Análise Multifractal do Ibovespa: Dinâmicas de Preço, Volume Negociado e Eficiência de Mercado
DOI:
https://doi.org/10.22478/ufpb.2318-1001.2024v12n1.70209Abstract
Objective: The present study aims to investigate the price fluctuations, traded volume, and cross-correlation of the Ibovespa through the application of multifractal methods. The focus is on understanding the trends in the financial market in a more profound way and providing a more solid foundation for decision-making.
Background: While multifractal methods are commonly used in analyzing price fluctuations of financial assets, this study stands out for examining price and volume fluctuations of traded assets in the financial market.
Method: The Multifractal Detrended Fluctuation Analysis (MF-DFA) and Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) methods were employed to analyze the properties of the data series of the São Paulo Stock Exchange Index (Ibovespa).
Results: The analysis revealed that the daily series in reais and dollars show increasing distributions and fluctuations, with multifractality reflecting both anti-persistent and persistent temporal changes, indicating behavior with large and small fluctuations. Additionally, the traded volume and the price-volume cross-correlation demonstrated persistent trends, suggesting a long-term memory behavior.
Contributions: This study significantly contributes to the multifractal literature by analyzing the complexity of the Ibovespa series and traded volume. It emphasizes the importance of further research in the Brazilian market and provides relevant evidence for investors, especially during turbulent periods like the recession caused by Covid-19. The results underscore the relevance of traded volume as a crucial source of information for decision-making in the financial market.
Downloads
References
Ahundjanov, B. B., Akhundjanov, S. B. Okhunjanov, E. & Botir b. (2021). Risk perception and oil and gasoline markets under COVID-19. Journal of Economics and Business, Elsevier, v.115(C). https://doi.org/10.1016/j.jeconbus.2020.105979
Ali, S. R. M., Mensi, W., Anik, K. I., Rahman, M. & Kang, S. H. (2022). The impacts of COVID-19 crisis on spillovers between the oil and stock markets: Evidence from the largest oil importers and exporters. Economic Analysis and Policy, v. 73: 345–372. https://doi.org/10.1016/j.eap.2021.11.009.
Aloui, C., Shahzad, S.J.H. & Jammazi, R. (2018). Dynamic efficiency of European credit sectors: arolling-window multifractal detrended fluctuation analysis. Physica, 506 pp. 337-349. https://doi.org/10.1016/j.physa.2018.04.039
Barbaglia, L., Croux, C. & Wilms, I. (2020). Volatility spillovers in commodity markets: A large t-vector autoregressive approach. Energy Economics, v. 85:1-11. https://doi.org/10.1016/j.eneco.2019.104555
Beaver, W.H. (1968). The information content of annual earnings announcements. J. Account. Res. pp. 67-92. https://doi.org/10.2307/2490070
Borgards, O., Czudaj, R. L.C. & Hoang, T. H. V. (2021). Price overreactions in the commodity futures market: An intraday analysis of the Covid-19 pandemic impact. Resources Policy, v.71. https://doi.org/10.1016/j.resourpol.2020.101966
Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, Volume 1, Issue 2, pages 223-236. https://doi.org/10.1080/713665670
Crouch, R.L. (1970). The volume of transactions and price changes on the New York Stock Exchange. Financial Analysts Journal, v.26 (4) pp. 104-109. https://doi.org/10.2469/faj.v26.n4.104
Chen, C. Hui, E. C. M. & Chiang, S. (2021). What do we know about the status of housing market in China? Evidence from price and rent spillovers across first-tier cities. Habitat International, v.118: pp.1-10. https://doi.org/ 10.1016/j.habitatint.2021.102471
Corbet, S., Goodell, J. W. & Günay, S. (2020). Co-movements and spillovers of oil and renewable firms under extreme conditions: New Evidence from negative WTI prices during COVID-19. Energy Economics, 92. https://doi.org/ 10.1016/j.eneco.2020.104978.
