How sensitive are financial markets to COVID-19 outbreak? Evidence from the United States and Colombia markets.
DOI:
https://doi.org/10.46661/rev.metodoscuant.econ.empresa.6431Keywords:
Market risk, Volatility, Value at risk, Median Shortfall, Crisis, COVID-19, Financial marketsAbstract
In this article, the market risk associated with the financial markets of New York and Colombia is evaluated in three periods belonging to the 2019–2020-time window, characterized by shocking economic and social conditions such as the oil price war between Saudi Arabia and Russia and the global pandemic by COVID-19. Risk measurement is carried out using the value at risk (VaR) and Median Shortfall (MS), applying a statistical methodology that considers the use of parametric and non-parametric resampling techniques (Bootstrapping). Data from five indices (Standard and Poor's 500, Dow Jones, COLCAP, VIX and Brent) were taken in order to evaluate the effects caused by variables such as the price of oil and the conditions generated by the COVID-19 pandemic on the dates of study, as the main result it is obtained that in general there is a very high volatility in the periods affected by the two aforementioned phenomena when they occurred simultaneously, and that in addition to large falls in the reference indices, there is also evidence of large recoveries that contribute positively to the trend in prices.
Downloads
References
Ahmed, D., Soleymani, F., Ullah, M. Z., & Hasan, H. (2021). Managing the risk based on entropic value-at-risk under a normal-Rayleigh distribution. Applied Mathematics and Computation, 402, 126129. https://doi.org/10.1016/j.amc.2021.126129
Alonso, J. C., & Chaves, J. M. (2013). Value-at-risk: Evaluation of the behavior of different methodologies for 5 Latin American countries. Estudios Gerenciales, 29(126), 37-48. https://doi.org/10.1016/S0123-5923(13)70018-4
Ardia, D., & Hoogerheide, L. F. (2014). GARCH models for daily stock returns: Impact of estimation frequency on Value-at-Risk and Expected Shortfall forecasts. Economics Letters, 123(2), 187-190. https://doi.org/10.1016/j.econlet.2014.02.008
Bourghelle, D., Jawadi, F., & Rozin, P. (2021). Oil price volatility in the context of Covid-19. International Economics, 167(April), 39-49. https://doi.org/10.1016/j.inteco.2021.05.001
Chernick, M. R. (2008). Bootstrap Methods: A Guide for Practitioners and Researchers by CHERNICK, M. R. Biometrics, 64(3), 998-999.
https://doi.org/10.1111/j.1541-0420.2008.01082_17.x
Davison, A. C., & Hinkley, D. V. (1997). The Basic Bootstraps. In Bootstrap Methods and their Application (pp. 11-69). Cambridge University Press. https://doi.org/10.1017/CBO9780511802843.003
Efron, B. (2007). Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, 7(1), 1-26. https://doi.org/10.1214/aos/1176344552
Tibshirani, R. J., & Efron, B. (1993). An introduction to the bootstrap. Monographs on statistics and applied probability, 57(1). 1-436
https://doi.org/10.1007/978-1-4899-4541-9_1
Erdely, A. (2017). Valor en riesgo y el dogma de la diversificación. Revista De Métodos Cuantitativos Para La Economía Y La Empresa, 24, Páginas 209 a 219. https://doi.org/10.46661/revmetodoscuanteconempresa.2888
Feria Domínguez, J. M. (2005). El Riesgo de mercado: su medición y control. In Delta Publicaciones (Ed.), Finanzas para la nueva economía. (N.o 1 edic).
