Default risk in agricultural lending, the effects of commodity price volatility and climate. Issue 4 (28th October 2014)

• Record Type: Journal Article
• Authors: Castro, Carlos; Garcia, Karen
• Is Part Of: Agricultural finance review. Volume 74:Issue 4(2014)
• Page Start: 501
• Page End: 521
• Publication Date: 2014-10-28
• Subjects: Agriculture -- Finance -- Periodicals; Agriculture -- Finance -- Statistics -- Periodicals; Agricultural insurance -- Periodicals; Agriculture -- Taxation -- Periodicals; 332.71
• DOI: 10.1108/AFR-10-2013-0036
• Languages: English
• ISSNs: 0002-1466

Abstract

<abstract> <title> <x content-type="archive" xml:space="preserve">Abstract</x> </title> <sec> <title content-type="abstract-heading">Purpose</title> <p> – Commodity price volatility and small variations in climate conditions may have an important impact on the creditworthiness of any agricultural project. The evolution of such risk factors is vital for the credit risk analysis of a rural bank. The purpose of this paper is to determine the importance of price volatility and climate factors within a default risk model. </p> </sec> <sec> <title content-type="abstract-heading">Design/methodology/approach</title> <p> – The authors estimate a generalized linear model (GLM) based on a structural default risk model. With the estimated factor loadings, the authors simulate the loss distribution of the portfolio and perform stress test to determine the impact of the relevant risk factors on economic capital. </p> </sec> <sec> <title content-type="abstract-heading">Findings</title> <p> – The results indicate that both the price volatility and climate factors are statistically significant; however, their economic significance is smaller compare to other factors that the authors control for: macroeconomic conditions for the agricultural sector and intermediate input prices. </p> </sec> <sec> <title content-type="abstract-heading">Research limitations/implications</title> <p> – The analysis of non-systemic risk factors such as price volatility and climate conditions requires statistical methods focussed on measuring causal effects at higher quantiles, not just at the conditional mean, this is, however, a current limitation of GLMs. </p> </sec> <sec> <title content-type="abstract-heading">Practical implications</title> <p> – The authors provide a design of a portfolio credit risk model, that is more suited to the special characteristics of a rural bank, than commercial credit risk models. </p> </sec> <sec> <title content-type="abstract-heading">Originality/value</title> <p> – The paper incorporates agricultural-specific risk factors in a default risk model and a portfolio credit risk model.</p> </sec> </abstract>