El Alaoui, M. (2017). Price–volume multifractal analysis of the moroccan stock market. Physica A: Statistical Mechanics and its Applications, V. 486, 15, Pages 473-485. https://doi.org/10.1016/j.physa.2017.05.052
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, v. 25: 383–417.
Ferreira, P. (2019). Assessing the relationship between dependence and volume in stock markets: A dynamic analysis. Physica A: Statistical Mechanics and Its Applications, v. 516: pp.90–97. https://doi.org/10.1016/j.physa.2018.09.187
Guo, Y., Yu, Z., Yu, C., Cheng, H., Chen, W. & Zhang, H. (2021). Asymmetric multifractal features of the price–volume correlation in China’s gold futures market based on MF-ADCCA. Research in International Business and Finance, V. 58. https://doi.org/10.1016/j.ribaf.2021.101495
Haroon, O. & Rizvi, S.A.R. (2020). COVID-19: media coverage and financial markets behavior—A sectoral inquiry. J. Behav. Exp. Finance, v. 27. https://doi.org/10.1016/j.jbef.2020.100343. 100343.
Hasan, R. & Salim, M. M. (2017). Power law cross-correlations between price change and volume change of indian stocks. Physica A: Statistical Mechanics and Its Applications, 473: 620–31. https://doi.org/10.1016/j.physa.2017.01.002
Hurst, E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, v.116, pp.770-799.
IPEA. (2022) Carta conjuntura, Número 56, Visão geral da conjuntura, nota de conjuntura 31, terceiro trimestre de 2022. Fonte: https://www.ipea.gov.br/cartadeconjuntura/wp-content/uploads/2022/09/220930_cc_56_nota_31_visao_geral.pdf
Jiang, Z.-Q. Xie, W-J., Zhou, W-X. & Sornette, D. (2019). Multifractal analysis of financial markets: a review. Reports on Progress in Physics, V. 82. https://doi.org/ 10.1088/1361-6633/ab42fb
John, K. & Li, J. (2021). Covid-19, volatility dynamics, and sentiment trading. Journal of Banking & Finance, v. 133. https://doi.org/10.1016/j.jbankfin.2021.106162.
Rak, R., Drozdż, S., Forczek, M., Kwapień, J. & Oświȩcimka, P. (2015). Detrended cross-correlations between returns, volatility, trading activity, and volume traded for the stock market companies. Europhys. Lett., v. 112. https://doi.org/10.5506/APhysPolB.44.2035
Rizvi, S.A., Dewandaru, G., Bacha, O.I., & Masih, M. (2014). An analysis of stock market efficiency: Developed vs Islamic stock markets using MF-DFA. Physica A: Statistical Mechanics and its Applications. V. 407, p. 86-99. https://doi.org/10.1016/j.physa.2014.03.091
Rodriguez, E., Aguilar-Cornejo, M., Femat, R. & Alvarez-Ramirez, J. (2014). Us stock market efficiency over weekly, monthly, quarterly and yearly time scales. Physica A: Statistical Mechanics and Its Applications, v.413, p.554–564. https://doi.org/10.1016/j.physa.2014.07.036
Ruan, Q. & Haocui, V. (2020). Spread de esmagamento de soja da China: análise não linear baseada em MF-DCCA. Physica A., V. 554. https://doi.org/10.1016/j.physa.2019.123899
Ruan, Q., Wang, Y., Lu, X. & Qin, J. (2016). Cross-correlations between Baltic Dry Index and crude oil prices. Phys. A Stat. Mech. Its Appl., v. 453, pp. 278-289. 10.1016/j.physa.2016.02.018
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, Elsevier, v. 70. https://doi.org/10.1016/j.irfa.2020.101496.
