Thesis (Ph.D.) - University of Queensland, 2005. / Includes bibliography.

The valuation of weather derivatives is complex since the underlying temperature process has no negotiable price. This thesis introduces a selection of models for the valuation of weather derivative contracts, governed by a stochastic underlying temperature process. We then present a new weather pricing model, which is used to determine the fair hedging price of a weather derivative under the assumptions of mean self-financing. This model is then extended to incorporate a compensation (or market price of risk) awarded to investors who hold undiversifiable risks. This results in the derivation of a non-linear two-dimensional PDE, for which the numerical evaluation cannot be performed using standard finite-difference techniques. The numerical techniques applied in this thesis are based on a broad range of lattice based schemes, including enhancements to finite-differences, quadrature methods and binomial trees. Furthermore simulations of temperature processes are undertaken that involves the development of Monte Carlo based methods.

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Thesis (MBA)--Stellenbosch University, 2006. / ENGLISH ABSTRACT: Weather derivatives, a new breed of financial assets, allow firms to manage weather risk that disturbs their activities and may lead to variability in earnings and operating cost. Considering that nearly 20% of the U.S. economy alone is directly affected by the weather, weather derivatives are an important development in the area of risk management. This study project explores the concept, functioning and pricing of weather derivatives by reviewing available literature on the topic. It then investigates international weather derivative markets to establish which markets are thriving and what lessons can be learnt from them. This then forms the basis for a set of requirements for developing a weather derivative market in South Africa. Finally, the study project makes a number of recommendations for developing a weather derivative market in South Africa. The findings suggest that, in the absence of a deregulated energy industry, South African suppliers of weather derivatives need to target small·medium size organisations, specifically within the agricultural industry, in order to grow market liquidity. Furthermore, these suppliers need to attract capital market investors either by marketing weather derivatives as a diversification tool to portfolio managers, or by issuing weather·linked bonds as a more familiar investment product for investors. It also suggests that weather data problems can be resolved through, among others, data cleaning and data enhancement techniques and should therefore not impede the growth of a weather derivative market in South Africa. / AFRIKAANSE OPSOMMING: Weer opsies, 'n bundel nuwegenerasie finansiele instruments, stel maatskappye in staat om die invloed van weer op hulle besigheidsaktiwiteite, soos byvoorbeeld die variasie in inkomste en operasionele koste, beter te bestuur. Weer opsies verteenwoordig 'n belangrike ontwikkeling in die area van risikobestuur, inaggenome dat bykans 20% van die V.S.A. ekonomie deur die weer geaffekteer word. Hierdie werkstuk ondersoek die konsep, funksionering, en prysbepaling van weer opsies deur die oorsig en evaluering van die beskikbare literatuur oor die onderwerp. Verder word die internasionale mark vir weer opsies ondersoek om vas te stel waar die grootste suksesse behaal word en watter lesse daaruit geleer kan word. Laastens word daar 'n aantal aanbevelings gemaak vir die ontwikkeling van die weer opsie mark in Suid-Afrika. Die werkstuk bevind dat die verskaffers van weer opsies in Suid Afrika, in die afwesigheid van 'n gedereguleerde energie sektor, klein to medium sakeondernemings (veral in die landbou sektor) moet oormerk en teiken, ten einde mark likiditeit te verhoog. Verskaffers kan kapitaalmark beleggers betrek deur die produk te bemark as 'n diversifisering instrument vir portefeuljebestuurders, of deur die aanbieding van weer geassosieerde verbande as 'n meer alledaagse beleggingsproduk. Daar word verder bevind dat data kwaliteit probleme aangespreek kan word deur gebruik te maak van data-skoonmaak en - verbeterings tegnieke, en dat dataprobleme dus nie 'n effek behoort te hê op die groei van die weer opsie mark in Suid-Afrika nie.

The diploma thesis deals with the possibilities of applying weather derivatives and catastrophe bonds as alternative risk transfer products, which enable to cope with natural disaster risk in the Czech Republic. The author highlights an obvious increase in occurrence and intensity of extreme climate events resulting into devastating floods. Total costs caused by floods in August 2002, which hadn't been known so far, had important impact on the Czech insurance market. The situation is in many aspects similar to circumstances, which led to the birth of ART products abroad. If the recent tendencies continue, Czech insurers will have to find new ways of dealing with these risks beside the traditional commercial insurance. In accordance with conclusions of this thesis, applying of catastrophe bonds isn't supposed in the future. However, the weather derivatives seem to be an alternative with great potential.

