IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i23p3058-d690094.html
   My bibliography  Save this article

A Case Study of the Impact of Climate Change on Agricultural Loan Credit Risk

Author

Listed:
  • Jagdeep Kaur Brar

    (Department of Mathematics & Statistics, University of British Columbia, Okanagan Campus (UBCO), Kelowna, BC V1V 1V7, Canada)

  • Antoine Kornprobst

    (School of Statistics & Actuarial Sciences, University of Western Ontario (UWO), London, ON N6A 3K7, Canada)

  • Willard John Braun

    (Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Okanagan Campus (UBCO), Kelowna, BC V1V 1V7, Canada)

  • Matthew Davison

    (School of Statistics & Actuarial Sciences, University of Western Ontario (UWO), London, ON N6A 3K7, Canada)

  • Warren Hare

    (Department of Mathematics, University of British Columbia, Okanagan Campus (UBCO), Kelowna, BC V1V 1V7, Canada)

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.

Suggested Citation

  • Jagdeep Kaur Brar & Antoine Kornprobst & Willard John Braun & Matthew Davison & Warren Hare, 2021. "A Case Study of the Impact of Climate Change on Agricultural Loan Credit Risk," Mathematics, MDPI, vol. 9(23), pages 1-23, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3058-:d:690094
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/23/3058/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/23/3058/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dietsch, Michel & Petey, Joel, 2002. "The credit risk in SME loans portfolios: Modeling issues, pricing, and capital requirements," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 303-322, March.
    2. Ieda, Akira & Marumo, Kohei & Yoshiba, Toshinao, 2000. "A Simplified Method for Calculating the Credit Risk of Lending Portfolios," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 18(2), pages 49-82, December.
    3. Freya von Negenborn & Ron Weber & Oliver Musshoff, 2018. "Explaining weather-related credit risk with evapotranspiration and precipitation indices," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 78(2), pages 246-261, February.
    4. Elena Georgopoulou & Sebastian Mirasgedis & Yannis Sarafidis & Vassiliki Hontou & Nikos Gakis & Dimitri Lalas & Foteini Xenoyianni & Nikos Kakavoulis & Dimitris Dimopoulos & Vrassidas Zavras, 2015. "A methodological framework and tool for assessing the climate change related risks in the banking sector," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 58(5), pages 874-897, May.
    5. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    6. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
    7. Betz, Jennifer & Krüger, Steffen & Kellner, Ralf & Rösch, Daniel, 2020. "Macroeconomic effects and frailties in the resolution of non-performing loans," Journal of Banking & Finance, Elsevier, vol. 112(C).
    8. Matthews, R. B. & Kropff, M. J. & Horie, T. & Bachelet, D., 1997. "Simulating the impact of climate change on rice production in Asia and evaluating options for adaptation," Agricultural Systems, Elsevier, vol. 54(3), pages 399-425, July.
    9. Antoine Kornprobst & Matt Davison, 2020. "Influence Of Climate Change On The Corn Yield In Ontario And Its Impact On Corn Farms Income At The 2068 Horizon," Papers 2003.01270, arXiv.org, revised May 2020.
    10. Molua, Ernest L. & Lambi, Cornelius M., 2007. "The economic impact of climate change on agriculture in Cameroon," Policy Research Working Paper Series 4364, The World Bank.
    11. Ani L. Katchova & Peter J. Barry, 2005. "Credit Risk Models and Agricultural Lending," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(1), pages 194-205.
    12. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gao, Wei & Ju, Ming & Yang, Tongyang, 2023. "Severe weather and peer-to-peer farmers’ loan default predictions: Evidence from machine learning analysis," Finance Research Letters, Elsevier, vol. 58(PA).
