[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/asieco/v84y2023ics1049007822001300.html
   My bibliography  Save this article

Modeling an early warning system for household debt risk in Korea: A simple deep learning approach

Author

Listed:
  • Kwon, Yujin
  • Park, Sung Y.
Abstract
We construct an early warning indicator for household debt risk by analyzing the relationship between household debt and certain important macroeconomic determinants using a simple deep learning approach. A precise and informative indicator can help inform economic policies, especially in light of the recent growth in the ratio of household debt to income. Although several studies have analyzed the determinants of the household debt crisis, very few have examined early warning indicators for household debt risk. Some studies suggest that a situation can be regarded as a crisis if the household debt ratio is greater than 50% or 85%. However, as the household debt ratio in Korea is already over this threshold, this criterion is neither informative nor useful. Accordingly, we propose a transformed index that addresses long-term memory characteristics. Moreover, five categories for the degree of household debt crisis are considered instead of the binary variable that has been frequently used in previous studies. Furthermore, we use a well-known deep learning approach to find a non-linear relationship between crisis indices and many factors. The empirical results demonstrate that the proposed early warning indicator explains the household debt crisis quite well.

Suggested Citation

  • Kwon, Yujin & Park, Sung Y., 2023. "Modeling an early warning system for household debt risk in Korea: A simple deep learning approach," Journal of Asian Economics, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:asieco:v:84:y:2023:i:c:s1049007822001300
    DOI: 10.1016/j.asieco.2022.101574
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1049007822001300
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.asieco.2022.101574?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nyman, Rickard & Kapadia, Sujit & Tuckett, David, 2021. "News and narratives in financial systems: Exploiting big data for systemic risk assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    2. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    3. Qin, Xiao & Luo, Chengying, 2014. "Capital account openness and early warning system for banking crises in G20 countries," Economic Modelling, Elsevier, vol. 39(C), pages 190-194.
    4. Alessi, Lucia & Detken, Carsten, 2018. "Identifying excessive credit growth and leverage," Journal of Financial Stability, Elsevier, vol. 35(C), pages 215-225.
    5. Sebastian Barnes & Garry Young, 2003. "The rise in US household debt: assessing its causes and sustainability," Bank of England working papers 206, Bank of England.
    6. Stockhammer, Engelbert & Wildauer, Rafael, 2018. "Expenditure Cascades, Low Interest Rates or Property Booms? Determinants of Household Debt in OECD Countries," Review of Behavioral Economics, now publishers, vol. 5(2), pages 85-121, September.
    7. Tsai, Feng-Tse & Lu, Hsin-Min & Hung, Mao-Wei, 2016. "The impact of news articles and corporate disclosure on credit risk valuation," Journal of Banking & Finance, Elsevier, vol. 68(C), pages 100-116.
    8. Caggiano, Giovanni & Calice, Pietro & Leonida, Leone, 2014. "Early warning systems and systemic banking crises in low income countries: A multinomial logit approach," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 258-269.
    9. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    10. Ms. Meral Karasulu, 2008. "Stress Testing Household Debt in Korea," IMF Working Papers 2008/255, International Monetary Fund.
    11. Galil, Koresh & Soffer, Gil, 2011. "Good news, bad news and rating announcements: An empirical investigation," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3101-3119, November.
    12. Meniago, Christelle & Mukuddem-Petersen, Janine & Petersen, Mark A. & Mongale, Itumeleng P., 2013. "What causes household debt to increase in South Africa?," Economic Modelling, Elsevier, vol. 33(C), pages 482-492.
    13. Giovanni Caggiano & Leone Leonida, 2009. "International output convergence: evidence from an autocorrelation function approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(1), pages 139-162.
    14. Oliver D. Babson & Steven B. Kamin, 1999. "The contributions of domestic and external factors to Latin American devaluation crises: an early warning systems approach," International Finance Discussion Papers 645, Board of Governors of the Federal Reserve System (U.S.).
    15. Stephen Cecchetti & Madhusudan Mohanty & Fabrizio Zampolli, 2011. "The real effects of debt," BIS Working Papers 352, Bank for International Settlements.
    16. A. Berg & C. Pattillo, 1999. "What Caused the Asian Crises: An Early Warning System Approach," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 28(3), pages 285-334, November.
    17. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    18. Franklin Allen & Douglas Gale, 2000. "Financial Contagion," Journal of Political Economy, University of Chicago Press, vol. 108(1), pages 1-33, February.
    19. Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020. "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1163-1172.
    20. Anna Zabai, 2017. "Household debt: recent developments and challenges," BIS Quarterly Review, Bank for International Settlements, December.
    21. John Haslett & Adrian E. Raftery, 1989. "Space‐Time Modelling with Long‐Memory Dependence: Assessing Ireland's Wind Power Resource," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 38(1), pages 1-21, March.
    22. Cynamon Barry Z. & Fazzari Steven M., 2008. "Household Debt in the Consumer Age: Source of Growth--Risk of Collapse," Capitalism and Society, De Gruyter, vol. 3(2), pages 1-32, October.
    23. Michelacci, Claudio & Zaffaroni, Paolo, 2000. "(Fractional) beta convergence," Journal of Monetary Economics, Elsevier, vol. 45(1), pages 129-153, February.
    24. Wildauer, Rafael & Stockhammer, Engelbert, 2018. "Expenditure cascades, low interest Rates, credit deregulation or property booms? Determinants of household debt in OECD countries," Greenwich Papers in Political Economy 20882, University of Greenwich, Greenwich Political Economy Research Centre.
    25. Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.
    26. Hyun Jeong Kim & Jong Chil Son & Myung-Soo Yie, 2017. "House Price Dynamics with Household Debt: The Korean Case-super-," Asian Economic Journal, East Asian Economic Association, vol. 31(1), pages 39-59, March.
    27. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
    28. Smales, Lee A., 2016. "News sentiment and bank credit risk," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 37-61.
    29. Song, Wonho, 2010. "Building an Early Warning System for Crude Oil Price Using Neural Network," East Asian Economic Review, Korea Institute for International Economic Policy, vol. 14(2), pages 79-109, December.
    30. Steven B. Kamin & Shawna L. Samuel & John W. Schindler, 2001. "The contribution of domestic and external factors to emerging market devaluation crises: an early warning systems approach," International Finance Discussion Papers 711, Board of Governors of the Federal Reserve System (U.S.).
    31. Kim, Hyun Jeong & Lee, Dongyeol & Son, Jong Chil & Son, Min Kyu, 2014. "Household indebtedness in Korea: Its causes and sustainability," Japan and the World Economy, Elsevier, vol. 29(C), pages 59-76.
    32. Markus Holopainen & Peter Sarlin, 2017. "Toward robust early-warning models: a horse race, ensembles and model uncertainty," Quantitative Finance, Taylor & Francis Journals, vol. 17(12), pages 1933-1963, December.
    33. Rambaccussing, Dooruj & Kwiatkowski, Andrzej, 2020. "Forecasting with news sentiment: Evidence with UK newspapers," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1501-1516.
    34. Stephen G. Cecchetti & Sunil Mohanty & Fabrizio Zampolli, 2011. "Achieving growth amid fiscal imbalances: the real effects of debt," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 145-196.
    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. Nguyen Quoc Hung & Trinh Hoang Viet & Phuong Truong Viet & Ly Truong Thi Minh, 2024. "Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam," Foundations of Management, Sciendo, vol. 16(1), pages 195-216.

