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João Afonso Bastos
(Joao Afonso Bastos)

Personal Details

First Name:Joao
Middle Name:Afonso
Last Name:Bastos
Suffix:
RePEc Short-ID:pba531
[This author has chosen not to make the email address public]

Affiliation

Instituto Superior de Economia e Gestão (ISEG)
Universidade de Lisboa

Lisboa, Portugal
http://www.iseg.ulisboa.pt/
RePEc:edi:isutlpt (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. João A. Bastos & Maria Inês Bernardes, 2024. "Understanding online purchases with explainable machine learning," Working Papers REM 2024/0313, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
  2. João A. Bastos & Jeanne Paquette, 2024. "On the uncertainty of real estate price predictions," Working Papers REM 2024/0314, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
  3. João A. Bastos, 2023. "Conformal prediction of option prices," Working Papers REM 2023/0304, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
  4. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
  5. João A. Bastos & Sara M. Matos, 2021. "Explainable models of credit losses," Working Papers REM 2021/0161, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
  6. Bastos, João A., 2019. "Forecasting the capacity of mobile networks," MPRA Paper 92727, University Library of Munich, Germany.
  7. Joao A. Bastos, 2013. "Ensemble predictions of recovery rates," CEMAPRE Working Papers 1301, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
  8. Joao A. Bastos, 2010. "Predicting bank loan recovery rates with neural networks," CEMAPRE Working Papers 1003, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
  9. Joao A. Bastos & Jorge Caiado, 2010. "Recurrence quantification analysis of global stock markets," CEMAPRE Working Papers 1006, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
  10. Joao A. Bastos & Jorge Caiado, 2010. "The structure of international stock market returns," CEMAPRE Working Papers 1002, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
  11. Joao A. Bastos & Joaquim J. S. Ramalho, 2010. "Nonparametric models of financial leverage decisions," CEMAPRE Working Papers 1005, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
  12. Joao A. Bastos & Jorge Caiado, 2009. "Clustering financial time series with variance ratio statistics," CEMAPRE Working Papers 0904, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
  13. Joao A. Bastos, 2009. "Forecasting bank loans loss-given-default," CEMAPRE Working Papers 0901, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
  14. Bastos, Joao, 2007. "Credit scoring with boosted decision trees," MPRA Paper 8034, University Library of Munich, Germany.

Articles

  1. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
  2. João A. Bastos, 2022. "Predicting Credit Scores with Boosted Decision Trees," Forecasting, MDPI, vol. 4(4), pages 1-11, November.
  3. João A. Bastos, 2019. "Forecasting the capacity of mobile networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(2), pages 231-242, October.
  4. João A. Bastos & Joaquim J. S. Ramalho, 2016. "Nonparametric Models Of Financial Leverage Decisions," Bulletin of Economic Research, Wiley Blackwell, vol. 68(4), pages 348-366, October.
  5. João A. Bastos & Jorge Caiado, 2014. "Clustering financial time series with variance ratio statistics," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2121-2133, December.
  6. João Bastos, 2014. "Ensemble Predictions of Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 46(2), pages 177-193, October.
  7. Bastos, João A. & Caiado, Jorge, 2011. "Recurrence quantification analysis of global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1315-1325.
  8. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.

    Cited by:

    1. Lúcio, Francisco & Caiado, Jorge, 2022. "COVID-19 and Stock Market Volatility: A Clustering Approach for S&P 500 Industry Indices," Finance Research Letters, Elsevier, vol. 49(C).

  2. João A. Bastos & Sara M. Matos, 2021. "Explainable models of credit losses," Working Papers REM 2021/0161, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.

    Cited by:

    1. Petter Eilif de Lange & Borger Melsom & Christian Bakke Vennerød & Sjur Westgaard, 2022. "Explainable AI for Credit Assessment in Banks," JRFM, MDPI, vol. 15(12), pages 1-23, November.
    2. Sun, Weixin & Zhang, Xuantao & Li, Minghao & Wang, Yong, 2023. "Interpretable high-stakes decision support system for credit default forecasting," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    3. González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    5. Julia Brasse & Hanna Rebecca Broder & Maximilian Förster & Mathias Klier & Irina Sigler, 2023. "Explainable artificial intelligence in information systems: A review of the status quo and future research directions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-30, December.
    6. Xiong Xiong & Fan Yang & Li Su, 2023. "Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques," Papers 2310.19200, arXiv.org.

