When two banks fall, how do markets react?

Authors

DOI:

https://doi.org/10.17811/ebl.12.4.2023.331-341

Keywords:

Bank fall, Credit Suisse Bank, Detrended fluctuation analysis, Silicon Valley Bank, Sliding windows

Abstract

The most recent fall of the Silicon Valley (SVB) and Credit Suisse (CS) banks increased the fear of a worldwide banking crisis. We analyse the impacts of their fall on five financial indices. We apply detrended fluctuation analysis, static and with sliding windows. We find a higher impact of the SVB fall on the efficiency dynamic of the studied indices, which revealed fluctuating efficiency and a loss of efficiency during the period of the falls. The fall of both banks contributed to some persistence in stock indices returns. The Nasdaq and STOXX Europe 600 Banks are the most and the least efficient indices, respectively. Despite the apparent evidence of inefficiency, it might not necessarily mean a capacity for abnormal profits.

References

Almeida, D., Soares, F. & Carvalho, J. (2013). A sliding window approach to detrended fluctuation analysis of heart rate variability. 35th Annual International Conference of the IEEE EMBS.

Anagnostidis, P., Varsakelis, C., & Emmanouilides, C. J. (2016). Has the 2008 financial crisis affected stock market efficiency? the case of Eurozone. Physica A: Statistical Mechanics and Its Applications, 447, 116–128. https://doi.org/10.1016/j.physa.2015.12.017

Cajueiro, D. & Tabak, B. (2004a). The Hurst exponent over time: Testing the assertion that emerging markets are becoming more efficient. Physica A, 336(3-4), 521–537.

Cajueiro, D.& Tabak, B. (2004b). Evidence of long range dependence in Asian equity markets: The role of liquidity and market restrictions. Physica A, 342(3–4), 656–664.

Cajueiro, D. &Tabak, B. (2006). Testing for predictability in equity returns for European transition markets. Economic Systems, 30(1), 56–78.

Cajueiro, D. & Tabak, B. (2008). Testing for time-varying long-range dependence in real estate equity returns. Chaos, Solitons & Fractals, 38(1), 293–307.

Cao, G. & Zhang, M. (2015). Extreme values in the Chinese and American stock markets based on detrended fluctuation analysis. Physica A, 436(15), 25–35.

Costa, N., Silva, C., & Ferreira, P. (2019). Long-range behaviour and correlation in DFA and DCCA analysis of cryptocurrencies. International Journal of Financial Studies, 7(3). https://doi.org/10.3390/ijfs7030051

Darbellay, G. A. (1998). Predictability: An Information-Theoretic Perspective. In K. N. G. In: Procházka A., Uhlíř J., Rayner P.W.J. (Ed.), Signal Analysis and Prediction. Applied and Numerical Harmonic Analysis. (pp. 249–262). https://doi.org/10.1007/978-1-4612-1768-8_18

Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383–417.

Ferreira, P. (2020). Dynamic long-range dependences in the Swiss stock market. Empirical Economics, 58(4), 1541–1573. https://doi.org/10.1007/s00181-018-1549-x

Ferreira, P., & Dionísio, A. (2014). Revisiting serial dependence in the stock markets of the G7 countries, Portugal, Spain and Greece. Applied Financial Economics, 24(5), 319–331. https://doi.org/10.1080/09603107.2013.875106

Granger, C. W. J., & Morgenstern, O. (1963). Spectral Analysis of New York Stock Market Prices. Kyklos, 16(1), 1–27. https://doi.org/10.1111/j.1467-6435.1963.tb00270.x

Granger, C. W., Maasoumi, E., & Racine, J. (2004). A Dependence Metric for Possibly Nonlinear Processes. Journal of Time Series Analysis, 25(5), 649–669.

Kristoufek, L., & Vosvrda, M. (2013). Measuring capital market efficiency: Global and local correlations structure. Physica A: Statistical Mechanics and Its Applications, 392(1), 184–193. https://doi.org/10.1016/j.physa.2012.08.003

Lim, K. P., Brooks, R. D., & Kim, J. H. (2008). Financial crisis and stock market efficiency: Empirical evidence from Asian countries. International Review of Financial Analysis, 17(3), 571–591. https://doi.org/10.1016/j.irfa.2007.03.001

Los, C. A., & Yu, B. (2008). Persistence characteristics of the Chinese stock markets. International Review of Financial Analysis, 17(1), 64–82. https://doi.org/10.1016/j.irfa.2006.04.001

Mohti, W., Dionísio, A., Ferreira, P., & Vieira, I. (2019). Frontier markets’ efficiency: mutual information and detrended fluctuation analyses. Journal of Economic Interaction and Coordination, 14(3), 551–572. https://doi.org/10.1007/s11403-018-0224-9

Morales, R., Di Matteo, T., Gramatica, R., & Aste, T. (2012). Dynamical generalized Hurst exponent as a tool to monitor unstable periods in financial time series. Physica A: Statistical Mechanics and Its Applications, 391(11), 3180–3189. https://doi.org/10.1016/j.physa.2012.01.004

Murialdo, P., Ponta, L., & Carbone, A. (2020). Long-range dependence in financial markets: A moving average cluster entropy approach. Entropy, 22(6), 1–19. https://doi.org/10.3390/E22060634

Peng, C. K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E., & Goldberger, A. L. (1994). Mosaic organization of DNA nucleotides. Physical Review E, 49(2), 1685–1689. https://doi.org/10.1103/PhysRevE.49.1685

Quintino, D. D., & Ferreira, P. (2021). Diesel prices in Brazil: A dynamic fractional integration analysis. Economics and Business Letters, 10(2), 116–125. https://doi.org/10.17811/ebl.10.2.2021.116-125

Sadique, S., & Silvapulle, P. (2001). Long-term memory in stock market returns: international evidence. International Journal of Finance & Economics, 6(1), 59–67. https://doi.org/10.1002/ijfe.143

Vogl, M. (2023). Hurst exponent dynamics of S&P 500 returns: Implications for market efficiency, long memory, multifractality and financial crises predictability by application of a nonlinear dynamics analysis framework. Chaos, Solitons and Fractals, 166(November 2022), 112884. https://doi.org/10.1016/j.chaos.2022.112884

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Published

15-12-2023

How to Cite

Almeida, D., Dionísio, A., & Ferreira, P. (2023). When two banks fall, how do markets react?. Economics and Business Letters, 12(4), 331–341. https://doi.org/10.17811/ebl.12.4.2023.331-341

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