Modeling Temperature and Pricing Weather Derivatives Based on Temperature

Tastan, Birhan; Hayfavi, Azize

doi:10.1155/2017/3913817

Cited by 11 publications

(6 citation statements)

References 27 publications

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“…is the sum of the trend N t and seasonal S t components of temperature. The expanded version of E[M t |F t ] is shown in (8), where χ 1 , χ 2 , χ 3 , and θ are location-dependent parameters, and ω and t are known constants [57].…”

Section: A Physical Market Model 1) Assumptionsmentioning

confidence: 99%

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Hedging Volumetric Risks of Solar Power Producers Using Weather Derivative Smart Contracts on a Blockchain Marketplace

Alao

Cuffe

2022

IEEE Trans. Smart Grid

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The vulnerability of solar power producers to sunshine fluctuations exposes them to the volumetric risk that future electricity generation may deviate from predicted generation. Weather derivatives have recently emerged as a tool for hedging the volumetric risks of these power producers. However, the stateof-the-art instruments have several shortcomings, contributing to their limited application in the industry. Therefore, novel solar radiation-based weather derivative smart contract arrangements on a blockchain marketplace are proposed to address some of the main limitations of traditional instruments. In this regard, the cash flow of solar generators is modelled to assess the weather elements causing its stochasticity. Using this information, novel smart contract arrangements on a blockchain marketplace with solar radiation days as the underlying weather index are developed and analytically valued. Thereafter, a suite of novel smart contract autonomous mechanisms compelling contracting parties to behave rationally and maintain an enduring arrangement is presented. Finally, a trading strategy based on the developed smart contract arrangements is proposed to minimize the power producers' volatility risk. Results emanating from notional simulations indicate that the proposed approach could be more suitable for hedging the volumetric risks of solar power producers than traditional instruments.

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Section: A Physical Market Model 1) Assumptionsmentioning

confidence: 99%

“…Average temperature is modeled as a Geometric Ornstein-Uhlenbeck process that comprises two independent processes: Brownian motion and a compound mean-reverting process as in (9) [57]. In (9), η and σ t represents the location-dependent drift and volatility coefficients, respectively.…”

Section: A Physical Market Model 1) Assumptionsmentioning

confidence: 99%

Hedging Volumetric Risks of Solar Power Producers Using Weather Derivative Smart Contracts on a Blockchain Marketplace

Alao

Cuffe

2022

IEEE Trans. Smart Grid

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“…Temperature‐related weather derivatives are the most frequent weather derivative type that is available at the markets and are used by the participants of it (Stell, 2005). Three indexes are determined and used by temperature derivatives (Birhan & Azize, 2016; CME Group, 2016; Tastan & Hayfavi, 2017); heating degree days (HDD), cooling degree days (CDD), and cumulative average temperature (CAT). HDD and CDD are the indexes for the days which are lower than 18°C, higher than 18°C during the winter and summer period, respectively.…”

Section: Weather Derivativesmentioning

confidence: 99%

Introduction to weather derivatives

Bemš

Aydin

2021

WIREs Energy & Environment

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The weather is one of the factors that may have an impact on the countries' economies. There are two main hedging ways against unexpected weather conditions: weather derivatives and weather insurances. During the last two decades, companies started to use weather derivatives against weather issues, especially in the energy and agriculture sectors. Starting from weather derivatives' first launch, their transaction volumes at the exchange and over-thecounter markets have increased. In addition to the increasing dependency of the economies on the weather, providing the weather derivative contracts with a reasonable premium amount is another reason which helps to have this positive trend. Since weather derivatives have similar parameters and rules with classical financial derivatives, it is possible to use the same pricing approaches for financial and weather derivatives. Monte-Carlo simulation, based on random number generation, is one of the existing methods of pricing derivative contracts. A difference between simulated values and really occurred data is the base point of the expected payoff or price of the contract. The current article introduces weather derivatives and shows two different approaches to their pricing, where one of them requires deeper statistical analysis. Adding the statistical analysis into the consideration, defining the relation between each data value, helps to provide better estimation and less volatility. Having less volatility can provide more accurate estimations and reasonable prices that are affordable and desired by the companies.

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“…The realistic view and application of meteorological IKS have been incorporated in the ecological disaster early warnings strategies of some of the local authorities in Kenya (Liang, 2017), desertification and range ecology framework in Morocco (Davis, 2004), agricultural systems in northern Malawi (Moyo, 2010), policy advocacy for inclusion of IKS in meteorological sciences curricula in South Africa (Riffel, 2015) and linkages of conventional and IKS in weather forecasting in Zaka District of Zimbabwe (Makwara, 2013). The realization that IKS is central in meteorology and climatology has been aptly expressed by Mahony and Caglioti (2017) who retorts that "… new scholarship is showing more directly how the conduct of meteorology and climatology is deeply entangled with society [local community knowledge systems]" (Mahony & Caglioti, 2017).…”

Section: Theoretical Underpinning: Realism Theorymentioning

confidence: 99%

Meteorological Indigenous Knowledge Systems for Prediction of Rainfall in the Chimanimani District of Zimbabwe and Potential for Community Disaster Preparedness

Sithole¹,

Chundu²

2020

JSS

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The paper focuses on indigenous knowledge systems (IKS) which people of Chimanimani District of Zimbabwe use to predict rainfall patterns. This is based on a 2019 socio-ecological study conducted in the district. The main objective of the study was to establish the extent of use and efficacy of meteorological IKS for prediction of rainfall in Chimanimani and to explore potential contribution of the meteorological IKS in local community disaster preparedness. The study came at the backdrop of a trail of unprecedented human and environmental destruction that occurred in the district following the devastating Cyclone Idai of April of 2019. Despite the existence of IKS and modern weather forecasting tools, the Cyclone seemed to have caught both the community and government unprepared, prompting this study. In depth-interviews and storytelling were conducted with traditional leaders and community elders. A qualitative approach was applied through the use of detailed interviews and focus group discussion to collect meteorological IKS from three communities in the district. Thematic analysis anchored in the interpretive and ontological paradigms was utilized within the realism theory. The meteorological IKS found in the study were classified into five interpretive categories namely vegetation, animals, birds, insects and cosmology. The study concluded that meteorological IKS are no longer widely used due to a number of factors including inferioritisation and limited documentation. It was noted that the IKS have potential for incorporation into the community disaster preparedness frameworks.

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Modeling Temperature and Pricing Weather Derivatives Based on Temperature

Cited by 11 publications

References 27 publications

Hedging Volumetric Risks of Solar Power Producers Using Weather Derivative Smart Contracts on a Blockchain Marketplace

Hedging Volumetric Risks of Solar Power Producers Using Weather Derivative Smart Contracts on a Blockchain Marketplace

Introduction to weather derivatives

Meteorological Indigenous Knowledge Systems for Prediction of Rainfall in the Chimanimani District of Zimbabwe and Potential for Community Disaster Preparedness

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