  3. Bastos, João A., 2019. "Forecasting the capacity of mobile networks," MPRA Paper 92727, University Library of Munich, Germany.

    Cited by:

    1. Irina Kochetkova & Anna Kushchazli & Sofia Burtseva & Andrey Gorshenin, 2023. "Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models," Future Internet, MDPI, vol. 15(9), pages 1-15, August.

  4. Joao A. Bastos, 2013. "Ensemble predictions of recovery rates," CEMAPRE Working Papers 1301, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    2. Paolo Gambetti & Francesco Roccazzella & Frédéric Vrins, 2022. "Meta-Learning Approaches for Recovery Rate Prediction," Risks, MDPI, vol. 10(6), pages 1-29, June.
    3. Pascal François & Weiyu Jiang, 2019. "Credit Value Adjustment with Market-implied Recovery," Journal of Financial Services Research, Springer;Western Finance Association, vol. 56(2), pages 145-166, October.
    4. Hwang, Ruey-Ching & Chu, Chih-Kang & Yu, Kaizhi, 2020. "Predicting LGD distributions with mixed continuous and discrete ordinal outcomes," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1003-1022.
    5. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2020. "Forecasting recovery rates on non-performing loans with machine learning," LIDAM Discussion Papers LFIN 2020002, Université catholique de Louvain, Louvain Finance (LFIN).
    6. Dimitris Andriosopoulos & Michael Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Post-Print hal-02879937, HAL.
    7. Altman, Edward I. & Kalotay, Egon A., 2014. "Ultimate recovery mixtures," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 116-129.
    8. Nazemi, Abdolreza & Baumann, Friedrich & Fabozzi, Frank J., 2022. "Intertemporal defaulted bond recoveries prediction via machine learning," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1162-1177.
    9. Chen, Xiaowei & Wang, Gang & Zhang, Xiangting, 2019. "Modeling recovery rate for leveraged loans," Economic Modelling, Elsevier, vol. 81(C), pages 231-241.
    10. Chih-Kang Chu & Ruey-Ching Hwang, 2019. "Predicting Loss Distributions for Small-Size Defaulted-Debt Portfolios Using a Convolution Technique that Allows Probability Masses to Occur at Boundary Points," Journal of Financial Services Research, Springer;Western Finance Association, vol. 56(1), pages 95-117, August.
    11. João A. Bastos, 2022. "Predicting Credit Scores with Boosted Decision Trees," Forecasting, MDPI, vol. 4(4), pages 1-11, November.
    12. Marc Gürtler & Marvin Zöllner, 2023. "Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 251-287, March.
    13. Nazemi, Abdolreza & Fatemi Pour, Farnoosh & Heidenreich, Konstantin & Fabozzi, Frank J., 2017. "Fuzzy decision fusion approach for loss-given-default modeling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 780-791.
    14. Miller, Patrick & Töws, Eugen, 2018. "Loss given default adjusted workout processes for leases," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 189-201.
    15. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
    16. Kaposty, Florian & Kriebel, Johannes & Löderbusch, Matthias, 2020. "Predicting loss given default in leasing: A closer look at models and variable selection," International Journal of Forecasting, Elsevier, vol. 36(2), pages 248-266.
    17. Hurlin, Christophe & Leymarie, Jérémy & Patin, Antoine, 2018. "Loss functions for Loss Given Default model comparison," European Journal of Operational Research, Elsevier, vol. 268(1), pages 348-360.
    18. Ruey-Ching Hwang & Chih-Kang Chu & Kaizhi Yu, 2021. "Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(3), pages 143-172, June.
    19. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    20. Olson, Luke M. & Qi, Min & Zhang, Xiaofei & Zhao, Xinlei, 2021. "Machine learning loss given default for corporate debt," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 144-159.

