Mirko Moscatelli

Personal Details

First Name:	Mirko
Middle Name:
Last Name:	Moscatelli
Suffix:
RePEc Short-ID:	pmo1539
	[This author has chosen not to make the email address public]

Affiliation

Banca d'Italia

Roma, Italy
http://www.bancaditalia.it/
RePEc:edi:bdigvit (more details at EDIRC)

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. Giuseppe Cascarino & Mirko Moscatelli & Fabio Parlapiano, 2022. "Explainable Artificial Intelligence: interpreting default forecasting models based on Machine Learning," Questioni di Economia e Finanza (Occasional Papers) 674, Bank of Italy, Economic Research and International Relations Area.

   Cited by:

   1. Raffaele De Marchi & Alessandro Moro, 2023. "Forecasting fiscal crises in emerging markets and low-income countries with machine learning models," Temi di discussione (Economic working papers) 1405, Bank of Italy, Economic Research and International Relations Area.
      • Raffaele Marchi & Alessandro Moro, 2024. "Forecasting Fiscal Crises in Emerging Markets and Low-Income Countries with Machine Learning Models," Open Economies Review, Springer, vol. 35(1), pages 189-213, February.
   2. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
   3. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
   4. Rosaria Cerrone, 2023. "Are Artificial Intelligence and Machine Learning Shaping a New Risk Management Approach?," International Business Research, Canadian Center of Science and Education, vol. 16(12), pages 1-82, December.
   5. Jorge Tejero, 2022. "Unwrapping black box models A case study in credit risk," Revista de Estabilidad Financiera, Banco de España, issue Otoño.
   6. Jorge Tejero, 2022. "Unwrapping black box models A case study in credit risk," Financial Stability Review, Banco de España, issue Autumn.
   7. Giuseppe Cascarino & Federica Ciocchetta & Stefano Pietrosanti & Ivan Quaglia, 2025. "Forecasting corporate default probabilities: a local logit approach for scenario analysis," Questioni di Economia e Finanza (Occasional Papers) 909, Bank of Italy, Economic Research and International Relations Area.
   8. Antonietta di Salvatore & Mirko Moscatelli, 2024. "Improving survey information on household debt using granular credit databases," Questioni di Economia e Finanza (Occasional Papers) 839, Bank of Italy, Economic Research and International Relations Area.

2. Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019. "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers) 1256, Bank of Italy, Economic Research and International Relations Area.

   Cited by:

   1. Nicola Branzoli & Ilaria Supino, 2020. "FinTech credit: a critical review of empirical research," Questioni di Economia e Finanza (Occasional Papers) 549, Bank of Italy, Economic Research and International Relations Area.
   2. Agnese Carella & Federica Ciocchetta & Valentina Michelangeli & Federico Maria Signoretti, 2020. "What can we learn about mortgage supply from online data?," Questioni di Economia e Finanza (Occasional Papers) 583, Bank of Italy, Economic Research and International Relations Area.
   3. Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2021. "Learning from revisions: a tool for detecting potential errors in banks' balance sheet statistical reporting," Questioni di Economia e Finanza (Occasional Papers) 611, Bank of Italy, Economic Research and International Relations Area.
   4. Giuseppe Cascarino & Mirko Moscatelli & Fabio Parlapiano, 2022. "Explainable Artificial Intelligence: interpreting default forecasting models based on Machine Learning," Questioni di Economia e Finanza (Occasional Papers) 674, Bank of Italy, Economic Research and International Relations Area.
   5. Fantazzini, Dean & Calabrese, Raffaella, 2021. "Crypto-exchanges and Credit Risk: Modelling and Forecasting the Probability of Closure," MPRA Paper 110391, University Library of Munich, Germany.
      • Dean Fantazzini & Raffaella Calabrese, 2021. "Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure," JRFM, MDPI, vol. 14(11), pages 1-23, October.
   6. Henri Fraisse & Matthias Laporte, 2021. "Return on Investment on AI: The Case of Capital Requirement," Working papers 809, Banque de France.
   7. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
   8. Golbayani, Parisa & Florescu, Ionuţ & Chatterjee, Rupak, 2020. "A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
   9. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).
   10. Salvatore Cardillo & Raffaele Gallo & Francesco Guarino, 2021. "Main challenges and prospects for the European banking sector: a critical review of the ongoing debate," Questioni di Economia e Finanza (Occasional Papers) 634, Bank of Italy, Economic Research and International Relations Area.
   11. Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020. "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers 01/2020, IMT School for Advanced Studies Lucca, revised Jun 2020.
   12. Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
   13. Falco J. Bargagli-Stoffi & Fabio Incerti & Massimo Riccaboni & Armando Rungi, 2023. "Machine Learning for Zombie Hunting: Predicting Distress from Firms' Accounts and Missing Values," Papers 2306.08165, arXiv.org.
   14. Flavio Bazzana & Marco Bee & Ahmed Almustfa Hussin Adam Khatir, 2024. "Machine learning techniques for default prediction: an application to small Italian companies," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-23, February.
   15. Bitetto, Alessandro & Cerchiello, Paola & Filomeni, Stefano & Tanda, Alessandra & Tarantino, Barbara, 2023. "Machine learning and credit risk: Empirical evidence from small- and mid-sized businesses," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
   16. Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
   17. Parisa Golbayani & Ionuc{t} Florescu & Rupak Chatterjee, 2020. "A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees," Papers 2007.06617, arXiv.org.
   18. Giuseppe Orlando & Roberta Pelosi, 2020. "Non-Performing Loans for Italian Companies: When Time Matters. An Empirical Research on Estimating Probability to Default and Loss Given Default," IJFS, MDPI, vol. 8(4), pages 1-22, November.
   19. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
   20. Alessandro Bitetto & Paola Cerchiello & Stefano Filomeni & Alessandra Tanda & Barbara Tarantino, 2021. "Machine Learning and Credit Risk: Empirical Evidence from SMEs," DEM Working Papers Series 201, University of Pavia, Department of Economics and Management.
   21. Branka Hadji Misheva & Joerg Osterrieder & Ali Hirsa & Onkar Kulkarni & Stephen Fung Lin, 2021. "Explainable AI in Credit Risk Management," Papers 2103.00949, arXiv.org.