Muitos negócios possuem exposição às variações climáticas e com poucas alternativas para mitigar este tipo de risco. Nos últimos 20 anos o mercado de derivativos climáticos se desenvolveu principalmente em locais como Canadá, EUA e Europa para transferir os riscos relacionados às variações climáticas para investidores com maior capacidade de absorção, tais como seguradoras, resseguradoras e fundos de investimentos. Este trabalho implementou uma metodologia de precificação destes contratos para a variável temperatura média diária no Brasil. Foram utilizados os dados de 265 estações meteorológicas cadastras no site do BDMEP/INMET, utilizando-se observações diárias durante o período 1970-2012. Enquanto a maior parte dos trabalhos de precificação fora desenvolvida para um local específico, neste estudo buscou-se uma solução mais generalizada e que permitisse aos participantes deste novo mercado balizar suas expectativas de preço para qualquer ponto com uma estação meteorológica no país. O principal desafio para esta abordagem foram as falhas nas séries temporais e para isto desenvolveu-se uma metodologia de preenchimento utilizando as informações do projeto NCEP/NCAR. Cada estação foi submetida ao algoritmo de análise e modelagem das séries de temperatura. Considerou-se \"Sucesso\" (36.2% dos casos) as estações cujo processo de modelagem culminou em um resíduo ruído branco, estacionário e homoscedástico. Por \"Fracasso\" (63.8% das estações) entendem-se os casos que violaram pelo menos uma destas condições. Para a incorporação da tendência nos dados utilizou-se a Regressão Polinomial Local (LOESS). Para a estimação da sazonalidade foi empregada análise espectral e utilizada a série de Fourier. Para o tratamento da autocorrelação serial nos resíduos utilizou-se modelos ARFIMA, que contempla um parâmetro para memória longa do processo. A análise espacial dos resultados sugere uma maior taxa de \"Sucesso\" para a precificação de contratos na região Centro-Sul do país e piores para Norte e Nordeste. O método de preenchimento das falhas não deve ser utilizado indiscriminadamente por todo o país, uma vez que a correlação entre as séries do BDMEP/INMET e NCEP/NCAR não é constante, além de apresentar um claro padrão na dispersão espacial. A precificação dos contratos foi feita pelos métodos de \"Burning cost\", \"Modelagem do Índice\" e \"Modelagem da temperatura média diária\". Para este último caso as temperaturas simuladas apresentaram um viés ligeiramente acima dos dados históricos, podendo causar grandes distorções na precificação dos contratos. Deve-se realizar uma correção dos valores simulados antes da precificação dos contratos. A qualidade e consistência dos dados climáticos representam a maior ameaça para a utilização de derivativos climáticos no país, principalmente na região Cento-Oeste, aonde existem poucas estações meteorológicas, e Nordeste, com baixíssima taxa de \"Sucesso\", mesmo com um razoável número de estações. / Many business are exposed to weather variations and managers did not use to have a tool to avoid it. In the last twenty years, weather derivative markets has developed mainly in Canada, USA and Europe, transferring these risks to investors who are willing and able to assume it and receive a financial compensation for that, such as investment funds, insurance and reinsurance companies. This study developed a methodology to price weather contracts with daily average temperature as underlying. It was used 265 public weather stations from BDMEP/ INMET and data was collected from 1970 up to 2012. While the most part of studies in this area have focused in one or few stations, the goal of this study was to develop a more general pricing tool which would allow assessing weather risk and quoting it at any place in Brazil with an available weather station. The main issue was the gaps that occur so frequently in weather time series data and a methodology using interpolated data from NCEP/NCAR was proposed to deal with it. At the bottom of modelling process, weather stations were classified as \"Success\" (36.2%) or \"Failure\" (63.8%) according to the analysis of residuals. To be considered \"Success\", residuals of a time series must be stationary, homoscedastic and white-noise, i.e., free of autocorrelation. If at least one of these was not reached, the modelling process of this weather station was considered \"Failure\". Detrend data was done using Local Polynomial Regression (LOESS). Seasonality was estimated using spectral analysis and Fourier analysis. Autocorrelation of residuals was incorporated into the model using ARFIMA models, which have a parameter to deal with long memory process. Spatial analysis of results suggests a higher \"Success\" rate for contracts priced in the Center south region and worst results were obtained in North and Northeast. Methodology to fill the gaps should not be used in all situations, once correlation is not constant through the country and has a strong spatial pattern (clustering). Pricing was done using \"Burning cost\", \"Index modelling\" and \"Daily modelling of average temperature\". In this former case, simulated temperature has shown a slightly positive bias, which could create huge differences in prices compared with other models. A correction should be done to these values, to use it for pricing purposes. The quality and consistency of weather data is the main issue to develop a weather market in Brazil, mainly in Center-West region, where there is a small number of weather stations and Northeast with the lowest \"Success\" rate, even with a not so small number of weather stations.