  5. Joao A. Bastos, 2010. "Predicting bank loan recovery rates with neural networks," CEMAPRE Working Papers 1003, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Aleksey Min & Matthias Scherer & Amelie Schischke & Rudi Zagst, 2020. "Modeling Recovery Rates of Small- and Medium-Sized Entities in the US," Mathematics, MDPI, vol. 8(11), pages 1-18, October.
    2. Stephan Höcht & Aleksey Min & Jakub Wieczorek & Rudi Zagst, 2022. "Explaining Aggregated Recovery Rates," Risks, MDPI, vol. 10(1), pages 1-30, January.
    3. Natalia Nehrebecka, 2019. "Bank loans recovery rate in commercial banks: A case study of non-financial corporations," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 37(1), pages 139-172.

  6. Joao A. Bastos & Jorge Caiado, 2010. "Recurrence quantification analysis of global stock markets," CEMAPRE Working Papers 1006, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Halari, Anwar & Helliar, Christine & Power, David M. & Tantisantiwong, Nongnuch, 2019. "Taking advantage of Ramadan and January in Muslim countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 74(C), pages 85-96.
    2. Kiran Sharma & Shreyansh Shah & Anindya S. Chakrabarti & Anirban Chakraborti, 2016. "Sectoral co-movements in the Indian stock market: A mesoscopic network analysis," Papers 1607.05514, arXiv.org.
    3. Fatoorehchi, Hooman & Zarghami, Reza & Abolghasemi, Hossein & Rach, Randolph, 2015. "Chaos control in the cerium-catalyzed Belousov–Zhabotinsky reaction using recurrence quantification analysis measures," Chaos, Solitons & Fractals, Elsevier, vol. 76(C), pages 121-129.
    4. Juan Benjamín Duarte Duarte & Juan Manuel Mascare?nas Pérez-Iñigo, 2014. "Comprobación de la eficiencia débil en los principales mercados financieros latinoamericanos," Estudios Gerenciales, Universidad Icesi, November.
    5. M. Shabani & M. Magris & George Tzagkarakis & J. Kanniainen & A. Iosifidis, 2023. "Predicting the state of synchronization of financial time series using cross recurrence plots," Post-Print hal-04415269, HAL.
    6. B. Goswami & G. Ambika & N. Marwan & J. Kurths, 2011. "On interrelations of recurrences and connectivity trends between stock indices," Papers 1103.5189, arXiv.org.
    7. Goswami, B. & Ambika, G. & Marwan, N. & Kurths, J., 2012. "On interrelations of recurrences and connectivity trends between stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4364-4376.
    8. Ashe, Sinéad & Egan, Paul, 2023. "Examining financial and business cycle interaction using cross recurrence plot analysis," Finance Research Letters, Elsevier, vol. 51(C).
    9. Xu, Mengjia & Shang, Pengjian & Lin, Aijing, 2017. "Multiscale recurrence quantification analysis of order recurrence plots," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 381-389.
    10. Teresa Aparicio & Dulce Saura, 2013. "Do Exchange Rate Series Present General Dependence? Some Results using Recurrence Quantification Analysis," Journal of Economics and Behavioral Studies, AMH International, vol. 5(10), pages 678-686.
    11. M., Krishnadas & Harikrishnan, K.P. & Ambika, G., 2022. "Recurrence measures and transitions in stock market dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    12. Sergii Piskun & Oleksandr Piskun & Dmitry Chabanenko, 2011. "RQA Application for the Monitoring of Financial and Commodity markets state," Papers 1112.0297, arXiv.org.
    13. Marisa Faggini & Bruna Bruno & Anna Parziale, 2019. "Does Chaos Matter in Financial Time Series Analysis?," International Journal of Economics and Financial Issues, Econjournals, vol. 9(4), pages 18-24.
    14. Krishnadas M. & K. P. Harikrishnan & G. Ambika, 2022. "Recurrence measures and transitions in stock market dynamics," Papers 2208.03456, arXiv.org.
    15. Tantisantiwong, Nongnuch & Halari, Anwar & Helliar, Christine & Power, David, 2018. "East meets West: When the Islamic and Gregorian calendars coincide," The British Accounting Review, Elsevier, vol. 50(4), pages 402-424.
    16. Yao, Can-Zhong & Lin, Qing-Wen, 2017. "Recurrence plots analysis of the CNY exchange markets based on phase space reconstruction," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 584-596.
    17. Giuseppe Orlando & Giovanna Zimatore, 2021. "Recurrence Quantification Analysis of Business Cycles," Dynamic Modeling and Econometrics in Economics and Finance, in: Giuseppe Orlando & Alexander N. Pisarchik & Ruedi Stoop (ed.), Nonlinearities in Economics, chapter 0, pages 269-282, Springer.
    18. Marisa Faggini, 2011. "Chaotic Time Series Analysis in Economics: Balance and Perspectives," Working papers 25, Former Department of Economics and Public Finance "G. Prato", University of Torino.
    19. Sandoval, Leonidas Junior, 2013. "To lag or not to lag? How to compare indices of stock markets that operate at different times," Insper Working Papers wpe_319, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    20. Ioannis Andreadis & Athanasios D. Fragkou & Theodoros E. Karakasidis & Apostolos Serletis, 2023. "Nonlinear dynamics in Divisia monetary aggregates: an application of recurrence quantification analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-17, December.
    21. Froguel, Lucas Belasque & de Lima Prado, Thiago & Corso, Gilberto & dos Santos Lima, Gustavo Zampier & Lopes, Sergio Roberto, 2022. "Efficient computation of recurrence quantification analysis via microstates," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    22. Mostafa Shabani & Martin Magris & George Tzagkarakis & Juho Kanniainen & Alexandros Iosifidis, 2022. "Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots," Papers 2210.14605, arXiv.org, revised Nov 2022.
    23. Chen, Yuan & Lin, Aijing, 2022. "Order pattern recurrence for the analysis of complex systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