3. Matteo Accornero & Mirko Moscatelli, 2018. "Listening to the buzz: social media sentiment and retail depositors' trust," Temi di discussione (Economic working papers) 1165, Bank of Italy, Economic Research and International Relations Area.

   Cited by:

   1. Gomez-Biscarri, Javier & López-Espinosa, Germán & Mesa-Toro, Andrés, 2021. "The risk implications of the business loan activity in credit unions," Journal of Financial Stability, Elsevier, vol. 56(C).
   2. Rho Caterina & Fernández Raúl & Palma Brenda, 2021. "A Sentiment-based Risk Indicator for the Mexican Financial Sector," Working Papers 2021-04, Banco de México.
      • Fernandez, Raul & Palma Guizar, Brenda & Rho, Caterina, 2021. "A sentiment-based risk indicator for the Mexican financial sector," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(3).
   3. Mrs. Jana Bricco & Ms. TengTeng Xu, 2019. "Interconnectedness and Contagion Analysis: A Practical Framework," IMF Working Papers 2019/220, International Monetary Fund.

Articles

1. Bonaccorsi di Patti, Emilia & Moscatelli, Mirko & Pietrosanti, Stefano, 2023. "The impact of bank regulation on the cost of credit: Evidence from a discontinuity in capital requirements," Journal of Financial Intermediation, Elsevier, vol. 55(C).

   Cited by:

   1. Cristina Jude & Grégory Levieuge, 2024. "Doubling Down: The Synergy of CCyB Release and Monetary Policy Easing," Working papers 961, Banque de France.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 5 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 (3) 2018-03-12 2022-04-04 2024-04-15. Author is listed
2. NEP-BIG: Big Data (2) 2020-01-13 2022-04-04. Author is listed
3. NEP-CMP: Computational Economics (2) 2020-01-13 2022-04-04. Author is listed
4. NEP-RMG: Risk Management (2) 2020-01-13 2022-04-04. Author is listed
5. NEP-FDG: Financial Development and Growth (1) 2022-08-15
6. NEP-FLE: Financial Literacy and Education (1) 2024-04-15
7. NEP-FOR: Forecasting (1) 2022-04-04
8. NEP-MAC: Macroeconomics (1) 2018-03-12
9. NEP-ORE: Operations Research (1) 2020-01-13
10. NEP-PAY: Payment Systems and Financial Technology (1) 2022-08-15
11. NEP-SBM: Small Business Management (1) 2022-08-15

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Mirko Moscatelli should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

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