Net income risk, crop insurance and hedging. Issue 1 (5th February 2018)

• Record Type: Journal Article
• Authors: Walters, Cory; Preston, Richard
• Is Part Of: Agricultural finance review. Volume 78:Issue 1(2018)
• Page Start: 135
• Page End: 151
• Publication Date: 2018-02-05
• Subjects: Hedging -- Copula -- Crop insurance -- Net income risk; Q13; Agriculture -- Finance -- Periodicals; Agriculture -- Finance -- Statistics -- Periodicals; Agricultural insurance -- Periodicals; Agriculture -- Taxation -- Periodicals; 332.71
• DOI: 10.1108/AFR-05-2017-0036
• Languages: English
• ISSNs: 0002-1466

Abstract

Abstract : Purpose: At the beginning of the production year producers face a complex risk management decision environment given by risks specific to their operation, multiple crop insurance contracts and hedging opportunities. The purpose of this paper is to provide a producer-level framework for risk management decision making, focusing on the interaction between crop insurance and hedging. Design/methodology/approach: The authors develop a Monte Carlo simulation model that generates a producer's net income (NI) distribution that incorporates historical producer risk, price-yield correlation via a copula, price risk, and production costs. The authors evaluate the NI distribution through a modified Modern Portfolio Theory (MPT) decision framework. The authors use the modified MPT decision framework to explore tradeoffs between expected NI and farm ruin (defined as 1 or 5 percent expected shortfall) from different crop insurance contracts and pre-harvest hedging options. Findings: Only revenue protection and the highest two levels of coverage level exist on the efficient frontier. The level of hedging on the efficient frontier ranges from 0 to 55 percent of Actual Production History. The authors find that increasing coverage level 5 percent (from 80 to 85 percent) negatively impacts the optimal hedging amount by 26 percentage points (from 35 to 9 percent). Originality/value: The model provides the precise identification of financial benefits from different risk management strategies by incorporating producer-level historical yield data, using a copula to capture yield-price dependency structure and producer production cost in generating the NI distribution. This model can be applied to any producer's characteristics and data.