  7. Joao A. Bastos & Jorge Caiado, 2010. "The structure of international stock market returns," CEMAPRE Working Papers 1002, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Guglielmo Maria Caporale & Luis A. Gil-Alana & C. James Orlando, 2015. "Linkages between the US and European Stock Markets: A Fractional Cointegration Approach," CESifo Working Paper Series 5523, CESifo.
    2. P., Srinivasan & M., Kalaivani, 2013. "Stock Market Linkages in Emerging Asia-Pacific Markets," MPRA Paper 45871, University Library of Munich, Germany.
    3. Andile Khula & Ntebogang Dinah Moroke, 2017. "The Performance of Maximum Likelihood Factor Analysis on South African Stock Price Performance," Journal of Economics and Behavioral Studies, AMH International, vol. 8(6), pages 40-51.
    4. Srinivasan Palamalai & Kalaivani M. & Christopher Devakumar, 2013. "Stock Market Linkages in Emerging Asia-Pacific Markets," SAGE Open, , vol. 3(4), pages 21582440135, November.

  8. Joao A. Bastos & Joaquim J. S. Ramalho, 2010. "Nonparametric models of financial leverage decisions," CEMAPRE Working Papers 1005, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Feng Li & Mattias Villani, 2013. "Efficient Bayesian Multivariate Surface Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 706-723, December.
    2. Villani, Mattias & Kohn, Robert & Nott, David J., 2012. "Generalized smooth finite mixtures," Journal of Econometrics, Elsevier, vol. 171(2), pages 121-133.

  9. Joao A. Bastos & Jorge Caiado, 2009. "Clustering financial time series with variance ratio statistics," CEMAPRE Working Papers 0904, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari, 2021. "Trimmed fuzzy clustering of financial time series based on dynamic time warping," Annals of Operations Research, Springer, vol. 299(1), pages 1379-1395, April.
    2. Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.
    3. Erniel B. Barrios & Paolo Victor T. Redondo, 2024. "Nonparametric Test for Volatility in Clustered Multiple Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 861-876, February.
    4. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    5. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2014. "Clustering of financial time series in risky scenarios," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(4), pages 359-376, December.
    6. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    7. Galagedera, Don U.A., 2013. "A new perspective of equity market performance," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 333-357.
    8. Jorge Caiado & Nuno Crato & Pilar Poncela, 2020. "A fragmented-periodogram approach for clustering big data time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 117-146, March.
    9. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    10. Ekaterina Dorodnykh, 2013. "What Drives Stock Exchange Integration?," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 6(2), pages 47-79, September.
    11. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2015. "Clustering of time series via non-parametric tail dependence estimation," Statistical Papers, Springer, vol. 56(3), pages 701-721, August.
    12. Anna CZAPKIEWICZ & Pawel MAJDOSZ, 2014. "Grouping Stock Markets with Time-Varying Copula-GARCH Model," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(2), pages 144-159, March.
    13. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    14. Lúcio, Francisco & Caiado, Jorge, 2022. "COVID-19 and Stock Market Volatility: A Clustering Approach for S&P 500 Industry Indices," Finance Research Letters, Elsevier, vol. 49(C).