Stošić, D., Stošić, D., Ludermir, T. B. & Stošić, T. (2019). Multifractal behavior of price and volume changes in the cryptocurrency market. Physica A: Statistical Mechanics and its Applications, V. 520, pp. 54-61. https://doi.org/10.1016/j.physa.2018.12.038
Stošić, D., Stošić, D., Stošić, T. & Stanley, H. E. (2015). Multifractal properties of price change and volume change of stock market índices. Physica A: Statistical Mechanics and its Applications, Elsevier, v. 428, pp. 46-51. https://doi: 10.1016/j.physa.2015.02.046
Sukpitak, J. & Varagorn, H. (2016). The influence of trading volume on market efficiency: The dcca approach. Physica A: Statistical Mechanics and Its Applications. v. 458: 259–65. https://doi:10.1016/j.physa.2016.03.080
Oświȩcimka, P., Drożdż, S., Forczek, M., Jadach, S. & Kwapień, J. Detrended cross-correlation analysis consistently extended to multifractality, Phys. Rev. E. v. 89, 2014. https://doi.org/10.1103/PhysRevE.89.023305
Patil, A. C. & Rastogi, S. (2019). Time-Varying Price–Volume Relationship and Adaptive Market Efficiency: A Survey of the Empirical Literature. Journal of Risk and Financial Management, v.12. https://doi.org/10.3390/jrfm12020105
Patil, A. C. & Rastogi, S. (2020). Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis. Journal of Risk and Financial Management, v.13. https://doi:10.3390/jrfm13100248
Peng, C-K., Shlomo, H., Eugene, S. H. & Goldberger, A. L. (1994). Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos: An Interdisciplinary Journal of Nonlinear Science, V. 5. https://doi:10.1063/1.166141
Peters, E. E. (1991). Chaos and order in the capital markets - a new view of cycles, prices and Market volatility. New York: John Wiley and Son.
Podobnik, B. & Stanley, H. (2008). Detrended Cross-Correlation Analysis: a new method for analyzing two nonstationary time series. Physical Review Letters, v. 100. https://doi.org/10.1103/PhysRevLett.100.084102
Kantelhardt, J.W., Zschiegner, S.A., Koscielny-Bunde, E., Havlin, S., Bunde, A. & Stanley, H. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and its Applications, v. 316, pp. 87-114.
Karpoff, J. M. A. (1986). Theory of Trading Volume. The Jornal of Finance. V. 41. https://doi.org/10.1111/j.1540-6261.1986.tb02531.x
Kim, Y., Hwang, S-Y., Kim, J-M. & Kim, S. (2022). Linear time-varying regression with copula–DCC–asymmetric–GARCH models for volatility: the co-movement between industrial electricity demand and financial factors. Applied Economics. https://doi.org/ 10.1080/00036846.2022.2086684.
Kristoufek, L. & Vosvrda, M. (2013). Measuring capital market efficiency: Global and local correlations structure. Physica A: Statistical Mechanics and its Applications, vol. 392, pp. 184-193. https://doi.org /10.1016/j.physa.2012.08.003
Khuntia, S. & Pattanayak. J. K. (2018). Adaptive market hypothesis and evolving predictability of bitcoin. Economics Letters, v. 167: 26–28. https://doi.org/10.1016/j.econlet.2018.03.005
Kukacka, J. & Kristoufek, L. (2020). Do ‘complex’ financial models really lead to complex dynamics? Agent-based models and multifractality. Journal of Economic Dynamics and Control, V. 113. https://doi.org/10.1016/j.jedc.2020.103855
Lo, A. W. (2004). The Adaptive Markets Hypothesis. The Journal of Portfolio Management 30th Anniversary. v. 30, pp.15-29. https://doi.org/10.3905/jpm.2004.442611
Mandelbrot, B. B. (1999). A multifractal walk down wall street. Scientific American, a division of Nature America, Inc., v. 280, n. (2) pp. 70-73. https://www.jstor.org/stable/26058060
Mensi, W., Lee, Y-J., Vo, X.V. & Yoon, S-M. (2021). Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil Consumers? Evidence from an asymmetric MF-DFA approach. The North American Journal of Economics and Finance, V. 57. https://doi.org/10.1016/j.najef.2021.101446
Miloş, L. R., Haţiegan, C., Miloş, M. C., Barna, F. M. & Boțoc, C. (2020). Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets. Sustainability, MDPI, vol. 12(2), pages 1-15. https://doi.org/10.3390/su12020535
Monge, M. (2022). Bunker fuel, commodity prices and shipping market indices following the COVID-19 pandemic. A time-frequency analysis. International Economics, V. 172, P. 29-39. https://doi.org/10.1016/j.inteco.2022.08.003.
