Impacto do surto de COVID-19 nos ratings de crédito: aplicação da abordagem through-the-cycle Outros Idiomas

ID:
78455
Resumo:
O objetivo deste estudo foi analisar como a crise da COVID-19 afetou os determinantes e a previsibilidade do rating de crédito doméstico emitida pela Fitch Ratings na Argentina. Além disso, pretende-se avaliar os efeitos das agências de classificação de risco de crédito usando o método through-the-cycle (ao longo do ciclo). Dada a natureza subjetiva da categorização dos ratings de crédito, os pesquisadores desenvolveram modelos para explicar e prever esses ratings. Essa subjetividade é significativa durante eventos econômicos. Portanto, é importante investigar se os fatores que determinam e preveem os ratings de crédito permaneceram consistentes antes e durante a crise da COVID-19. Este artigo contribui significativamente para a compreensão de como a aplicação do método through-the-cycle afeta os determinantes e a previsibilidade dos ratings de crédito em crises econômicas. A aplicação do método through-the-cycle pelas agências de classificação de risco de crédito como um critério durante a crise da COVID-19 resultou em uma quebra da correlação usual entre os determinantes e os ratings de crédito. Entender se as variáveis são componentes permanentes ou transitórios é fundamental para que os investidores e tomadores de empréstimos antecipem as mudanças nos ratings de crédito durante as recessões econômicas. As variáveis dependentes são as categorias de rating de crédito doméstico de longo prazo. As variáveis independentes são derivadas da metodologia de rating de crédito da Fitch Ratings e da literatura, que inclui variáveis quantitativas e qualitativas. Os métodos estatísticos utilizados são a regressão logística ordinal, a regressão logística ordinal generalizada e as máquinas de vetores de suporte. A crise da COVID-19 foi considerada um evento transitório devido à aplicação da abordagem through-the-cycle pelas agências de classificação de risco de crédito. Durante a pandemia, os determinantes específicos dos ratings de crédito não são considerados devido à sua natureza transitória. O estudo identifica o índice de cobertura de juros e a posição competitiva como componentes transitórios. Essa abordagem levou a uma menor previsibilidade, mas a um rating de crédito mais estável.
Citação ABNT:
TERRENO, D. D.; DONADILLE, M. E. Impacto do surto de COVID-19 nos ratings de crédito: aplicação da abordagem through-the-cycle. Revista Contabilidade & Finanças, v. 35, n. 95, p. 0-0, 2024.
Citação APA:
Terreno, D. D., & Donadille, M. E. (2024). Impacto do surto de COVID-19 nos ratings de crédito: aplicação da abordagem through-the-cycle. Revista Contabilidade & Finanças, 35(95), 0-0.
Link Permanente:
https://www.spell.org.br/documentos/ver/78455/impacto-do-surto-de-covid-19-nos-ratings-de-credito--aplicacao-da-abordagem-through-the-cycle/i/pt-br
Tipo de documento:
Artigo
Idioma:
Português
Referências:
Altman, E. I., & Rijken, H. A. (2004). How rating agencies achieve rating stability. Journal of Banking & Finance, 28(11), 2679-2714. https://doi.org/10.1016/j.jbankfin.2004.06.006

Altman, E. I., Sieradzki, R., & Thlon, M. (2022). Assessing Corporate Credit Risk Transitions and Bankruptcy Prediction on SMEs as a result of the COVID-19 Pandemic [Working Paper]. Social Science Research Network, Rochester, NY. Recuperado de http://dx.doi.org/10.2139/ssrn.4048333

Amato, J. D., & Furfine, C. H. (2004). Are credit ratings procyclical? Journal of Banking & Finance, 28(11), 2641-2677. https://doi.org/10.1016/j.jbankfin.2004.06.005

Aromí, D., Bermúdez, C., & Dabús, C. (2022). Incertidumbre y crecimiento económico: enseñanzas de América Latina. Revista CEPAL. Recuperado de https://repositorio.cepal.org/handle/11362/48085

Bernardi, A., Bragoli, D., Fedreghini, D., Ganugi, T., & Marseguerra, G. (2021). COVID-19 and firms’ financial health in Brescia: a simulation with Logistic regression and neural networks. National Accounting Review, 3(3), 293-309. https://doi.org/10.3934/NAR.2021015

Blume, M., Lim, F., & MacKinlay A. (1998). The declining quality of US corporate debt: Myth or reality? Journal of Finance, 53, 1389–1413. https://doi.org/10.1111/0022-1082.00057

