Conditional Dependence between Stock Markets in Mexico and the United States

Authors

DOI:

https://doi.org/10.4067/S0718-88702016000100001

Keywords:

Stock returns, copulas, TGARCH, EGARCH

Abstract

In this paper the conditional dependence of stock market in Mexico and the United States is studied. Symmetric Joe-Clayton copula is used and conditional probabilities of increases (decreases) in Mexico stock index when there are increases (decreases) in the U.S. stock index are estimated. For the marginal distributions, AR-TGARCH and AR-EGARCH models with a standardized Student’s t distribution for innovations are proposed. Empirical results suggest that there is a high degree of conditional dependence in the tails, presenting higher volatility on the upper (right) tail throughout the period.

Downloads

Download data is not yet available.

References

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1

Canela, M., & Pedreira, E. (2012). Modeling dependence in Latin American markets using copula functions. Journal of Emerging Market Finance, 11(3), 231–270.

Chollete, L., Heinen, A., & Valdesogo, A. (2008). Modeling international financial returns with a multivariate regime-switching copula. Journal of Financial Econometrics, 7(4), 437–480. https://doi.org/10.1093/jjfinec/nbp014

Czapkiewicz, A., & Majdosz, P. (2014). Grouping stock markets with time-varying copula-GARCH model. Czech Journal of Economics and Finance, 64(2), 144–159.

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773

Glosten, L. R., Jagannathan, R., & Runkle, D. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance, 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x

Johansson, A. (2011). Financial markets in East Asia and Europe during the global financial crisis. The World Economy, 34(7), 1088–1105. https://doi.org/10.1111/j.1467-9701.2011.01366.x

Lorenzo-Valdés, A., & Massa, R. (2013). Measuring dependence in financial crisis: A copula approach for Mexico and Brazil. Economía Mexicana, 22(2), 341–355.

Nelsen, R. (1999). An introduction to copulas. Springer-Verlag. https://doi.org/10.1007/978-1-4757-3076-0_1

Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. https://doi.org/10.2307/2938260

Okimoto, T. (2008). New evidence of asymmetric dependence structures in international equity markets. Journal of Financial and Quantitative Analysis, 43(3), 787–815. https://doi.org/10.1017/s0022109000004294

Patton, A. (2001a). Modelling time-varying exchange rate dependence using the conditional copula [Working paper]. University of California, San Diego.

Patton, A. (2001b). Estimation of copula models for time series of possible different lengths [Working paper]. University of California, San Diego.

Patton, A. (2006a). Modelling asymmetric exchange rate dependence. International Economic Review, 2(2), 527–555. https://doi.org/10.1111/j.1468-2354.2006.00387.x

Patton, A. (2006b). Estimation of multivariate models for time series of possibly different lengths. Journal of Applied Econometrics, 21(2), 143–173. https://doi.org/10.1002/jae.865

Rodríguez, J. (2007). Measuring financial contagion: A copula approach. Journal of Empirical Finance, 14(3), 401–423.

Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges [Distribution functions of n dimensions and their margins]. Publications de l'Institut de Statistique de l'Université de Paris, 8, 229–231.

Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931–955.

Published

2016-04-25

How to Cite

Lorenzo-Valdés, A. (2016). Conditional Dependence between Stock Markets in Mexico and the United States. Economic Analysis Review, 31(1), 3–14. https://doi.org/10.4067/S0718-88702016000100001

Issue

Section

Articles

Article usage

ViewsTimes this article's page was opened since the journal started recording statistics. One person can visit more than once.
183
DownloadsTimes the full text of this article (usually the PDF) was opened or downloaded.
230

All time, as recorded by the journal.

Last 2 years: 82 views, 96 downloads.

CitationsTimes other scholarly works cited this article, according to OpenAlex and Crossref. The two indexes cover different sources, so their counts differ. : OpenAlex 2 · Crossref 1

1.26 downloads per view (journal average 1.65).Downloads of the full text for every abstract view of this article, compared with the whole journal.

Explore the journal's metrics