Diversifying systemic risk in agriculture. Issue 4 (7th November 2016)

• Record Type: Journal Article
• Authors: Feng, Xiaoguang; Hayes, Dermot
• Is Part Of: Agricultural finance review. Volume 76:Issue 4(2016)
• Page Start: 512
• Page End: 531
• Publication Date: 2016-11-07
• Subjects: Agriculture -- Crop insurance -- Systemic risk -- Diversification effect -- Hierarchical Kendall copulas; Agriculture -- Finance -- Periodicals; Agriculture -- Finance -- Statistics -- Periodicals; Agricultural insurance -- Periodicals; Agriculture -- Taxation -- Periodicals; 332.71
• DOI: 10.1108/AFR-06-2016-0061
• Languages: English
• ISSNs: 0002-1466

Abstract

Abstract : Purpose: Portfolio risk in crop insurance due to the systemic nature of crop yield losses has inhibited the development of private crop insurance markets. Government subsidy or reinsurance has therefore been used to support crop insurance programs. The purpose of this paper is to investigate the possibility of converting systemic crop yield risk into "poolable" risk. Specifically, this study examines whether it is possible to remove the co-movement as well as tail dependence of crop yield variables by enlarging the risk pool across different crops and countries. Design/methodology/approach: Hierarchical Kendall copula (HKC) models are used to model potential non-linear correlations of the high-dimensional crop yield variables. A Bayesian estimation approach is applied to account for estimation risk in the copula parameters. A synthetic insurance portfolio is used to evaluate the systemic risk and diversification effect. Findings: The results indicate that the systemic nature – both positive correlation and lower tail dependence – of crop yield risks can be eliminated by combining crop insurance policies across crops and countries. Originality/value: The study applies the HKC in the context of agricultural risks. Compared to other advanced copulas, the HKC achieves both flexibility and parsimony. The flexibility of the HKC makes it appropriate to precisely represent various correlation structures of crop yield risks while the parsimony makes it computationally efficient in modeling high-dimensional correlation structure.