Estimation of the Agricultural Probability of Loss: evidence for soybean in Paraná State

Vitor Augusto Ozaki, Ricardo Olinda, Priscila Neves Faria and Rogério Costa Campos

Revista de Economia e Sociologia Rural (RESR), 2014, vol. 52, issue 01

Abstract: : In any agricultural insurance program, the accurate quantification of the probability of the loss has great importance. In order to estimate this quantity, it is necessary to assume some parametric probability distribution. The objective of this work is to estimate the probability of loss using the theory of the extreme values modeling the left tail of the distribution. After that, the estimated values will be compared to the values estimated under the normality assumption. Finally, we discuss the implications of assuming a symmetrical distribution instead of a more flexible family of distributions when estimating the probability of loss and pricing the insurance contracts. Results show that, for the selected regions, the probability distributions present a relative degree of skewness. As a consequence, the probability of loss is quite different from those estimated supposing the Normal distribution, commonly used by Brazilian insurers.

Keywords: Crop Production/Industries; Risk and Uncertainty (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/341425/files/Vitor%20Augusto%20Ozaki.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:revi24:341425

DOI: 10.22004/ag.econ.341425

Access Statistics for this article

More articles in Revista de Economia e Sociologia Rural (RESR) from Sociedade Brasileira de Economia e Sociologia Rural Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().