Cepal, N.U. (2020). Balance preliminar de las economías de América Latina y el Caribe 2020. Santiago: CEPAL. Recuperado de https://repositorio.cepal.org/bitstream/handle/11362/46501/4/BP2020_Argentina_es.pdf

Chodnicka-Jaworska, P. (2022). Impact of COVID-19 on European banks’ credit ratings. Economic Research-Ekonomska Istraživanja, 1-20. https://doi.org/10.1080/1331677X.2022.2153717

Damasceno, D. L., Artes, R., & Minardi, A. M. A. F. (2008). Determinação de rating de crédito de empresas brasileiras com a utilização de índices contábeis. Revista de Administração-RAUSP, 43(4), 344-355.

Drobetz, W., & Heller, S. (2014). What factors drive corporate credit ratings? Evidence from German SMEs and large corporates [Working Paper]. Social Science Research Network, Rochester, NY. Recuperado de http://dx.doi.org/10.2139/ssrn.239237

Dubinova, A., Lucas, A., & Telg, S. (2021). COVID-19, Credit Risk and Macro Fundamentals. Tinbergen Institute Discussion Paper 2021-059/III. Social Science Research Network, Rochester, NY. Recuperado de http://dx.doi.org/10.2139/ssrn.3875628

Du, Y., & Suo, W. (2007). Assessing credit quality from the equity market: can a structural approach forecast credit ratings? Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l’Administration, 24(3), 212-228. https://doi.org/10.1002/cjas.27

Elton, E. J., Gruber, M. J., & Blake, C. R. (1995). Fundamental economic variables, expected returns, and bond fund performance. The Journal of Finance, 50(4), 1229-1256. https://doi.org/10.1111/j.1540-6261.1995.tb04056.x

Feki, A., & Khoufi, W. (2015). The determinants of issuers’ long term credit ratings: American S&P500 index. International Journal of Accounting and Economics Studies, 3(1), 78–85. https://doi.org/10.14419/ijaes.v3i1.4631

Fernandes, N. (2020), Economic effects of coronavirus outbreak (COVID-19) on the world economy [Working Paper]. Social Science Research Network, Rochester, NY. Recuperado de https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3557504

Figlioli, B., Antonio, R. M., & Lima, F. G. (2019). Stock Price Synchronicity and Current and Potential Credit Ratings. International Journal of Economics and Finance, 11(10), 1-16. https://doi.org/10.5539/ijef.v11n10p1

FixScr (2014). Argentina: Metodología de calificación de riesgos. Recuperado de https://www.fixscr.com/site/download?file=Manual+Empresas_Jun+2014.pdf

FixScr (2020). Corporates Argentina: Vulnerabilidad ante COVID-19 Revisión Sectorial y por Emisor. Recuperado de https://www.fixscr.com/site/download?file=oJEvh8OT6lmrlTUOQMz99E50l29BjBy9.pdf

FixScr (2021). Calificaciones. Recuperado de https://www.fixscr.com/calificaciones

Freitas, A. D. P. N., & Minardi, A. M. A. F. (2013). The impact of credit rating changes in Latin American stock markets. BAR-Brazilian Administration Review, 10, 439-461. https://doi.org/10.1590/S1807-76922013000400005

Gonzalez, F., Haas, F., Persson, M., Toledo, L., Violi, R., Wieland, M., & Zins, C. (2004). Market dynamics associated with credit ratings: a literature review. ECB Occasional Paper Nº 16. Social Science Research Network, Rochester, NY. Recuperado de http://dx.doi.org/10.2139/ssrn.752065

Gormsen, N. J., & Koijen, R. S. (2020). Coronavirus: Impact on stock prices and growth expectations. The Review of Asset Pricing Studies, 10(4), 574–597. https://doi.org/10.1093/rapstu/raaa013

Graham, J. R., & Harvey, C. R. (2001). The theory and practice of corporate finance: Evidence from the field. Journal of Financial Economics, 60(2-3), 187–243. https://doi.org/10.1016/S0304-405X(01)00044-7

Gray, S., Mirkovic, A., & Ragunathan, V. (2006). The determinants of credit ratings: Australian evidence. Australian Journal of Management, 31(2), 333–354. https://doi.org/10.1177/031289620603100208

Hung, K., Cheng, H. W., Chen, S. S., & Huang, Y. C. (2013). Factors that affect credit rating: An application of ordered probit models. Romanian Journal of Economic Forecasting, 16(4), 94-108.