  10. Joao A. Bastos, 2009. "Forecasting bank loans loss-given-default," CEMAPRE Working Papers 0901, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Yuta Tanoue & Satoshi Yamashita & Hideaki Nagahata, 2020. "Comparison study of two-step LGD estimation model with probability machines," Risk Management, Palgrave Macmillan, vol. 22(3), pages 155-177, September.
    2. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Corporate Loan Recovery Rates under Downturn Conditions in a Developing Economy: Evidence from Zimbabwe," Risks, MDPI, vol. 10(10), pages 1-24, October.
    3. Stanhouse, Bryan & Schwarzkopf, Al & Ingram, Matt, 2011. "A computational approach to pricing a bank credit line," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1341-1351, June.
    4. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    5. Hong Wang & Catherine S. Forbes & Jean-Pierre Fenech & John Vaz, 2018. "The determinants of bank loan recovery rates in good times and bad - new evidence," Papers 1804.07022, arXiv.org.
    6. Guglielmo Maria Caporale & Alessandro Girardi, 2011. "Price Discovery and Trade Fragmentation in a Multi-Market Environment: Evidence from the MTS System," CESifo Working Paper Series 3525, CESifo.
    7. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2015. "Support vector regression for loss given default modelling," European Journal of Operational Research, Elsevier, vol. 240(2), pages 528-538.
    8. Peter-Hendrik Ingermann & Frederik Hesse & Christian Bélorgey & Andreas Pfingsten, 2016. "The recovery rate for retail and commercial customers in Germany: a look at collateral and its adjusted market values," Business Research, Springer;German Academic Association for Business Research, vol. 9(2), pages 179-228, August.
    9. Abu, Benjamin Musah & Domanban, Paul Bata & Haruna, Issahaku, 2017. "Microcredit Loan Repayment Default among Small Scale Enterprises: A Double Hurdle Approach," MPRA Paper 101576, University Library of Munich, Germany, revised 12 Mar 2017.
    10. Christophe Hurlin & Jérémy Leymarie & Antoine Patin, 2018. "Loss functions for LGD model comparison," Working Papers halshs-01516147, HAL.
    11. Morne Joubert & Tanja Verster & Helgard Raubenheimer & Willem D. Schutte, 2021. "Adapting the Default Weighted Survival Analysis Modelling Approach to Model IFRS 9 LGD," Risks, MDPI, vol. 9(6), pages 1-17, June.
    12. Gürtler, Marc & Hibbeln, Martin, 2013. "Improvements in loss given default forecasts for bank loans," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2354-2366.
    13. Krüger, Steffen & Rösch, Daniel, 2017. "Downturn LGD modeling using quantile regression," Journal of Banking & Finance, Elsevier, vol. 79(C), pages 42-56.
    14. Nithi Sopitpongstorn & Param Silvapulle & Jiti Gao, 2017. "Local logit regression for recovery rate," Monash Econometrics and Business Statistics Working Papers 19/17, Monash University, Department of Econometrics and Business Statistics.
    15. Florian Kaposty & Philipp Klein & Matthias Löderbusch & Andreas Pfingsten, 2022. "Loss given default in SME leasing," Review of Managerial Science, Springer, vol. 16(5), pages 1561-1597, July.
    16. Bonfim, Diana & Dias, Daniel A. & Richmond, Christine, 2012. "What happens after corporate default? Stylized facts on access to credit," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2007-2025.
    17. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    18. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).
    19. Stefan Hlawatsch & Sebastian Ostrowski, 2010. "Simulation and Estimation of Loss Given Default," FEMM Working Papers 100010, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    20. Alessandro Girardi & Marco Ventura, 2021. "Measuring credit crunch in Italy: evidence from a survey-based indicator," Annals of Operations Research, Springer, vol. 299(1), pages 567-592, April.
    21. Hussain, Inayat & Durand, Robert B. & Harris, Mark N., 2016. "Default resolution and access to fresh credit in an emerging market," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 256-274.
    22. Wojciech Starosta, 2020. "Modelling Recovery Rate for Incomplete Defaults Using Time Varying Predictors," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(2), pages 195-225, June.
    23. Nazemi, Abdolreza & Heidenreich, Konstantin & Fabozzi, Frank J., 2018. "Improving corporate bond recovery rate prediction using multi-factor support vector regressions," European Journal of Operational Research, Elsevier, vol. 271(2), pages 664-675.
    24. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
    25. Chen, Rongda & Zhou, Hanxian & Jin, Chenglu & Zheng, Wei, 2019. "Modeling of recovery rate for a given default by non-parametric method," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    26. Aneta Ptak-Chmielewska & Paweł Kopciuszewski, 2023. "Application of the Bayesian approach in loss given default modelling," Bank i Kredyt, Narodowy Bank Polski, vol. 54(6), pages 625-650.
