Analysis of Colombian market volatility in the covid-19 scenario: a review from the postulates of fractal geometry and econometric models
Abstract
The analysis of the MSCI COLCAP index of the Colombian Stock Exchange is crucial to understand the dynamics of the market and assess its stability and risks, especially post-pandemic. This study is justified by the need to examine the persistence and behavior of the volatility of the Colombian index in a context affected by covid-19, where price fluctuations have been notoriously marked. Using the Hurst coefficient, it was determined that the index shows historical memory. The volatility analysis, based on 20-day windows, revealed greater instability after the pandemic. Backtesting tests with GARCH models indicated a significant increase in risk during the pandemic, with expected values of -1.14% before the pandemic, -7.4% in March 2020 and -1.95% afterwards. Given the above context, it can be said that the present study allows us to understand the dynamics and stability of the Colombian market post-pandemic and provides practical tools to improve risk forecasting and management. It is relevant to investors, regulators and academics interested in emerging markets and their response to disruptive global events.
Keywords
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References
Banco de la República. (s.f.). Mercado accionario. https://www.banrep.gov.co/es/estadisticas/mercado-accionario
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31 (3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
Bollerslev, T., Engle, R. & Wooldridge, J. (1988). A capital asset pricing model with time varying covariances. Journal of Political Economy, 96 (1), 116-131. https://doi.org/10.1086/261527
Casas, M. y Cepeda, E. (2008). Modelos ARCH, GARCH y EGARCH: aplicaciones a series financieras. Cuadernos de Economía, 27(48), 287-319.
Casparri, M. y Moreno, A. (2008). Geometría fractal y mercados financieros. Universidad de Buenos Aires.
Cortés, J. y Bravo, W. (2023). Análisis del propósito de un portafolio eficiente para clientes inversionistas. Economía & Sociedad, 4(1), 8-16. https://doi.org/10.5377/aes.v4i1.16155
Engle, R. F. (1982). Autoregresive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007. https://doi.org/10.2307/1912773
Engle, R., Granger, C. & Kraft, D. (1984). Combining competing forecasts of inflation with a bivariate ARCH model. Journal of Economic Dynamics and Control, 8(2), 151-165. https://doi.org/10.1016/0165-1889(84)90031-9
Espinosa, O. y Vaca, P. (2017). Ajuste de modelos GARCH clásico y Bayesiano con innovaciones T-Student para el índice COLCAP. Revista de Economía del Caribe, 19, 34-63. https://rcientificas.uninorte.edu.co/index.php/economia/article/view/8343
Fan, J. & Yao, Q. (2003). Nonlinear time series: nonparametric and parametric methods. Springer. https://doi.org/10.1007/978-0-387-69395-8
Hamilton, J. (1994). Time series analysis. Princenton University press.
Hanusz, Z., Tarasinska, J. & Zielinski, W. (2016). Shapiro-Wilk test with know mean. REVSTAT - Statistical Journal, 14(1), 89-100. https://doi.org/10.57805/revstat.v14i1.180
Luengas, D., Ardila, E. y Moreno, J. (2010). Metodología e interpretación del coeficiente de Hurst. Odeon, (5), 265-290.
Mandelbrot, B. (1987). Los objetos fractales. Forma, azar y dimensión. Tusquets Editores.
Mandelbrot, B. (1997). Fractals and scalind in finance. Springer.
Martínez, M., Ariza, M. y Cadena, J. (2021). Relevancia del patrón de persistencia de Hurst en la gestión de portafolios de renta variable. Revista de Métodos Cuantitativos para la Economía y la Empresa, 32, 66-82. https://doi.org/10.46661/revmetodoscuanteconempresa.4122
Nieto, H., Álvarez, J. y Rodríguez, E. (2016). Análisis de persistencia en acciones financieras en el mercado colombiano a través de la metodología de Rango Reescalado (R/S). Cuadernos Latinoamericanos de Administración, 12(22), 23-32. https://doi.org/10.18270/cuaderlam.v12i22.1783
Peters, E. (1994a). Fractal market analysis. Wiley & Sons Inc.
Peters, E. (1994b). Market analysis: applying chaos theory to investment and economics (5st ed.). Wiley.
Rodríguez, N. (2018). La bolsa de valores de Colombia, su naturaleza y su posición sobre las sociedades comisionistas de bolsa: el planteamiento del service level agreement (SLA) como posible forma de mitigación. Derecho PUCP, (81), 265-302. https://doi.org/10.18800/derechopucp.201802.009
Rodríguez, R. (2014). El coeficiente de Hurst y el parámetro α-estable para el análisis de series financieras. Aplicación al mercado cambiario mexicano. Contaduría y Administración, 59(1), 149-173. https://doi.org/10.1016/S0186-1042(14)71247-1
Rossi, G. (2013). La volatilidad en mercados financieros y de commodities. Un repaso de sus causas y la evidencia reciente. Invenio, 16(30), 59-74. http://www.redalyc.org/articulo.oa?id=87726343005
Shapiro, S. & Wilk, M. (1965). An analysis of variance test for normality. Biometrika, 52(3/4), 591-611. https://doi.org/10.2307/2333709
Shapiro, S. & Wilk, M. (1968). Approximations for the null distribution of the W statistic. Technometrics, 10(4), 861-866. https://doi.org/10.2307/1267467
Solis, J., Ponce, M., Castilla, G., González, J., Pérez, J. & Terán, J. (2019). Hurst exponent with ARIMA and exponential smoothing for measuring persistency of M3- competition series. IEEE Latin America Transactions, 17(5), 815-822. https://latamt.ieeer9.org/index.php/transactions/article/view/1119
Taylor, S. (1986). Modeling financial time series. John Wiley & Sons.
Tsay, R. (2005). Analysis of financial time series . John Wiley & Sons.
Villalba, F. y Florez-Ortega, M. (2014). Análisis de la volatilidad del índice principal del mercado bursátil mexicano, del índice de riesgo país y de la mezcla mexicana de exportación mediante un modelo GARCH trivariado asimétrico. Revista de Métodos Cuantitativos para la Economía y la Empresa, 17, 3-22. https://doi.org/10.46661/revmetodoscuanteconempresa.2191