    2. Helena Redondo & Elisa Aracil, 2024. "Climate‐related credit risk: Rethinking the credit risk framework," Global Policy, London School of Economics and Political Science, vol. 15(S1), pages 21-33, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Szego, Giorgio, 2005. "Measures of risk," European Journal of Operational Research, Elsevier, vol. 163(1), pages 5-19, May.
    2. Stelios Bekiros & Nikolaos Loukeris & Iordanis Eleftheriadis & Christos Avdoulas, 2019. "Tail-Related Risk Measurement and Forecasting in Equity Markets," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 783-816, February.
    3. Mariusz Górajski & Dobromił Serwa & Zuzanna Wośko, 2019. "Measuring expected time to default under stress conditions for corporate loans," Empirical Economics, Springer, vol. 57(1), pages 31-52, July.
    4. Annalisa Di Clemente, 2020. "Modeling Portfolio Credit Risk Taking into Account the Default Correlations Using a Copula Approach: Implementation to an Italian Loan Portfolio," JRFM, MDPI, vol. 13(6), pages 1-24, June.
    5. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    6. Kull, Andreas, 2009. "Sharing Risk – An Economic Perspective," ASTIN Bulletin, Cambridge University Press, vol. 39(2), pages 591-613, November.
    7. Curtis, John & Lynch, Muireann Á. & Zubiate, Laura, 2016. "The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland," Energy Economics, Elsevier, vol. 58(C), pages 186-198.
    8. Brian Tomlin & Yimin Wang, 2005. "On the Value of Mix Flexibility and Dual Sourcing in Unreliable Newsvendor Networks," Manufacturing & Service Operations Management, INFORMS, vol. 7(1), pages 37-57, June.
    9. Alexander, Gordon J. & Baptista, Alexandre M. & Yan, Shu, 2014. "Bank regulation and international financial stability: A case against the 2006 Basel framework for controlling tail risk in trading books," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 107-130.
    10. Kolos Ágoston, 2012. "CVaR minimization by the SRA algorithm," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(4), pages 623-632, December.
    11. Vladimir Rankovic & Mikica Drenovak & Branko Uroševic & Ranko Jelic, 2016. "Mean Univariate-GARCH VaR Portfolio Optimization: Actual Portfolio Approach," CESifo Working Paper Series 5731, CESifo.
    12. Harris, Richard D.F. & Mazibas, Murat, 2013. "Dynamic hedge fund portfolio construction: A semi-parametric approach," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 139-149.
    13. Maziar Sahamkhadam, 2021. "Dynamic copula-based expectile portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 22(3), pages 209-223, May.
    14. Alexandre Carbonneau & Fr'ed'eric Godin, 2021. "Deep equal risk pricing of financial derivatives with non-translation invariant risk measures," Papers 2107.11340, arXiv.org.
    15. Martin Herdegen & Cosimo Munari, 2023. "An elementary proof of the dual representation of Expected Shortfall," Papers 2306.14506, arXiv.org.
    16. Matthew Norton & Valentyn Khokhlov & Stan Uryasev, 2021. "Calculating CVaR and bPOE for common probability distributions with application to portfolio optimization and density estimation," Annals of Operations Research, Springer, vol. 299(1), pages 1281-1315, April.
    17. Juan Ma & Foad Mahdavi Pajouh & Balabhaskar Balasundaram & Vladimir Boginski, 2016. "The Minimum Spanning k -Core Problem with Bounded CVaR Under Probabilistic Edge Failures," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 295-307, May.
    18. Ken Kobayashi & Yuichi Takano & Kazuhide Nakata, 2021. "Bilevel cutting-plane algorithm for cardinality-constrained mean-CVaR portfolio optimization," Journal of Global Optimization, Springer, vol. 81(2), pages 493-528, October.
    19. Yuanying Guan & Zhanyi Jiao & Ruodu Wang, 2022. "A reverse ES (CVaR) optimization formula," Papers 2203.02599, arXiv.org, revised May 2023.
    20. Xia Han & Bin Wang & Ruodu Wang & Qinyu Wu, 2021. "Risk Concentration and the Mean-Expected Shortfall Criterion," Papers 2108.05066, arXiv.org, revised Apr 2022.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3058-:d:690094. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.