Pricing rainfall derivatives in the equatorial Pacific

Cabrales, Sergio; Solano, Jaime; Valencia, Carlos; Bautista, Rafael

doi:10.1108/afr-09-2019-0105

Cited by 2 publications

(2 citation statements)

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“…Rainfall is considered to be a stochastic process consisting of two random variables: one representing frequency, which is a two state Markov Chain, and the other for rainfall amount. These variables can be modeled separately as in Cabrales et al [5]. They can also be combined as a composite variable as in Dzupire et al [18].…”

Section: Stochastic Modelsmentioning

confidence: 99%

“…Lastly, interdependence of climatic factors and linearity assumptions in the model imply that the model cannot accurately predict maize yield because the relationship between climate factors and yield is nonlinear [4]. To take care of error independence violation for incorporation of dependent weather variables in a linear regression, a time series model can be fitted which does not violate first order Markov-Chain assumption [5]. Most time series model might fail to account for change in climate because of stationarity assumption [6], which might lower crop prediction precision [7].…”

Section: Introductionmentioning

confidence: 99%

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Trivariate Stochastic Weather Model for Predicting Maize Yield

Chidzalo¹,

Ngare

Mung’atu³

2022

Journal of Applied Mathematics

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Maize yield prediction in the sub-Saharan region is imperative for mitigation of risks emanating from crop loss due to changes in climate. Temperature, rainfall amount, and reference evapotranspiration are major climatic factors affecting maize yield. They are not only interdependent but also have significantly changed due to climate change, which causes nonlinearity and nonstationarity in weather data. Hence, there exists a need for a stochastic process for predicting maize yield with higher precision. To solve the problem, this paper constructs a joint stochastic process that acquires joints effects of the three weather processes from joint a probability density function (pdf) constructed using copulas that maintain interdependence. Stochastic analyses are applied on the pdf and process to account for nonlinearity and nonstationarity, and also establish a corresponding stochastic differential equation (SDE) for maize yield. The trivariate stochastic process predicts maize yield with R 2 = 0.8389 and M A P E = 4.31 % under a deep learning framework. Its aggregated values predict maize yield with R 2 up to 0.9765 and M A P E = 1.94 % under common machine learning algorithms. Comparatively, the R 2 is 0.8829% and M A P E = 4.18 % , under the maize yield SDE. Thus, the joint stochastic process can be used to predict maize yield with higher precision.

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Section: Stochastic Modelsmentioning

confidence: 99%