Can expert knowledge compensate for data scarcity in crop insurance pricing?

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: SFB 649 Discussion Paper ; No. 2013-030

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Agriculture: Other
• Thema: expert knowledge ; data scarcity ; crop insurance pricing ; Bayesian estimation

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Shen, Zhiwei ; Odening, Martin ; Okhrin, Ostap
• Ereignis: Veröffentlichung
• (wer): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (wo): Berlin
• (wann): 2013

Weitere Informationen

• Handle: http://hdl.handle.net/10419/79635
• Letzte Aktualisierung: 10.03.2025, 11:44 MEZ