Abstract
Changing weather patterns may impose increased risk to the creditworthiness of financial institutions in the agriculture sector. To reduce the credit risk caused by climate change, financial institutions need to update their agricultural lending portfolios to consider climate change scenarios. In this paper we introduce a framework to compute the optimal agricultural lending portfolio under different increased temperature scenarios. In this way we quantify the impact of increased temperature, taken as a measure of climate change, on credit risk. We provide a detailed case study of how our approach applies to the problem of optimizing a portfolio of agricultural loans made to corn farmers across different corn producing regions of Ontario, Canada, under various climate change scenarios. We conclude that the lending portfolio obtained by taking into account the climate change is less risky than the lending portfolio neglecting climate change.
Highlights
The impacts of climate change are manifesting through rising sea levels, reduced ice cover, extreme weather events, erratic weather patterns, and record-breaking temperatures across the globe
Our model provides us with the optimal loan portfolio under a given climate change scenario, which would help the banks to decide on their budget allocation to n regions in order to minimize their credit risk for a given minimum acceptable level of return
We use the methodology developed until this point to quantify the impact of climate changes on the credit risk of agricultural loans made to Ontario corn farmers
Summary
The impacts of climate change are manifesting through rising sea levels, reduced ice cover, extreme weather events, erratic weather patterns, and record-breaking temperatures across the globe. This study examines how climate change models can be used in portfolio optimization of loans to the agricultural sector, and how climate change impacts credit risk. We have designed a framework to determine the optimal agricultural lending portfolio under different climate change scenarios to control the climate related credit risk. We use the corn yield, simulated in [26] for 10 regions of Ontario under the considered climate change scenarios and the optimal lending portfolios for these 10 regions are determined This methodology can help banks decide their lending portfolio in a given geographical region under different climate change scenarios and will provide them with the information to control climate related credit risk by updating their agricultural lending portfolio.
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