Estimation and relation of memory persistence in contagion and market variables

Authors

  • Guillermo Sierra Juárez Universidad de Guadalajara, Departamento de Métodos Cuantitativos CUCEA

DOI:

https://doi.org/10.29201/peipn.v17i35.97

Keywords:

COVID-19, Hurst coefficient, financial market

Abstract

This work is an application of the methodology Rescaled Range (R/S) for the determination of the Hurst coefficient in the case of the representative variables of the financial markets in the United States and Mexico, as well as in the growth in COVID-19 infections worldwide, in the United States and Mexico. One of the most important results, there is greater memory persistence in financial returns and contagions in the world case and in the United States than with respect to Mexico. Another important result is that during the periods of greatest contagion of COVID-19, the memory of the series in infections increase in the world, United States and Mexico. Additionally, the correlation coefficients between Hurst exponents are higher for the health series for the three cases than when compared with the Hurst coefficients of the financial series.

Downloads

Download data is not yet available.

References

Álvarez R. J.; J. Alvarez; E. Rodriguez; G. Fernández (2008). Time-varying Hurst exponent for US stock markets, Physica A: Statistical Mechanics and its Applications, 387 ( 24), pp. 6159-6169, https://doi.org/10.1016/j.physa.2008.06.056.

Arouxet M. B.; A. F. Bariviera; V. E. Pastor y V. Vampa (2020). Covid-19 impact on cryptocurrencies: evidence form a wavelet-based Hurst exponent, https://arxiv.org/abs/2009.05652v1.

Barua, S. (2020). COVID-19. Pandemic and World Trade: Some Analytical Notes, SSRN, http://dx.doi.org/10.2139/ssrn.3577627.

Bodenstein M.; G. Corsetti; L. Guerrieri (2020). “Disruptions in a Pandemic”, Social Distancing and Supply VOX EU CEPR, https://voxeu.org/article/social-distancing-and-supply-disruptions-pandemic.

Hurst, H. (1951). The long-term storage capacity of reservoirs, Transactions of the American Society of Civil Engineers, 1951, vol. 116, Issue 1, pp. 770-799.

Laktyunkin, A.; A. Potapov (2020). Impact of COVID-19 on the Financial Crisis: Calculation of Fractal Parameters, Biomed J. Sci & Tech Res 30(5)-2020. BJSTR. MS.ID.005019.

Mandelbrot, B. (1982). The Fractal Geometry of Nature, NY W.H. Freeman.

Mandelbrot, B. and V. Ness (1968). Fractional Brownian Motions, Fractional Noises and Applications, SIAM review 10, 11(3).

Okorie, D.; B. Lin (2021). Stock markets and COVID-19 fractal contagion effects, Finance Research Letters, vol., 38, https://doi.org/10.1016/j.frl.2020.101640.

Palomas E. (2002). Evidencia e Implicaciones del fenómeno Hurst en el mercado de capitales, Gaceta de economía, año 8, núm., p. 15.

Peters, E. (1991). Chaos and Order in Capital Markets, New York, 2nd ed., John Wiley and Sons.

Peters, E. (1994). Fractal Market Analysis, Applying Chaos Theory to Invesment an Economic, New York: John Wiley and Sons.

Swetadri S.; G. Koushik (2020). Analysis of Self-Similarity, Memory and Variation in Growth Rate of COVID-19 Cases in Some Major Impacted Countries, Journal of Physics: Conference Series, vol., pp. 1797.

Zavarce C. (2020). Comportamiento Estocástico de la COVID-19 en la República Bolivariana de Venezuela ¿Persistencia o antipersistencia de los Contagios?, vol. 5, num. 2, mayo-agosto 2020, pp. 91-110.

Published

2022-01-14 — Updated on 2021-12-20

How to Cite

Sierra Juárez, G. (2021). Estimation and relation of memory persistence in contagion and market variables. Panorama Económico, 17(35), 129–144. https://doi.org/10.29201/peipn.v17i35.97

Issue

Section

Artículos

Similar Articles

1 2 3 4 > >> 

You may also start an advanced similarity search for this article.