    27. Girardi, Alessandro & Ventura, Marco & Margani, Patrizia, 2018. "An Indicator of Credit Crunch using Italian Business Surveys," MPRA Paper 88839, University Library of Munich, Germany.
    28. Altman, Edward I. & Kalotay, Egon A., 2014. "Ultimate recovery mixtures," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 116-129.
    29. Hartmann-Wendels, Thomas & Miller, Patrick & Töws, Eugen, 2014. "Loss given default for leasing: Parametric and nonparametric estimations," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 364-375.
    30. Christophe Croux & Julapa Jagtiani & Tarunsai Korivi & Milos Vulanovic, 2020. "Important Factors Determining Fintech Loan Default: Evidence from the LendingClub Consumer Platform," Working Papers 20-15, Federal Reserve Bank of Philadelphia.
    31. Gürtler, Marc & Hibbeln, Martin Thomas & Usselmann, Piet, 2018. "Exposure at default modeling – A theoretical and empirical assessment of estimation approaches and parameter choice," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 176-188.
    32. Seidler, Jakub & Konečný, Tomáš & Belyaeva, Aelita & Belyaev, Konstantin, 2017. "The time dimension of the links between loss given default and the macroeconomy," Working Paper Series 2037, European Central Bank.
    33. Han, Chulwoo & Jang, Youngmin, 2013. "Effects of debt collection practices on loss given default," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 21-31.
    34. Joao A. Bastos, 2013. "Ensemble predictions of recovery rates," CEMAPRE Working Papers 1301, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.
    35. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    36. Toshiro Masahiro & Tasaki Masao & Hikidera Yusuke & Hibiki Norio, 2019. "Estimating the Recovery Rates for Unsecured Loans to Small Sized Firms," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 13(2), pages 1-26, July.
    37. Salvatore D. Tomarchio & Antonio Punzo, 2019. "Modelling the loss given default distribution via a family of zero‐and‐one inflated mixture models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1247-1266, October.
    38. So Sohn & Yoon Kim, 2013. "Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking," Small Business Economics, Springer, vol. 41(4), pages 931-943, December.
    39. Qi, Min & Zhao, Xinlei, 2011. "Comparison of modeling methods for Loss Given Default," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2842-2855, November.
    40. Jobst, Rainer & Kellner, Ralf & Rösch, Daniel, 2020. "Bayesian loss given default estimation for European sovereign bonds," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1073-1091.
    41. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Enhancing two-stage modelling methodology for loss given default with support vector machines," European Journal of Operational Research, Elsevier, vol. 263(2), pages 679-689.
    42. Nazemi, Abdolreza & Rezazadeh, Hani & Fabozzi, Frank J. & Höchstötter, Markus, 2022. "Deep learning for modeling the collection rate for third-party buyers," International Journal of Forecasting, Elsevier, vol. 38(1), pages 240-252.
    43. Chen, Xiaowei & Wang, Gang & Zhang, Xiangting, 2019. "Modeling recovery rate for leveraged loans," Economic Modelling, Elsevier, vol. 81(C), pages 231-241.
    44. Loterman, Gert & Brown, Iain & Martens, David & Mues, Christophe & Baesens, Bart, 2012. "Benchmarking regression algorithms for loss given default modeling," International Journal of Forecasting, Elsevier, vol. 28(1), pages 161-170.
    45. TOBBACK, Ellen & MARTENS, David & VAN GESTEL, Tony & BAESENS, Bart, 2012. "Forecasting loss given default models: Impact of account characteristics and the macroeconomic state," Working Papers 2012019, University of Antwerp, Faculty of Business and Economics.
    46. Cheng, Dan & Cirillo, Pasquale, 2018. "A reinforced urn process modeling of recovery rates and recovery times," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 1-17.
    47. Thamayanthi Chellathurai, 2017. "Probability Density Of Recovery Rate Given Default Of A Firm’S Debt And Its Constituent Tranches," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-34, June.
    48. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank.
    49. Marc Gürtler & Marvin Zöllner, 2023. "Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 251-287, March.
    50. Diana Bonfim & Daniel Dias, 2011. "What Happens After Default? Stylized Facts on Access to Credit," Working Papers w201101, Banco de Portugal, Economics and Research Department.
    51. Nazemi, Abdolreza & Fatemi Pour, Farnoosh & Heidenreich, Konstantin & Fabozzi, Frank J., 2017. "Fuzzy decision fusion approach for loss-given-default modeling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 780-791.
    52. Miller, Patrick & Töws, Eugen, 2018. "Loss given default adjusted workout processes for leases," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 189-201.