Key words. Newsvendor Model, Inventory Model, Seasonal Product, Weather Risk, Weather Option, Weather Derivative, Weather-Conditional Rebate, CVaR, Mean-CVaR. / The first model considers the problem of hedging inventory risk for a newsvendor who sells a seasonal product. The newsvendor not only decides the order quantity, but also adopts a weather hedging strategy. A typical hedging strategy is to use an option that is constructed on a weather index before the season begins, which will compensate the buyer of the option if the actual seasonal weather index is above (or below) a given strike level. We explore the joint decision problem in mean-variance, expected utility, conditional value-at-risk (CVaR), and mean-CVaR frameworks. We analyze the impact of weather hedging on optimal order quantity. It is proven that the newsvendor may order more than in the absence of weather options. Numerical analysis on the sensitivity of the optimal order quantity, the risk premium of the option, the portfolio selection and the comparison between the weather option hedging and a particular operational hedging are presented as well. / The second model investigates the advantages of early sales of a seasonal product. To induce early sales, the newsvendor adopts a weather-conditional rebate program, which will pay rebates to the customers who buy the product in the preselling period if a specified weather condition for normal selling season is realized. For an example, a certain amount of refund will be paid to early buyer if the seasonal average temperature falls below the past-three-year seasonal average. Two conditional rebate programs with early booking and early purchasing are investigated and compared. Both of them can price differentiate within a customer among his/her post valuation on the seasonal product, and thus increase the sales. For the early purchasing program, it can further save inventory holding cost and ordering cost. The expected profit can be improved by the programs. Moreover, combined with weather derivatives, the conditional rebate program can manage the financial risk with the expected profit being still improved. / To investigate the means that firms may adopt in managing the adverse impacts of weather on their businesses, this dissertation proposes and analyzes two inventory models for seasonal products when the demand is sensitive to the weather in the season. Both models are formulated under the newsvendor context. / Gao, Fei. / "October 2007." / Adviser: Youhua Frank Chen. / Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 5002. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 108-119). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

In Champagne, France grape growers and and winemakers work together to make the world's most iconic sparkling wine. Part of what makes Champagne so celebrated is its reputation for constant quality: only the best grapes are used to make wine. In poor vintage years, grape growers sell less grapes to winemakers; poor vintages are the result of bad weather. This presents the opportunity for grape growers to hedge the risk of poor weather and resulting lower harvest values with weather derivatives. This study explores the potential for grape growers to trade them to effectively hedge against low harvest values by hedging against cooler weather in the month of June, when grape vines are flowering and sensitive to cold, through an empirical study of historical grape harvest and temperature data from 1952 through 2011. Weather derivatives would have provided an effective hedge against low harvest values up through 1991. After 1991, harvest sizes and therefore harvest revenues are no longer significantly correlated with weather and weather derivatives no longer provide an effective hedge against low harvest values for grape growers in Champagne.

