A critical view on temperature modelling for application in weather derivatives markets

Benth, Jūratė Šaltytė; Benth, Fred Espen

doi:10.1016/j.eneco.2011.09.012

Cited by 25 publications

(3 citation statements)

References 20 publications

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“…Among these variables, s us,t is simulated in the same way as in the light service. s te,t is simulated with trigonometric functions [17] and expressed as:…”

Section: A Simulated Environmentmentioning

confidence: 99%

Reinforcement Learning Based Architectures for Dynamic Generation of Smart Home Services

Qiu

Najm

Sharrock

et al. 2022

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

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A smart home system is realized by implementing various services. However, the design and deployment of smart home services are challenging due to their complexity and the large number of connected objects. Existing approaches to the smart home system to create services either require complex input from the inhabitant or can only work if the inhabitant specifies regulation solutions rather than targets. In addition, smart home services may conflict if they access the same actuators. Learning methods to dynamically generate smart home services are promising ways to solve the above problems. In this paper, depending on the ability to consider the composition of services and their mutual influence, we propose several reinforcement learning-based architectures for a smart home system to dynamically generate services. The expected advantages are, first, that the smart home services can propose the states of the actuators by considering the target values of the controllable environment states given by the inhabitant or by interacting with the inhabitant in a simple and natural way; and second, that there is no conflict between these propositions. We compare the performance of the proposed architectures using several simulated smart home environments with different services and select the architectures with the best performance concerning our predefined metrics.

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“…Among these variables, s us,t is simulated in the same way as in the light service. s te,t is simulated with trigonometric functions [17] and expressed as:…”

Section: A Simulated Environmentmentioning

confidence: 99%

Reinforcement Learning Based Architectures for Dynamic Generation of Smart Home Services

Qiu

Najm

Sharrock

et al. 2022

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

View full text Add to dashboard Cite

show abstract

“…where k is the number of components, β y k is the magnitude of the component and ϕ y k is the phase difference between the data and the component. According to [37] the set {exp(2πκi)} κ∈Z is an orthonormal basis and any periodic function can be approximated by a sum of trigonometric functions arbitrarily good. Here as the computational part is applied to the Turkish market, hourly price data is used.…”

Section: Electricity Price Modelsmentioning

confidence: 99%

Comparison of Electricity Spot Price Modelling and Risk Management Applications

Çanakoğlu¹,

Adıyeke²

2020

Energies

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In dealing with sharp changes in electricity prices, contract planning is considered as a vital risk management tool for stakeholders in deregulated power markets. In this paper, dynamics of spot prices in Turkish electricity market are analyzed, and predictive performance of several models are compared, i.e., time series models and regime-switching models. Different models for derivative pricing are proposed, and alternative portfolio optimization problems using mean-variance optimization and conditional value at risk (CVaR) are solved. Expected payoff and risk structure for different hedging strategies for a hypothetical electricity company with a given demand are analyzed. Experimental studies show that regime-switching models are able to capture electricity characteristics better than their standard counterparts. In addition, evaluations with various risk management models demonstrate that those models are highly competent in providing an effective risk control practice for electricity markets.

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“…The orders of the autoregressive process vary between 3 and 20 for all stations with no distinct geographical pattern. Although Šaltytė-Benth and Benth (2012) mention that choosing large lags is not meaningful from a statistical and a meteorological point of view, we keep using the original lags individually for each station to account for data differences between stations. Figure A2 illustrates the observed temperature at Quzhou and the fitted conditional mean function (Eq.…”

Section: Daily Temperature Modelmentioning

confidence: 99%

Modelling spatio-temporal variability of temperature

et al. 2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractForecasting temperature in time and space is an important precondition for both the design of weather derivatives and the assessment of the hedging effectiveness of index based weather insurance. In this article, we show how this task can be accomplished by means of Kriging techniques. Moreover, we compare Kriging with a dynamic semiparametric factor model (DSFM) that has been recently developed for the analysis of high dimensional financial data. We apply both methods to comprehensive temperature data covering a large area of China and assess their performance in terms of predicting a temperature index at an unobserved location. The results show that the DSFM performs worse than standard Kriging techniques. Moreover, we show how geographic basis risk inherent to weather derivatives can be mitigated by regional diversification.

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A critical view on temperature modelling for application in weather derivatives markets

Cited by 25 publications

References 20 publications

Reinforcement Learning Based Architectures for Dynamic Generation of Smart Home Services

Reinforcement Learning Based Architectures for Dynamic Generation of Smart Home Services

Comparison of Electricity Spot Price Modelling and Risk Management Applications

Modelling spatio-temporal variability of temperature

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