ANÁLISE MULTIFRACTAL DAS FLUTUAÇÕES EM MERCADOS FUTUROS DE CAFÉ: IMPLICAÇÕES PARA A EFICIÊNCIA DO MERCADO
Keywords:
Efficient Market, Price and Volume Fluctuation, Commodity, Coffee, Multifractal Analysis.Abstract
This study investigated the efficiency of coffee futures markets, both in the international context (ICE Coffee) and in the Brazilian context (B3 Coffee), through the application of multifractal methods to price fluctuations and traded volumes. The analysis revealed complex multifractal behaviors, with evidence of long-term memory and temporal dependencies, challenging the random walk hypothesis. The traded volume, in particular, proved to be an important predictive indicator, suggesting opportunities for market exploitation. The B3 market exhibited greater multifractal complexity than the ICE market, which presents more arbitrage opportunities but also increases predictive challenges. These results are more aligned with the Adaptive Market Hypothesis (AMH), which recognizes exploitable inefficiencies. The research contributes by expanding the knowledge of interdependence in commodity markets and offers a robust methodology for analyzing complex markets. Practically, the findings provide valuable insights for researchers, investors, traders, and policymakers, suggesting more effective investment strategies tailored to the complexities of these markets.
References
Abry, P., & Veitch, D. (1998). Wavelet analysis of long-range dependent traffic. EEE Transactions on Information Theory, 44(1), 2–15.
Angelis, M. (2003). Multifractal analysis of the time series of Brazilian stock prices. Physica A: Statistical Mechanics and its Applications.
Aslam, F., Ferreira , P., & Ali, H. (2022). Analysis of the Impact of COVID-19 Pandemic on the Intraday Efficiency of Agricultural Futures Markets. J. Risk Financial Manag, 15(12), p. 15(12). doi:https://doi.org/10.3390/jrfm15120607
Baude, F. (2007). Multifractal processes in finance. The European Physical Journal B., 55(4), 605–617.
Bouchaud, J., & Mézard, M. (2000). On the origin of power laws in financial markets. Physica A: Statistical Mechanics and its Applications, 282(1-2), 536–558.
B3. (2024). Contratos futuros de café. Disponível em: https://www.b3.com.br/pt_br/market-data-e-indices/servicos-de-dados/market-data/ 7
CME Group. (2024). Coffee futures. Disponível em: https://www.cmegroup.com/markets/agriculture/lumber-and-softs/coffee.html 7
Cao, G., He, L.-Y., & Cao, J. (2018). Multifractal Detrended Fluctuation Analysis (MF-DFA). Em Multifractal Detrended Analysis Method and Its Application in Financial Markets. Springer. doi:10.1007/978-981-10-7916-0
Conab. (2024). Companhia Nacional de Abastecimento. Brasília. Acesso em 23 de 08 de 2024, disponível em https://www.conab.gov.br/info-agro/safras/cafe
Dai, M., Shao, S., Gao, J., Sun, Y., & Su, W. (2016). Mixed multifractal analysis of crude oil, gold and exchange rate series. Fractals, 24(04). doi:https://doi.org/10.1142/S0218348X16500468
Fama, E. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25, pp. 383-417.
He, L.-Y., & Chen, S.-P. (2010). Are developed and emerging agricultural futures markets multifractal? A comparative perspective. Physica A: Statistical Mechanics and its Applications, 389 (18), pp. 3828-3836. doi:https://doi.org/10.1016/j.physa.2010.05.021
Ihlen, E. (2012). Introduction to multifractal detrended fluctuation analysis in matlab. Frontiers in Physiology: Fractal Physiology, 3 (141(, pp. 1-18. doi:10.3389/fphys.2012.00141
Kantelhardt , J. (2012). Fractal and Multifractal Time Series. Mathematics of Complexity and Dynamical Systems.
Kantelhardt, J., Zschiegner, S., 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, 316(1-4), pp. 87-114. doi:https://doi.org/10.1016/S0378-4371(02)01383-3
Laib , M., Golay, J., Telesca, L., & Kanevski, M. (2018). Multifractal analysis of the time series of daily means of wind speed in complex regions. Chaos, Solitons & Fractals, 109, pp. 118-127. doi:10.1016/j.chaos.2018.02.024
Lo, A. (2004). The adaptive markets hypothesis: market efficiency from an evolutionary perspective. J. Portf. Manage, 30, 15-29.
Mandelbrot, B. (1977). Fractals: Form, Chance, and Dimension. W. H. Freeman and Company.
Mandelbrot, B. (1982). he Fractal Geometry of Nature. W. H. Freeman and Company.
Memon, B., Yao, H., & Naveed, H. (2022). Examining the efficiency and herding behavior of commodity markets using multifractal detrended fluctuation analysis. Empirical evidence from energy, agriculture, and metal markets. Resources Policy, 77. doi:https://doi.org/10.1016/j.resourpol.2022.102715
Moraes, A., Furtini, A., Prado, J., Castro Junior, L., & Ceretta, P. (2024). Evolução da produção científica sobre o Mercado Eficiente: Estudo bibliométrico. Contextus – Revista Contemporânea de Economia e Gestão, v. 22,. doi:https://doi.org/10.19094/contextus.2024.92462
Nejad, S., Stosic, T., & Stosic, B. (2021). Multifractal analysis of the gold market. Fractals, 29(01). doi:https://doi.org/10.1142/S0218348X21500109
Niere, H. (2015). Measuring efficiency of international crude oil markets: A multifractality approach. International Journal of Modern Physics: Conference Series, 36. doi:https://doi.org/10.1142/S2010194515600137
Patil, A., & Rastogi, S. (2020). Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis. J. Risk Financial Manag., 13, 248. doi: https://10.3390/jrfm13100248
Paula, D. A., Fehr, L. C. F. A., Magnago, B. S., Tavares, M., & Lima, D. S. (2022). Custos de produção do café conilon: análise em algumas regiões produtoras do brasil. Organizações Rurais & Agroindustriais, 4, e1872. doi:10.48142/2420221872
Samuelson, P. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial Management Review, Cambridge, 6, p. 41-49.
Silva, E., & Almeida, P. (2011). Multifractal analysis of financial time series: A survey. Physica A: Statistical Mechanics and its Applications.
Sornette, D. (2004). Critical Phenomena in Natural Sciences: Chaos, Fractals, Selforganization and Disorder: Concepts and Tools. Springer.
Stosic, T., Nejad, S., & Stosic, B. (2020). Multifractal analysis of brazilian agricultural market. Fractals, 28(05). doi:https://doi.org/10.1142/S0218348X20500760
Wang, L., Gao , X.-L., & Zhou , W.-X. (2023). Testing for intrinsic multifractality in the global grain spot market indices: a multifractal detrended fluctuation analysis. Fractals, 31(07). doi:https://doi.org/10.1142/S0218348X23500901
Wang, Y., Wei, Y., & Wu, C. (2011). Analysis of the efficiency and multifractality of gold markets based on multifractal detrended fluctuation analysis. Physica A: Statistical Mechanics and its Applications, 390, pp. 817-827. doi:https://doi.org/10.1016/j.physa.2010.11.002
Yin, T., & Wang , Y. (2021). Market Efficiency and Nonlinear Analysis of Soybean Futures. Sustainability, 13(2). doi:https://doi.org/10.3390/su13020518