Muitos negócios possuem exposição às variações climáticas e com poucas alternativas para mitigar este tipo de risco. Nos últimos 20 anos o mercado de derivativos climáticos se desenvolveu principalmente em locais como Canadá, EUA e Europa para transferir os riscos relacionados às variações climáticas para investidores com maior capacidade de absorção, tais como seguradoras, resseguradoras e fundos de investimentos. Este trabalho implementou uma metodologia de precificação destes contratos para a variável temperatura média diária no Brasil. Foram utilizados os dados de 265 estações meteorológicas cadastras no site do BDMEP/INMET, utilizando-se observações diárias durante o período 1970-2012. Enquanto a maior parte dos trabalhos de precificação fora desenvolvida para um local específico, neste estudo buscou-se uma solução mais generalizada e que permitisse aos participantes deste novo mercado balizar suas expectativas de preço para qualquer ponto com uma estação meteorológica no país. O principal desafio para esta abordagem foram as falhas nas séries temporais e para isto desenvolveu-se uma metodologia de preenchimento utilizando as informações do projeto NCEP/NCAR. Cada estação foi submetida ao algoritmo de análise e modelagem das séries de temperatura. Considerou-se \"Sucesso\" (36.2% dos casos) as estações cujo processo de modelagem culminou em um resíduo ruído branco, estacionário e homoscedástico. Por \"Fracasso\" (63.8% das estações) entendem-se os casos que violaram pelo menos uma destas condições. Para a incorporação da tendência nos dados utilizou-se a Regressão Polinomial Local (LOESS). Para a estimação da sazonalidade foi empregada análise espectral e utilizada a série de Fourier. Para o tratamento da autocorrelação serial nos resíduos utilizou-se modelos ARFIMA, que contempla um parâmetro para memória longa do processo. A análise espacial dos resultados sugere uma maior taxa de \"Sucesso\" para a precificação de contratos na região Centro-Sul do país e piores para Norte e Nordeste. O método de preenchimento das falhas não deve ser utilizado indiscriminadamente por todo o país, uma vez que a correlação entre as séries do BDMEP/INMET e NCEP/NCAR não é constante, além de apresentar um claro padrão na dispersão espacial. A precificação dos contratos foi feita pelos métodos de \"Burning cost\", \"Modelagem do Índice\" e \"Modelagem da temperatura média diária\". Para este último caso as temperaturas simuladas apresentaram um viés ligeiramente acima dos dados históricos, podendo causar grandes distorções na precificação dos contratos. Deve-se realizar uma correção dos valores simulados antes da precificação dos contratos. A qualidade e consistência dos dados climáticos representam a maior ameaça para a utilização de derivativos climáticos no país, principalmente na região Cento-Oeste, aonde existem poucas estações meteorológicas, e Nordeste, com baixíssima taxa de \"Sucesso\", mesmo com um razoável número de estações. / Many business are exposed to weather variations and managers did not use to have a tool to avoid it. In the last twenty years, weather derivative markets has developed mainly in Canada, USA and Europe, transferring these risks to investors who are willing and able to assume it and receive a financial compensation for that, such as investment funds, insurance and reinsurance companies. This study developed a methodology to price weather contracts with daily average temperature as underlying. It was used 265 public weather stations from BDMEP/ INMET and data was collected from 1970 up to 2012. While the most part of studies in this area have focused in one or few stations, the goal of this study was to develop a more general pricing tool which would allow assessing weather risk and quoting it at any place in Brazil with an available weather station. The main issue was the gaps that occur so frequently in weather time series data and a methodology using interpolated data from NCEP/NCAR was proposed to deal with it. At the bottom of modelling process, weather stations were classified as \"Success\" (36.2%) or \"Failure\" (63.8%) according to the analysis of residuals. To be considered \"Success\", residuals of a time series must be stationary, homoscedastic and white-noise, i.e., free of autocorrelation. If at least one of these was not reached, the modelling process of this weather station was considered \"Failure\". Detrend data was done using Local Polynomial Regression (LOESS). Seasonality was estimated using spectral analysis and Fourier analysis. Autocorrelation of residuals was incorporated into the model using ARFIMA models, which have a parameter to deal with long memory process. Spatial analysis of results suggests a higher \"Success\" rate for contracts priced in the Center south region and worst results were obtained in North and Northeast. Methodology to fill the gaps should not be used in all situations, once correlation is not constant through the country and has a strong spatial pattern (clustering). Pricing was done using \"Burning cost\", \"Index modelling\" and \"Daily modelling of average temperature\". In this former case, simulated temperature has shown a slightly positive bias, which could create huge differences in prices compared with other models. A correction should be done to these values, to use it for pricing purposes. The quality and consistency of weather data is the main issue to develop a weather market in Brazil, mainly in Center-West region, where there is a small number of weather stations and Northeast with the lowest \"Success\" rate, even with a not so small number of weather stations.

Weather derivatives are financial tools used to manage the risks related to changes in the weather and are priced considering weather variables such as rainfall, temperature, humidity and wind as the underlying asset. Some recent researches suggest to model the amount of rainfall by considering the mean reverting processes. As an example, the Ornstein Uhlenbeck process was proposed by Allen [3] to model yearly rainfall and by Unami et al. [52] to model the irregularity of rainfall intensity as well as duration of dry spells. By using the Feynman-Kac theorem and the rainfall indexes we derive the partial differential equations (PDEs) that governs the price of an European option. We apply the Lie analysis theory to solve the PDEs, we provide the group classification and use it to find the invariant analytical solutions, particularly the ones compatible with the terminal conditions.