Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/79635
Authors: 
Shen, Zhiwei
Odening, Martin
Okhrin, Ostap
Year of Publication: 
2013
Series/Report no.: 
SFB 649 Discussion Paper 2013-030
Abstract: 
Although there is an increasing interest in index-based insurances in many developing countries, crop data scarcity hinders its implementation by forcing insurers to charge higher premiums. Expert knowledge has been considered a valuable information source to augment limited data in insurance pricing. This article investigates whether the use of expert knowledge can mitigate model risk which arises from insufficient statistical data. We adopt the Bayesian framework that allows for the combination of scarce data and expert knowledge, to estimate the risk parameter and buffer load. In addition, a benchmark for the evaluation of expert information is created by using a richer dataset generated from resampling. We find that expert knowledge reduces the parameter uncertainty and changes the insurance premium in the correct direction, but that the effect of the correction is sensitive to different strike levels of insurance indemnity.
Subjects: 
expert knowledge
data scarcity
crop insurance pricing
Bayesian estimation
JEL: 
C14
Q19
Document Type: 
Working Paper

Files in This Item:
File
Size
720.31 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.