financial Mathematics".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 4687

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Guest Editor
School of Mathematics and Applied Statistics, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: mathematical biology; financial mathematics; forensic medicine; fractional calculus; ordinary and partial differential equations

Special Issue Information

Dear Colleagues,

The Special Issue “Derivative Pricing and Risk Management in Weather, Climate and Geological Risks” is open for submissions and welcomes articles that develop financial solutions to mitigate, hedge and transfer climatic and disaster risks. The broad aim of this Special Issue is to look at the development of mathematical models and approaches for the fair valuation and hedging of financial derivatives, with a focus on two urgent areas of environmental concern: climate change adaptation and disaster risk management. Another important theme of this Special Issue is to address catastrophic and geological risks that magnify property insurance and reinsurance losses. Other topics, not necessarily on derivative pricing, that aim to develop climate change scenarios that can be used to model credit and insurance risk for financial institutions and to develop mitigation strategies are also welcome.

Dr. Mariano Rodrigo
Guest Editor

Manuscript Submission Information

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Published Papers (2 papers)

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Research

23 pages, 979 KiB  
Article
A Case Study of the Impact of Climate Change on Agricultural Loan Credit Risk
by Jagdeep Kaur Brar, Antoine Kornprobst, Willard John Braun, Matthew Davison and Warren Hare
Mathematics 2021, 9(23), 3058; https://doi.org/10.3390/math9233058 - 28 Nov 2021
Cited by 3 | Viewed by 2362
Abstract
Changing weather patterns may impose increased risk to the creditworthiness of financial institutions in the agriculture sector. To reduce the credit risk caused by climate change, financial institutions need to update their agricultural lending portfolios to consider climate change scenarios. In this paper [...] Read more.
Changing weather patterns may impose increased risk to the creditworthiness of financial institutions in the agriculture sector. To reduce the credit risk caused by climate change, financial institutions need to update their agricultural lending portfolios to consider climate change scenarios. In this paper we introduce a framework to compute the optimal agricultural lending portfolio under different increased temperature scenarios. In this way we quantify the impact of increased temperature, taken as a measure of climate change, on credit risk. We provide a detailed case study of how our approach applies to the problem of optimizing a portfolio of agricultural loans made to corn farmers across different corn producing regions of Ontario, Canada, under various climate change scenarios. We conclude that the lending portfolio obtained by taking into account the climate change is less risky than the lending portfolio neglecting climate change. Full article
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15 pages, 610 KiB  
Article
A Stochastic Harmonic Oscillator Temperature Model for the Valuation of Weather Derivatives
by Alessio Giorgini, Rogemar S. Mamon and Marianito R. Rodrigo
Mathematics 2021, 9(22), 2890; https://doi.org/10.3390/math9222890 - 13 Nov 2021
Cited by 3 | Viewed by 1600
Abstract
Stochastic processes are employed in this paper to capture the evolution of daily mean temperatures, with the goal of pricing temperature-based weather options. A stochastic harmonic oscillator model is proposed for the temperature dynamics and results of numerical simulations and parameter estimation are [...] Read more.
Stochastic processes are employed in this paper to capture the evolution of daily mean temperatures, with the goal of pricing temperature-based weather options. A stochastic harmonic oscillator model is proposed for the temperature dynamics and results of numerical simulations and parameter estimation are presented. The temperature model is used to price a one-month call option and a sensitivity analysis is undertaken to examine how call option prices are affected when the model parameters are varied. Full article
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