Sobre a sincronização de índices financeiros bancários – uma abordagem baseada em wavelets

Autores

DOI:

https://doi.org/10.1590/1980-53575234wpc

Palavras-chave:

Crise da dívida soberana, Blocos comerciais, Contágio bancário

Resumo

Nós acrescentamos à discussão sobre a transmissão dos ciclos de negócios, modelando a sincronização dos ciclos dos índices do setor bancário mundial, levando em consideração o comportamento variante no tempo e frequência específica das variáveis. Com base na coerência múltipla, coerência parcial, diferença de fase parcial e ganho parcial, encontramos regiões de coerência forte e significativa entre os parceiros do NAFTA e no núcleo europeu: França, Alemanha e Reino Unido. Em relação a esses blocos comerciais, também encontramos forte desempenho no período 2010 2012 em todas as frequências, período caracterizado pela crise da dívida soberana em alguns países europeu.

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Biografia do Autor

  • Wilson Silva Oliveira, Banco do Brasil

    Gerente

  • Paulo Rogério Faustino Matos, Universidade Federal do Ceará

    Professor Associado

  • Cristiano da Costa da Silva, Universidade Federal do Ceará.Centro de Ciências Agrárias

    Professor Adjunto

Referências

Aguiar-Conraria, L., Soares, M., 2010. The continuous wavelet transform: a primer. NIPE-WP 23/2010.

Aguiar-Conraria, L., Soares, M., 2011. Business cycle synchronization and the Euro: A wavelet analysis. Journal of Macroeconomics, 33, 477–489.’

Aguiar-Conraria, L., Soares, M., 2014. The continuous wavelet transform: moving beyond uni-and bivariate analysis. Journal of Econonomic Survey, 28, 344–375.

Aguiar-Conraria, L., Martins, M, Soares, M., 2018. Estimating the Taylor rule in the time-frequency domain. Journal of Macroeconomics, 57, 122–137.

Aharony, J., Swary, I., 1983. Contagion Effects of Bank Failures: Evidence from Capital Markets. The Journal of Business, 56, 305–322.

Akhtaruzzaman, M., Boubaker, S., & Sensoy, A. (2021). Financial contagion during COVID–19 crisis. Finance Research Letters, 38, 101604.

Anderson, T., Darling, D., 1952. Asymptotic theory of certain goodness-of-fit criteria based on stochastic processes. Annals of Mathematical Statistics, 23, 193–212.

Beveridge, S., Nelson, C., 1981. A new approach to decomposition of economic Time series into permanent and transitory Components with particular attention to Measurement of the business cycle. Journal of Monetary Economics, 7, 151–174.

Bordo, M., Eichengreen, B., Klingebiel, D., Martinez-Peria, M.S., 2001. Is the crisis problem growing more severe? Economic Policy, 16, 51–82.

Bühler, W., & Prokopczuk, M., 2010. Systemic risk: Is the banking sector special?. Available at SSRN 1612683.

Dickey, D., Wayne, F., 1979. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057–1072.

Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of forecasting, 28(1), 57-66.

Dimic, N., Kiviaho, J. Piljak, V., Aijo, J., 2016. Impact of financial market uncertainty and macroeconomic factors on stock–bond correlation in emerging markets. Research in International Business and Finance, 36, 41–51.

Dungey, M., Gajurel, D., 2015. Contagion and banking crisis – International evidence for 2007–2009. Journal of Banking & Finance, 60, 271–283.

Engle, R., 1982. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50, 987–1007.

Farge, M. (1992). Wavelet transforms and their applications to turbulence. Annual review of fluid mechanics, 24(1), 395-458.

Gamba-Santamaria, S., Gomez-Gonzalez, J. E., Hurtado-Guarin, J. L., & Melo-Velandia, L. F. (2017). Stock market volatility spillovers: Evidence for Latin America. Finance Research Letters, 20, 207-216.

Gençay, R., Selçuk, F., & Whitcher, B. (2001). Differentiating intraday seasonalities through wavelet multi-scaling. Physica A: Statistical Mechanics and its Applications, 289(3-4), 543-556.

Goupillaud, P., Grossman, A., Morlet, J., 1984. Cycle-octave and related transforms in seismic signal analysis. Geoexploration 23, 85–102.