Geenens, G., & Dunn, R. (2022). A nonparametric copula approach to conditional Value-at-Risk. Econometrics and Statistics, 21, pp. 19 - 37. https://doi.org/10.1016/j.ecosta.2020.07.001
Google Finance. (2021). https://www.google.com/finance/
Investing. (2021). https://es.investing.com/
Josaphat, B. P., & Syuhada, K. (2021). Dependent conditional value-at-risk for aggregate risk models. Heliyon, 7(7). e07492. https://doi.org/10.1016/j.heliyon.2021.e07492
Kim, D., & Kang, K. H. (2021). Conditional value-at-risk forecasts of an optimal foreign currency portfolio. International Journal of Forecasting, 37(2), 838-861. https://doi.org/10.1016/j.ijforecast.2020.09.011
Kou, S., & Heyde, C. C. (2013). External Risk Measures and Basel Accords Mathematics of Operations Research, 38 (3). https://doi.org/10.1287/moor.1120.0577
Leung, M., Li, Y., Pantelous, A. A., & Vigne, S. A. (2021). Bayesian Value-at-Risk backtesting: The case of annuity pricing. European Journal of Operational Research, 293(2), 786-801. https://doi.org/10.1016/j.ejor.2020.12.051
Li, L. (2017). Testing and comparing the performance of dynamic variance and correlation models in value-at-risk estimation. North American Journal of Economics and Finance, 40, 116-135. https://doi.org/10.1016/j.najef.2017.02.006
Lorenzo Valdés, Arturo. (2016). Exceso de confianza como determinante de la volatilidad en mercados accionarios latinoamericanos. Contaduría y administración, 61(2), 324-333. https://doi.org/10.1016/j.cya.2015.11.006
Meng, X., & Taylor, J. W. (2018). An approximate long-memory range-based approach for value at risk estimation. International Journal of Forecasting, 34(3), 377-388. https://doi.org/10.1016/j.ijforecast.2017.11.007
Oxford Business Group. The Report: Colombia 2013: Country Profile. https://oxfordbusinessgroup.com/colombia-2013
Ozkan, O. (2021). Impact of COVID-19 on stock market efficiency: Evidence from developed countries. Research in International Business and Finance, 58(April), 101445. https://doi.org/10.1016/j.ribaf.2021.101445
Rodríguez, G. (2017). Teoría de valores extremos: una aplicación a los retornos bursátiles peruanos. Revista De Métodos Cuantitativos Para La Economía Y La Empresa, 23, Páginas 48 a 74. https://doi.org/10.46661/revmetodoscuanteconempresa.2686
Sarwar, S., Shahbaz, M., Anwar, A., & Tiwari, A. K. (2019). The importance of oil assets for portfolio optimization: The analysis of firm level stocks. Energy Economics, 78, 217-234. https://doi.org/10.1016/j.eneco.2018.11.021
So, M. K. P., & Yu, P. L. H. (2006). Empirical analysis of GARCH models in value at risk estimation. Journal of International Financial Markets, Institutions and Money, 16(2), 180-197. https://doi.org/10.1016/j.intfin.2005.02.001
Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995). Continuous univariate distributions, volume 2 (2nd ed). John Wiley & Sons.
Wei, Y., Qin, S., Li, X., Zhu, S., & Wei, G. (2019). Oil price fluctuation, stock market and macroeconomic fundamentals: Evidence from China before and after the financial crisis. Finance Research Letters, 30(January), 23-29. https://doi.org/10.1016/j.frl.2019.03.028
World Health Organization. (3 de marzo de 2020). Coronavirus disease 2019 (COVID-19) Situation Report-43.
Xu, Y., Wang, X., & Liu, H. (2021). Quantile-based GARCH-MIDAS: Estimating value-at-risk using mixed-frequency information. Finance Research Letters, 1, 101965.
https://doi.org/10.1016/j.frl.2021.101965
Zhang, J. (2010). A highly efficient L-estimator for the location parameter of the Cauchy distribution. Computational Statistics, 25(1), 97-105.
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 James D. Ramírez Quintero , Jefferson Marulanda Piedrahita, José Rafael Tovar Cuevas, Diego F. Manotas Duque
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Submission of manuscripts implies that the work described has not been published before (except in the form of an abstract or as part of thesis), that it is not under consideration for publication elsewhere and that, in case of acceptance, the authors agree to automatic transfer of the copyright to the Journal for its publication and dissemination. Authors retain the authors' right to use and share the article according to a personal or instutional use or scholarly sharing purposes; in addition, they retain patent, trademark and other intellectual property rights (including research data).
All the articles are published in the Journal under the Creative Commons license CC-BY-SA (Attribution-ShareAlike). It is allowed a commercial use of the work (always including the author attribution) and other derivative works, which must be released under the same license as the original work.
Up to Volume 21, this Journal has been licensing the articles under the Creative Commons license CC-BY-SA 3.0 ES. Starting from Volume 22, the Creative Commons license CC-BY-SA 4.0 is used.
