[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126760-en":3,"doc-seo-126760-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126760,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Forecasting for regulatory credit loss derived from the COVID-19 pandemic - A machine learning approach","The economic shock from the COVID-19 pandemic disrupted risk management in financial institutions and made regulatory outcomes difficult to forecast reliably with traditional approaches. Regulatory credit loss for defaulted mortgages, measured by the expected loss best estimate (ELBE), is forecasted using a machine learning technique. The study projects two ELBEs for 2022—one reflecting the outbreak and one assuming no pandemic—and compares the results. Findings indicate the crisis adversely affects high-risk portfolios, and the method performs strongly for estimating future expected and unexpected losses under extreme events.","Research in International Business and Finance 64 (2023) 101907  \nContents lists available at ScienceDirect  \nResearch in International Business and Finance  \njournal [homepage:](homepage: www.elsevier.com/locate/ribaf)[ www.elsevier.com/locate/ribaf](homepage: www.elsevier.com/locate/ribaf)  \n| Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach☆\u003Cbr>Marta Ramos Gonz´alez a, *, 1, Antonio Partal Ure˜nab, Pilar G´omez Fern´andezAguadob |  |  |  |\n| --- | --- | --- | --- |\n| a European Investment Bank, Luxembourg, Luxembourg\u003Cbr>b Department of Financial Economics and Accounting, Faculty of Legal and Social Sciences, University of Ja´en, Ja´en, Spain |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| JEL classifications: C53\u003Cbr>G17\u003Cbr>D81\u003Cbr>G21\u003Cbr>G28\u003Cbr>G32\u003Cbr>Keywords: Machine learning COVID-19\u003Cbr>Internal-rating-based Credit risk Defaulted exposures |  | The economic onslaught of the COVID-19 pandemic has compromised the risk management of financial institutions. The consequences related to such an unprecedented situation are difficult to foresee with certainty using traditional methods. The regulatory credit loss attached to defaulted mortgages, so-called expected loss best estimate (ELBE), is forecasted using a machine learning technique. The projection of two ELBEs for 2022 and their comparison are presented. One accounts for the outbreak’s impact, and the other presumes the nonexistence of the pandemic. Then, it is concluded that the referred crisis surely adversely affects said high-risk portfolios. The proposed method has excellent performance and may serve to estimate future expected and unexpected losses amidst any event of extraordinary magnitude. |  |\n\n1. Introduction  \nAccording to the World Bank (2020), the 2020 economic recession-resulting from the measures adopted to mitigate the spread of COVID-19 – resulted in the fastest, steepest decline in consensus growth forecasts among all global downturns since 1990. The European Commission (2020) published analyses in alignment with this statement while focusing on the EU economy, observing substantial differences across countries and industries. Several articles were recently published analysing the impact of the disease outbreak in various economic fields, such as business model shifts (Seetharaman, 2020), exchange rate shocks (Narayan, 2021), firm performance (Hu and Zhang, 2021), income distribution (O’Donoghue et al., 2020) and stock market (Sharif et al., 2020), among others. As published later by the World Bank (2021a), the aforementioned expectations were met, with some sharp rebounds observed during 2021 in major economies, particularly in the US, although emerging markets and developing economies are generally lagging. Nonetheless, forecasting the consequences of such an unprecedented situation (IMF, 2020b) has proven challenging. Ioannidis et al.(2022) found significant failures while reviewing certain COVID-19 epidemic predictions.  \n☆ To be considered for the special issue of Research in International Business and Finance entitled “Artificial Intelligence and Machine Learning in Finance”  \n* Corresponding author.  \n[E-mail address:](E-mail address: m_ramos@tutanota.com)[ m_ramos@tutanota.com](E-mail address: m_ramos@tutanota.com) (M.R. Gonz´alez).  \n1 The views expressed are those of the authors and do not necessarily reflect those of the European Investment Bank  \n[https://doi.org/10.1016/j.ribaf.2023.101907](https://doi.org/10.1016/j.ribaf.2023.101907)  \nReceived 11 January 2022; Received in revised form 26 January 2023; Accepted 15 February 2023 Available online 16 February 2023  \n0275-5319/© 2023 Elsevier B.V. All rights reserved.  \nRecent studies published by the Organisation for Economic Co-operation and Development (OECD, 2021) projected an increase in the non-performing loan ratio across all world regions as a consequence of the coronavirus crisis. Learning from experience, European org","cbCaiu2Lk360ouv0","https://ap.wps.com/l/cbCaiu2Lk360ouv0","pdf",1471616,1,14,"English","en",105,"# Introduction\n## Background on COVID-19 macroeconomic impacts\n## Regulatory credit risk and defaulted exposures\n## Internal-rating-based approaches and ELBE framework","[{\"question\":\"How does the paper define the regulatory credit loss problem for defaulted mortgages?\",\"answer\":\"It focuses on regulatory credit loss measured through the expected loss best estimate (ELBE) for defaulted mortgages.\"},{\"question\":\"What machine learning-based forecasting setup does the study use for 2022?\",\"answer\":\"It generates two ELBE projections for 2022: one incorporating the outbreak’s impact and another assuming the pandemic did not occur, then compares them.\"},{\"question\":\"What is the main conclusion about the effect of COVID-19 on high-risk credit portfolios?\",\"answer\":\"The results conclude that the crisis adversely affects the high-risk portfolios considered in the analysis.\"}]","Forecasting for regulatory credit loss derived from the COVID-19 pandemic - A machine learning approach | PDF",1785934642,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"forecasting-for-regulatory-credit-loss-derived-from-the-covid-19-pandemic-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forecasting-for-regulatory-credit-loss-derived-from-the-covid-19-pandemic-a-machine-learning-approach/126760/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the paper define the regulatory credit loss problem for defaulted mortgages?","Question",{"text":75,"@type":76},"It focuses on regulatory credit loss measured through the expected loss best estimate (ELBE) for defaulted mortgages.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning-based forecasting setup does the study use for 2022?",{"text":80,"@type":76},"It generates two ELBE projections for 2022: one incorporating the outbreak’s impact and another assuming the pandemic did not occur, then compares them.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main conclusion about the effect of COVID-19 on high-risk credit portfolios?",{"text":84,"@type":76},"The results conclude that the crisis adversely affects the high-risk portfolios considered in the analysis.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]