    53. Betz, Jennifer & Kellner, Ralf & Rösch, Daniel, 2016. "What drives the time to resolution of defaulted bank loans?," Finance Research Letters, Elsevier, vol. 18(C), pages 7-31.
    54. Raffaella Calabrese, 2012. "Regression Model for Proportions with Probability Masses at Zero and One," Working Papers 201209, Geary Institute, University College Dublin.
    55. Hibbeln, Martin & Gürtler, Marc, 2011. "Pitfalls in modeling loss given default of bank loans," Working Papers IF35V1, Technische Universität Braunschweig, Institute of Finance.
    56. Justin A. Sirignano & Gerry Tsoukalas & Kay Giesecke, 2016. "Large-Scale Loan Portfolio Selection," Operations Research, INFORMS, vol. 64(6), pages 1239-1255, December.
    57. Ellen Tobback & David Martens & Tony Van Gestel & Bart Baesens, 2014. "Forecasting Loss Given Default models: impact of account characteristics and the macroeconomic state," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 376-392, March.
    58. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
    59. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond," Journal of Financial Stability, Elsevier, vol. 28(C), pages 1-15.
    60. Pesola, Jarmo, 2011. "Joint effect of financial fragility and macroeconomic shocks on bank loan losses: Evidence from Europe," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3134-3144, November.
    61. Raffaella Calabrese, 2012. "Estimating bank loans loss given default by generalized additive models," Working Papers 201224, Geary Institute, University College Dublin.
    62. Baker, Rose D. & McHale, Ian G., 2018. "Time-varying ratings for international football teams," European Journal of Operational Research, Elsevier, vol. 267(2), pages 659-666.
    63. Ellis Kofi, Akwaa-Sekyi & Portia, Bosompra, 2015. "Determinants of business loan default in Ghana," MPRA Paper 71961, University Library of Munich, Germany.
    64. Kaposty, Florian & Kriebel, Johannes & Löderbusch, Matthias, 2020. "Predicting loss given default in leasing: A closer look at models and variable selection," International Journal of Forecasting, Elsevier, vol. 36(2), pages 248-266.
    65. Betz, Jennifer & Kellner, Ralf & Rösch, Daniel, 2018. "Systematic Effects among Loss Given Defaults and their Implications on Downturn Estimation," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1113-1144.
    66. Pascal François, 2019. "The Determinants of Market-Implied Recovery Rates," Risks, MDPI, vol. 7(2), pages 1-15, May.
    67. Ruey-Ching Hwang & Huimin Chung & C. K. Chu, 2016. "A Two-Stage Probit Model for Predicting Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 50(3), pages 311-339, December.
    68. Aneta Ptak-Chmielewska & Paweł Kopciuszewski & Anna Matuszyk, 2023. "Application of the kNN-Based Method and Survival Approach in Estimating Loss Given Default for Unresolved Cases," Risks, MDPI, vol. 11(2), pages 1-14, February.
    69. Yurchenko, Yurii, 2019. "The impact of macroeconomic factors on collateral value within the framework of expected credit loss calculation," MPRA Paper 97135, University Library of Munich, Germany.
    70. Tanoue, Yuta & Kawada, Akihiro & Yamashita, Satoshi, 2017. "Forecasting loss given default of bank loans with multi-stage model," International Journal of Forecasting, Elsevier, vol. 33(2), pages 513-522.
    71. Hurlin, Christophe & Leymarie, Jérémy & Patin, Antoine, 2018. "Loss functions for Loss Given Default model comparison," European Journal of Operational Research, Elsevier, vol. 268(1), pages 348-360.
    72. Natalia Nehrebecka, 2019. "Bank loans recovery rate in commercial banks: A case study of non-financial corporations," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 37(1), pages 139-172.
    73. Ruey-Ching Hwang & Chih-Kang Chu & Kaizhi Yu, 2021. "Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(3), pages 143-172, June.
    74. Majid Bazarbash, 2019. "FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk," IMF Working Papers 2019/109, International Monetary Fund.
    75. Christoph Memmel & Angelika Sachs & Ingrid Stein, 2012. "Contagion in the Interbank Market with Stochastic Loss Given Default," International Journal of Central Banking, International Journal of Central Banking, vol. 8(3), pages 177-206, September.
    76. Antão, Paula & Lacerda, Ana, 2011. "Capital requirements under the credit risk-based framework," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1380-1390, June.
    77. Sopitpongstorn, Nithi & Silvapulle, Param & Gao, Jiti & Fenech, Jean-Pierre, 2021. "Local logit regression for loan recovery rate," Journal of Banking & Finance, Elsevier, vol. 126(C).
    78. Olson, Luke M. & Qi, Min & Zhang, Xiaofei & Zhao, Xinlei, 2021. "Machine learning loss given default for corporate debt," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 144-159.