Grossmann, A., Morlet, J., 1984. Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM Journal on Mathematical Analysis 15, 723–736.

Hung, N. T. (2019). Spillover effects between stock prices and exchange rates for the central and eastern European countries. Global Business Review, 0972150919869772.

In, F. & Kim, S. (2013). An introduction to wavelet theory in finance: a wavelet multiscale approach. World scientific.

Jarque, C., Bera, A., 1981. Efficient tests f or normality, homoscedasticity and serial independence of regression residuals: Monte Carlo evidence. Economics Letters, 7, 313–318.

Kaminsky, G. L., & Reinhart, C. M. (2000). On crises, contagion, and confusion. Journal of international Economics, 51(1), 145-168.

Kaminsky, G. L., & Reinhart, C. M. (2002). Financial markets in times of stress. Journal of Development Economics, 69(2), 451-470.

Kaufman, G. G. (1992). Bank contagion: theory and evidence (No. 92-13). Federal Reserve Bank of Chicago.

Jung, R. C., & Maderitsch, R. (2014). Structural breaks in volatility spillovers between international financial markets: Contagion or mere interdependence? Journal of Banking & Finance, 47, 331-342.

Laeven, L., Valencia, F., 2013. Systemic banking crises database. IMF Economic Review, 61, 225–270.

Lilly, J. M., & Olhede, S. C. (2009). Bivariate instantaneous frequency and bandwidth. IEEE Transactions on Signal Processing, 58(2), 591-603.

Lin, F., Yang, S., Marsh, T., Chen, Y., 2018. Stock and bond return relations and stock market uncertainty: Evidence from wavelet analysis. International Review of Economics and Finance, 55, 285–294.

Loh, L., 2013. Co-movement of Asia-Pacific with European and US stock market returns: A cross-time-frequency analysis. Research in International Business and Finance, 29, 1–13.

Matos, P., Benegas., Costa, H., 2019. Chaos in the world banking system? Working paper CAEN/UFC.

Matos, P., Costa, A., da Silva, C. 2021. On the Risk-based Contagion of G7 Banking System and the COVID-19 Pandemic. Global Business Review, online version, 1 – 21.

Matos, P., Oquendo, R., Trompieri, N., 2016. Integration and contagion of BRIC financial markets. Journal of Applied Economics and Business, 4, 23–48.

Mandler, M., Scharnagl, M. 2019. Financial cycles across G7 economies: A view from wavelet analysis, Deutsche Bundesbank Discussion Paper 22/2019.

Meltzer, A., 1967. Major issues in the regulation of financial institutions. Journal of Political Economy, 75, 482-501.

Pavlova, A., & Rigobon, R. (2008). The role of portfolio constraints in the international propagation of shocks. The Review of Economic Studies, 75(4), 1215-1256.

OECD, 2012. Financial Contagion in the Era of Globalised Banking? OECD Economics Department Policy Notes, No. 14, June.

Rajwani, S., & Kumar, D. (2016). Asymmetric dynamic conditional correlation approach to financial contagion: a study of Asian markets. Global Business Review, 17, 1339-1356.

Rajwani, S., & Kumar, D. (2019). Measuring Dependence Between the USA and the Asian Economies: A Timevarying Copula Approach. Global Business Review, 20, 962-980.

Rigobon, R. (2019). Contagion, Spillover, and Interdependence. Economía, 19(2), 69-100.

Rua, A., Nunes, L., 2009. International comovement of stock market returns: A wavelet analysis. Journal of Empirical Finance, 16, 632–639.

Scharnagl, M., Mandler, M. 2019. Real and financial cycles in euro area economies: Results from wavelet analysis, Journal of Economics and Statistics, 239(5-6), 895-916.

Torrence, C., Compo., G.P. (1998). A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society, 79, 61–78.

Torrence, C., & Webster, P. J. (1999). Interdecadal changes in the ENSO–monsoon system. Journal of climate, 12(8), 2679-2690.

Publicado

21-10-2022

Edição

Seção

Artigo

Como Citar

Oliveira, W. S., Matos, P. R. F., & Silva, C. da C. da . (2022). Sobre a sincronização de índices financeiros bancários – uma abordagem baseada em wavelets. Estudos Econômicos (São Paulo), 52(3), 571-611. https://doi.org/10.1590/1980-53575234wpc