Management of climate risks in agriculture–will weather derivatives permeate?

Mußhoff, Oliver; Odening, Martin; Xu, Wei

doi:10.1080/00036840802600210

Cited by 75 publications

(60 citation statements)

References 15 publications

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“…25 Then, for a given climate scenario c, yields in a given year i are represented by y c,i and z c,i represents the corresponding realisation of a weather index. The influence of weather on yields under given climatic conditions is captured 22 Barnett and Vedenov (2004); Breustedt et al (2008); Musshoff et al (2009);Berg et al (2009); Leblois and Quirion (2011). 23 Torriani et al (2007a).…”

Section: Theoretical Approachmentioning

confidence: 99%

Climate Change, Weather Insurance Design and Hedging Effectiveness

Kapphan

Calanca

Holzkaemper

2012

Geneva Pap Risk Insur Issues Pract

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Insurers have relied on historical data to design weather insurance contracts. In light of climate change, we examine the effects of this practice on the hedging effectiveness and profitability of insurance contracts. Using synthetic crop and weather data for today's and future climatic conditions we derive adjusted weather insurance contracts that account for shifts in the distribution of weather and yields. In our scenario, hedging benefits from adjusted contracts almost triple and expected profits increase by about 240 per cent. We further investigate the effect on risk reduction (for the insured) and profits (for the insurer) from hedging future weather risk with non-adjusted contracts based on historical data. In this case, insurers generate profits that are significantly smaller than for adjusted contracts, or even face substantial losses. Moreover, non-adjusted contracts that create higher profits than the adjusted counterparts cause negative hedging benefits for the insured.

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Section: Theoretical Approachmentioning

confidence: 99%

Climate Change, Weather Insurance Design and Hedging Effectiveness

Kapphan

Calanca

Holzkaemper

2012

Geneva Pap Risk Insur Issues Pract

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“…Thus, the discounted revenue of the producer holding the weather insurance is given by (cf. Mußhoff et al (2011) and Ritter et al (2013)…”

Section: Geographic Basis Riskmentioning

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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“…Tradicionalmente, os agricultores tentam se proteger contra variações de produtividade relacionadas com o clima através da compra de seguros agrícolas (Musshoff et al, 2011). Na cultura da soja e do milho, o produtor tem utilizado o zoneamento agroclimático como uma ferramenta na gestão de riscos e constitui-se numa ferramenta de fundamental importância em várias atividades do setor agrícola.…”

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Avaliação dos riscos envolvidos na produção de milho e soja nos municípios do Mato Grosso

Pereira

Soares

2017

Rev. iPecege

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ResumoA agricultura opera com instabilidade de produção principalmente devido a fatores imprevisíveis que afetam a cultura. Por esse motivo, as seguradoras têm dificuldade de quantificar o risco exato associado aos municípios produtores, principalmente no estado de Mato Grosso. Não existem estudos claros associados à segmentação e quantificação desse risco em uma escala menor. Diante desse fato, o trabalho buscou quantificar e segmentar o risco de produção de milho e soja no estado Mato-Grossense por meio de análises de agrupamento. Para o agrupamento das médias, foi adotada a metodologia não hierárquica de agrupamento denominada K-means, onde se obteve sete clusters. O risco associado à cada cluster foi estimado com base no cálculo do coeficiente de variação da média de produtividade de milho e soja entre os municípios. O trabalho apresentou variação na taxa de risco entre os clusters principalmente considerando a produtividade de milho; os municípios do cluster número 3 (Porto Alegre do Norte, Serra Nova Dourada, Santa Terezinha, Jangada, Barra do Bugres e Porto Estrela) apresentaram as maiores taxas de risco (10,2%) e podem ser desconsiderados da carteira de crédito das seguradoras; e, para a soja, as taxas de riscos entre os clusters foram similares devido à estabilidade produtiva até o momento. Nesse cenário, sugere-se que as seguradoras trabalhem com prêmios menores e introduzam programas de subvenção ao seguro rural naqueles municípios com risco individual elevado. Palavras chaves: análise de agrupamento, produtividade agrícola, seguro agrícola AbstractAgriculture operates with instability of production mainly due to unpredictable factors affecting the culture. Therefore, insurances have been difficult to quantify the exact risk associated with agricultural producing in the cities, mainly in the state of Mato Grosso. There are no clear studies associated with segmentation and quantification of this risk on a smaller scale. Therefore, the objective of this research was to quantify the risk and target production of corn and soybeans in Mato Grosso by methods of cluster analysis. For the means grouping, it was adopted the methodology of non-hierarchical clustering called K-means and seven clusters were obtained. The risk associated within each cluster was based on the coefficient of variation of the corn and soybeans yield among the cities. There was variation in the risk among clusters especially considering the corn yield. The cities Porto Alegre do Norte, Serra Nova Dourada, Santa Terezinha, Jangada, Barra do Bugres e Porto Estrela from the cluster number 3 had the highest risk rates and may be disregarded from the insurance credit portfolio. For soybeans, the risk rates among clusters were similar due to production stability so far. This scenario presented by the research suggests that insurers work with lower premiums values and introduce subsidy programs for rural insurance in those cities with high individual risk.

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Management of climate risks in agriculture–will weather derivatives permeate?

Cited by 75 publications

References 15 publications

Climate Change, Weather Insurance Design and Hedging Effectiveness

Climate Change, Weather Insurance Design and Hedging Effectiveness

Modelling spatio-temporal variability of temperature

Avaliação dos riscos envolvidos na produção de milho e soja nos municípios do Mato Grosso

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