  11. Bastos, Joao, 2007. "Credit scoring with boosted decision trees," MPRA Paper 8034, University Library of Munich, Germany.

    Cited by:

    1. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
    2. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    3. Zhang, Zhiwang & Gao, Guangxia & Shi, Yong, 2014. "Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors," European Journal of Operational Research, Elsevier, vol. 237(1), pages 335-348.
    4. Marco Locurcio & Francesco Tajani & Pierluigi Morano & Debora Anelli & Benedetto Manganelli, 2021. "Credit Risk Management of Property Investments through Multi-Criteria Indicators," Risks, MDPI, vol. 9(6), pages 1-23, June.

Articles

  1. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    See citations under working paper version above.
  2. João A. Bastos, 2019. "Forecasting the capacity of mobile networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(2), pages 231-242, October.
    See citations under working paper version above.
  3. João A. Bastos & Joaquim J. S. Ramalho, 2016. "Nonparametric Models Of Financial Leverage Decisions," Bulletin of Economic Research, Wiley Blackwell, vol. 68(4), pages 348-366, October.
    See citations under working paper version above.
  4. João A. Bastos & Jorge Caiado, 2014. "Clustering financial time series with variance ratio statistics," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2121-2133, December.
    See citations under working paper version above.
  5. João Bastos, 2014. "Ensemble Predictions of Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 46(2), pages 177-193, October.
    See citations under working paper version above.
  6. Bastos, João A. & Caiado, Jorge, 2011. "Recurrence quantification analysis of global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1315-1325.
    See citations under working paper version above.
  7. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    See citations under working paper version above.Sorry, no citations of articles recorded.

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 12 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-BAN: Banking (4) 2009-05-16 2010-07-31 2013-03-09 2021-02-22
  2. NEP-CMP: Computational Economics (4) 2008-04-15 2010-07-31 2021-08-09 2024-02-26
  3. NEP-FMK: Financial Markets (4) 2009-05-16 2009-10-03 2010-07-31 2010-12-18
  4. NEP-FOR: Forecasting (4) 2009-05-16 2010-07-31 2013-03-09 2019-03-25
  5. NEP-RMG: Risk Management (4) 2008-04-15 2009-05-16 2010-07-31 2021-02-22
  6. NEP-BEC: Business Economics (2) 2009-05-16 2010-10-02
  7. NEP-CFN: Corporate Finance (1) 2008-04-15
  8. NEP-ETS: Econometric Time Series (1) 2021-08-09
  9. NEP-PAY: Payment Systems and Financial Technology (1) 2019-03-25

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