Для цитирования:
Sakovich, M. (2024). Macroprudential Policies in the Light of the Development of Information Technologies: A Synthesis on the Effective Early Warning Signals. AlterEconomics, 21(3), 512–526. https://doi.org/10.31063/AlterEconomics/2024.21-3.5
Аннотация:
In response to recent recurrent crises, innovative macroprudential policies (MaPs) have been framed and implemented to address the weaknesses of market-led microprudential mechanisms and enhance the stability of financial systems. However, the effectiveness of the tools used to implement MaPs remains a critical research question. Early warning signals (EWS) serve as indicators of potential future crises. This paper explores approaches for identifying EWS to optimize the impact of MaPs, particularly in light of advances in information technology. It provides a comprehensive review of academic studies that identify effective EWS by analyzing numerical data through econometric and machine learning (ML) methods or by extracting economic insights from text using deep learning (DL) techniques — innovative methods for financial supervision and regulation. The findings, considering current regulatory practices, highlight the benefits of ML-based approaches for processing large sets of numerical data and the growing potential of text-based methods for assessing economic expectations.
Marina Sakovich — PhD Candidate at the Center of Research in Economics (CREG), Université Grenoble Alpes (Saint-Martin-d’Hères, France; e-mail: marina.sakovich@gmail.com).
Adrian, T., Natalucci, F.M., & Qureshi, M.S. (2022). Macro-Financial Stability in the COVID-19 Crisis: Some Reflections. IMF Working Paper, (2022/251).
Alessi, L., & Detken, C. (2018). Identifying excessive credit growth and leverage. Journal of Financial Stability, 35, 215–225. https://doi.org/10.1016/j.jfs.2017.06.005
Barbieri, C., Couaillier, C., Perales, C., & Rodriguez D’Acri, C. (2022). Informing macroprudential policy choices using credit supply and demand decompositions. ECB Working Paper Series, (2702).
Baret, K., Barbier-Gauchard, A., & Papadimitriou, T. (2023). Forecasting stability and growth pact compliance using machine learning. The World Economy, 47 (1), 188–216. https://doi.org/10.1111/twec.13518
Belkhir, M., Naceur, S. B., Candelon, B., & Wijnandts, J-C. (2022). Macroprudential Regulation and Sector-Specific Default Risk. IMF Working Paper, 2022 (141). http://dx.doi.org/10.5089/9798400215421.001
Bluwstein, K., Buckmann, M., Joseph, A., Kapadi, S., & Şimşek, Ö. (2023). Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach. Journal of International Economics, 145, 103773. https://doi.org/10.1016/j.jinteco.2023.103773
Di Patti, E. B., Calabresi, F., De Varti, B., Federico, F., Affinito, M., Antolini, M., Lorizzo, F., Marchetti, S., Masiani, I., Moscatelli, M., Privitera, F., & Rinna, G. (2022). Artificial intelligence in credit scoring: an analysis of some experiences in the Italian financial system (No. 721). Bank of Italy, Economic Research and International Relations Area.
Borio, C., Drehmann, M., & Xia, D. (2019). Predicting recessions: financial cycle versus term spread. BIS Working Papers, (818).
Borio, C., Shim, I., & Shin H. S. (2022). Macro-financial stability frameworks: experience and challenges. BIS Working Papers, (1057).
Brezigar-Masten, A., Masten, I., & Volk, M. (2021). Modeling credit risk with a Tobit model of days past due. Journal of Banking and Finance, 122, 105984. https://doi.org/10.1016/j.jbankfin.2020.105984
Casabianca, E. J., Catalanoa, M., Forni, L., Giarda, E., & Passeri, S. (2019). An Early Warning System for banking crises: From regression-based analysis to machine learning techniques. Marco Fanno Working Papers — 235.
Chen, M. S., & Svirydzenka, K. (2021). Financial Cycles — Early Warning Indicators of Banking Crises? IMF Working Paper WP/21/116.
Daníelsson, J., Macrae, R., & Uthemann, A. (2022). Artificial intelligence and systemic risk. Journal of Banking and Finance, 140, 106290. https://doi.org/10.1016/j.jbankfin.2021.106290
Eberhardt, M., & Presbitero, A. (2018). Commodity Price Movements and Banking Crises. IMF Working Paper WP/18/153.
European systemic risk board (2014). Recommendation of the ESRB on Guidance for Setting Countercyclical Buffer Rates.
European central bank (2023). Financial Stability Review, November 2023.
Fraisse, H., & Laporte, M. (2022). Return on investment on artificial intelligence: The case of bank capital requirement. Journal of Banking and Finance, 138, 106401. https://doi.org/10.1016/j.jbankfin.2022.106401
Fulop, A., & Kocsis, Z. (2023). News indices on country fundamentals. Journal of Banking and Finance, 154, 106951. https://doi.org/10.1016/j.jbankfin.2023.106951
Gu, C., & Kurov, A. (2020). Informational role of social media: Evidence from Twitter sentiment. Journal of Banking and Finance, 121, 105969. https://doi.org/10.1016/j.jbankfin.2020.105969
Kellner, R., Nagl, M., & Rösch, D. (2022). Opening the black box — Quantile neural networks for loss given default prediction. Journal of Banking and Finance, 134, 106334. https://doi.org/10.1016/j.jbankfin.2021.106334
Krivorotov, G. (2023). Machine learning-based profit modeling for credit card underwriting — implications for credit risk. Journal of Banking and Finance, 149, 106785. https://doi.org/10.1016/j.jbankfin.2023.106785
Laeven, L., Maddaloni, A., & Mendicino, C. (2022). Monetary and macroprudential policy effectiveness and spillovers. SUERF Policy Brief No 484, December 2022
Laeven, L., Maddaloni, A., & Mendicino, C. (2022). Monetary policy, macroprudential policy and financial stability. ECB Discussion Papers. No 2647 / February 2022.
Li, X., Shang, W., Wang, S. (2019). Text-based crude oil price forecasting: A deep learning approach. International Journal of Forecasting, 35 (4), 1548–1560. https://doi.org/10.1016/j.ijforecast.2018.07.006
Liu, R., Pun, C. S. (2022). Machine-Learning-enhanced systemic risk measure: A Two-Step supervised learning approach. Journal of Banking and Finance, 136, 106416. https://doi.org/10.1016/j.jbankfin.2022.106416
Schmitz, S. W., Posch, M., Strobl, P. (2022). The EU macroprudential review should prioritize removing regulatory overlaps and increasing the flexibility of the CCyB. SUERF Policy Note, (293).
Toporowski, J. (2005). Theories of Financial Disturbance: an examination of critical theories of finance from Adam Smith to the present day. Edward Elgar Publishing.
Vrontos, S. D., Galakis, J., & Vrontos, I. D. (2021). Modeling and predicting U.S. recessions using machine learning techniques. International Journal of Forecasting, 37 (2), 647–671. https://doi.org/10.1016/j.ijforecast.2020.08.005