    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. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    2. Abd Samad, Khairunnisa & Mohd Daud, Siti Nurazira & Mohd Dali, Nuradli Ridzwan Shah, 2020. "Early Warning Indicators for Systemic Banking Crises: Household Debt and Property Prices," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 54(1), pages 121-134.
    3. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    4. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2018. "An evaluation of early warning models for systemic banking crises: Does machine learning improve predictions?," Discussion Papers 48/2018, Deutsche Bundesbank.
    5. Bogdan Andrei Dumitrescu & Adrian Enciu & Cătălina Adriana Hândoreanu & Carmen Obreja & Florin Blaga, 2022. "Macroeconomic Determinants of Household Debt in OECD Countries," Sustainability, MDPI, vol. 14(7), pages 1-14, March.
    6. Deng, Xin & Yu, Mingzhe, 2021. "Does the marginal child increase household debt? – Evidence from the new fertility policy in China," International Review of Financial Analysis, Elsevier, vol. 77(C).
    7. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
    8. Truong, Chi & Sheen, Jeffrey & Trück, Stefan & Villafuerte, James, 2022. "Early warning systems using dynamic factor models: An application to Asian economies," Journal of Financial Stability, Elsevier, vol. 58(C).
    9. Huynh, Tran & Uebelmesser, Silke, 2024. "Early warning models for systemic banking crises: Can political indicators improve prediction?," European Journal of Political Economy, Elsevier, vol. 81(C).
    10. Maria Ludovica Drudi & Stefano Nobili, 2021. "A liquidity risk early warning indicator for Italian banks: a machine learning approach," Temi di discussione (Economic working papers) 1337, Bank of Italy, Economic Research and International Relations Area.
    11. Xianglong Liu, 2023. "Towards Better Banking Crisis Prediction: Could an Automatic Variable Selection Process Improve the Performance?," The Economic Record, The Economic Society of Australia, vol. 99(325), pages 288-312, June.
    12. Ricardo Barradas & Ines Tomas, 2023. "Household indebtedness in the European Union countries: Going beyond the mainstream interpretation," PSL Quarterly Review, Economia civile, vol. 76(304), pages 21-49.
    13. Mark Setterfield, 2024. "Managing the Discontent of the Losers Redux: A Future of Authoritarian Neoliberalism or Social Capitalism?," FMM Working Paper 98-2024, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    14. Mark Setterfield & Y.K. Kim, 2024. "How financially fragile can households become? Household borrowing, the welfare state, and macroeconomic resilience," Review of Evolutionary Political Economy, Springer, vol. 5(1), pages 121-151, June.
    15. Jean-Charles Bricongne & Leonor Coutinho & Alessandro Turrini & Stefan Zeugner, 2020. "Is Private Debt Excessive?," Open Economies Review, Springer, vol. 31(2), pages 471-512, April.
    16. Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
    17. Barbara Jarmulska, 2022. "Random forest versus logit models: Which offers better early warning of fiscal stress?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 455-490, April.
    18. Anelisa Nomatye & Andrew Phiri, 2018. "Investigating the Macroeconomic Determinants of Hosehold Debt in South Africa," International Journal of Economics and Financial Issues, Econjournals, vol. 8(2), pages 62-69.
    19. David Greasley & Les Oxley, 2010. "Cliometrics And Time Series Econometrics: Some Theory And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 24(5), pages 970-1042, December.
    20. Ozlem Albayrak, 2020. "Household Consumption, Household Indebtedness, and Inequality in Turkey: A Microeconometric Analysis," Economics Working Paper Archive wp_954, Levy Economics Institute.

    More about this item

    Keywords

    Household debt; Debt ratio; Debt crisis; Early warning indicator; Deep learning;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

    Statistics

    Access and download statistics

    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:eee:asieco:v:84:y:2023:i:c:s1049007822001300. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/asieco .

    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.