Hu, S., & Zhang, Y. (2021). COVID-19 pandemic and firm performance: Cross-country evidence. International Review of Economics & Finance, 74, 365–372. https://doi.org/10.1016/j.iref.2021.03.016

International Monetary Fund (IMF) (2021). World Economic Outlook: Managing Divergent Recoveries. Washington D.C., April. Recuperado de https://www.imf.org/-/media/Files/Publications/WEO/2021/April/English/text.ashx

Jiang, X., & Packer, F. (2017). Credit ratings of domestic and global agencies: What drives the differences in China and how are they priced? BIS Working Paper No. 648. Social Science Research Network, Rochester, NY. Recuperado de https://ssrn.com/abstract=2996280

Johnston, R., Jones, K., & Manley, D. (2018). Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour. Quality & Quantity, 52, 1957-1976. https://doi.org/10.1007/s11135-017-0584-6

Kang, Q., & Liu, Q. (2007). Credit rating changes and CEO incentives. Social Science Research Network, Rochester, NY. Recuperado de https://ssrn.com/abstract=2996280

Krichene, A. F., & Khoufi, W. (2016). On the Nonlinearity of the Financial Ratios-Credit Ratings Relationship. Applied Finance and Accounting, 2(2), 65-70. https://doi.org/10.11114/afa.v2i2.1604

Lehmann, B. (2003). Is it worth the while? The relevance of qualitative information in credit rating [Working Paper]. Social Science Research Network, Rochester, NY. Recuperado de http://dx.doi.org/10.2139/ssrn.410186

Liberti, J. M., & Petersen, M. A. (2019). Information: Hard and soft. Review of Corporate Finance Studies, 8(1), 1–41. https://doi.org/10.1093/rcfs/cfy009

Liu, X. (2009). Ordinal regression analysis: Fitting the proportional odds model using Stata, SAS and SPSS. Journal of Modern Applied Statistical Methods, 8(2), 632-645. https://doi.org/10.22237/jmasm/1257035340

Löffler, G. (2004). An anatomy of rating through the cycle. Journal of Banking & Finance, 28(3), 695–720. https://doi.org/10.1016/S0378-4266(03)00041-4

Louzada, F., Ara, A., & Fernandes, G. B. (2016). Classification methods applied to credit scoring: Systematic review and overall comparison. Surveys in Operations Research and Management Science, 21(2), 117-134. https://doi.org/10.1016/j.sorms.2016.10.001

Murcia, F. C. S., Murcia, F. D.-R., Rover, S., & Borba, J. A. (2014). The determinants of credit rating: Brazilian evidence. BAR-Brazilian Administration Review, 11(2), 188-209. https://doi.org/10.1590/S1807-76922014000200005

Novotná, M. (2013). Multivariate Analysis of Financial and Market-Based Variables for Bond Rating Prediction. Journal of Economic Computation and Economic Cybernetics Studies and Research, 47(2), 67–83.

Phan, D. H. B., & Narayan, P. K. (2020). Country responses and the reaction of the stock market to COVID-19—A preliminary exposition. Emerging Markets Finance and Trade, 56(10), 2138-2150. https://doi.org/10.1080/1540496X.2020.1784719

Sharif, A., Aloui, C., & Yarovaya, L. (2020). COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach. International Review of Financial Analysis, 70, 1-9. https://doi.org/10.1016/j.irfa.2020.101496

Shin, K. S., & Han, I. (2001). A case-based approach using inductive indexing for corporate bond rating. Decision Support Systems, 32(1), 41-52. https://doi.org/10.1016/S0167-9236(01)00099-9

Soares, G. D. O. G., Coutinho, E. S., & Camargos, M. A. (2012). Determinantes do rating de crédito de companhias brasileiras. Contabilidade Vista & Revista, 23(3), 109-143. Recuperado de https://www.redalyc.org/pdf/1970/197026238005.pdf

Tanthanongsakkun, S., & Treepongkaruna, S. (2008). Explaining Credit Ratings of Australian Companies—An Application of the Merton Model. Australian Journal of Management, 33(2), 261-275. https://doi.org/10.1177/031289620803300203

Wahlen, J. M., Baginski, S. P., & Bradshaw, M. (2014). Financial reporting, financial statement analysis and valuation. Cengage Learning.

Williams, R. (2006). Generalized ordered logit/partial proportional odds models for ordinal dependent variables. The Stata Journal, 6(1), 58–82. https://doi.org/10.1177/1536867X0600600104