Nason, G. P. (1999). Wavelets in Time-Series Analysis. Phil. Trans. Roy. Soc. Lond. A, v. 357.
Tauchen, G. E. & Pitts, M. (1983). The Price Variability-Volume Relationship on Speculative Markets. Econometrica, Vol. 51, No. 2, pp. 485-505. https://doi.org/10.2307/1912002
Topcu, M. & Gulai, O. S. (2020). The impact of COVID-19 on emerging stock markets Finance. Research Letters, v. 36. https://doi.org/10.1016/j.frl.2020.101691
Tiwari, A. K., Sim, B. C. & Gupta, R. (2019). Stock market efficiency analysis using long spans of Data: A multifractal detrended fluctuation approach. Finance Research Letters, V. 28, pp. 398-410. https://doi.org/10.1016/j.frl.2018.06.012
Umar, Z., Jareno, F. & Escribano, A. (2021). Agricultural commodity markets and oil prices: An analysis of the dynamic return and volatility connectedness. Resources Policy, v. 73, pp. 1-14. https://doi.org/10.1016/j.resourpol.2021.102147.
Umar, Z., Polat, O., Choi, S.-Y. & Teplova, T. (2022). The impact of the Russia-Ukraine conflict on the connectedness of financial markets. Finance Research Letters, v. 48. https://doi.org/ 10.1016/j.frl.2022.102976.
Zaremba, A., Kizys, R., Aharon, D.Y. & Demir, E. (2020). Infected markets: novel Coronavirus, government interventions, and stock return volatility around the Globe. Finance Res. Lett. V. 35. https://doi.org/ 10.1016/j.frl.2020.101597.
Zhang, W., Lia, Y., Zhang, Z. & Shen, D. (2018). The dynamic cross-correlations between foreign news, local news and stock returns. Physica A: Statistical Mechanics and its Applications, V. 509, pp. 861-872. https://doi.org/10.1016/j.physa.2018.06.098.
Zhang, X., Ding, Z., Hang, J. & He, Q. (2022). How do stock price indices absorb the COVID-19 pandemic shocks? The North American Journal of Economics and Finance, V. 60. https://doi.org/10.1016/j.najef.2022.101672.
Zhou, Y., Lu, B., Dayong, L. & Ruan, Q. (2019). The informativeness of options-trading activities: Non-linear analysis based on MF-DCCA and Granger test. Physica A: Statistical Mechanics and its Applications, V. 534. https://doi.org/10.1016/j.physa.2019.122269.
Wang, H. & Li, S. (2021). Asymmetric volatility spillovers between crude oil and China's financial markets. Energy, v. 233. https://doi.org/10.1016/j.energy.2021.121168.
WORLDBANK. Global economic Prospects, (2022). Disponível em: https://openknowledge.worldbank.org/bitstream/handle/10986/37224/Global-Economic-Prospects-June-2022-Global-Outlook.pdf.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Revista Evidenciação Contábil & Finanças
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Autores que publicam na RECFin concordam com os seguintes termos:- Autores mantém os direitos autorais e concedem à revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Licença Creative Commons Attribution que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista;
- Em virtude de aparecerem nesta revista de acesso público, os artigos são de uso gratuito, com atribuições próprias, em aplicações educacionais e não-comerciais;
- A revista permitirá o uso dos trabalhos publicados para fins não-comerciais, incluindo direito de enviar o trabalho para bases de dados de acesso público.
