Empirical Evidence of Associations and Similarities between the National Equity Markets Indexes and Crude Oil Prices in the International Market

Salles, Andre Assis de and Maria Eduarda, Silva and Paulo, Teles (2022): Empirical Evidence of Associations and Similarities between the National Equity Markets Indexes and Crude Oil Prices in the International Market. Published in: Open Journal of Business and Management , Vol. 10, (January 2022): pp. 155-174.

Abstract

The stock market is a major component of the financial sector of any economy and it is particularly affected by crude oil price. Moreover, the financialization of the oil market in the last three decades increased its association with the financial markets. The main purpose of this paper is to uncover similarities among the economy of selected countries based on the association between their national stock markets and crude oil price. This is achieved by time series clustering of the conditional correlations between the national stock market index returns and crude oil price returns estimated from bivariate GARCH models. The clusters do not lead to a clear classification concerning the countries stage of development, emerging and developed, or the geographical region which can be explained by crude oil market financialization.

• Item Type: MPRA Paper
• Language: English
• Keywords: Crude Oil Prices; Stock Markets; GARCH models; Time Series Clustering.
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; F - International Economics > F2 - International Factor Movements and International Business > F21 - International Investment ; Long-Term Capital Movements; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q43 - Energy and the Macroeconomy

References

[1] Chen, N., Roll, R., Ross, S., Economic Forces and the Stock Market. Journal of Business, 1986, 59, 383-403. http://dx.doi.org/10.1086/296344

[2] Ferson, W., Harvey, C., Predictability and Time-Varying Risk in World Equity Markets. Research in Finance, 1995, 13, 25-88. https://faculty.fuqua.duke.edu/~charvey/Teaching/CDROM_BA453_2003/Other_Harvey_Papers/P31_Predictability_and_time.pdf

[3] Jones, C., Kaul, G., Oil and Stock Markets. Journal of Finance, 1996, 51, 2, 463-491. https://doi.org/10.2307/2329368

[4] Fall, R., Brailsford, T., Oil Price Risk and the Australian Stock Market. Journal of Energy Finance and Development, 1999, 4, 69-87. https://doi.org/10.1016/S1085-7443(99)00005-8

[5] Sharpe, W., A Simplified Model for Portfolio Analysis. Management Science, 1963, 9, 2, 277-293. http://dx.doi.org/10.1287/mnsc.9.2.277

[6] Sadorsky, P., Oil Price Shocks and Stock Market Activity. Energy Economics, 1999, 21, 5, 449-469. https://doi.org/10.1016/S0140-9883(99)00020-1

[7] Papapetrou, E., Oil Price Shocks, Stock Markets, Economic Activity and Employment in Greece. Energy Economics, 2001, 23, 5, 511-532. https://doi.org/10.1016/S0140-9883(01)00078-0

[8] Sadorsky, P., The Macroeconomic Determinants of Technology Stock Price Volatility. Review of Financial Economics, 2003, 12, 2, 191-205. https://doi.org/10.1016/S1058-3300(02)00071-X

[9] Hammoudeh, S., Aleisa, E., Dynamic Relationship among GCC Stock Markets and NYMEX Oil Futures. Contemporary Economic Policy, Western Economic Association International, 2004, 22, 2, 250-269. https://doi.org/10.1093/cep/byh018

[10] Hammoudeh, S., Didooglu, S., Aleisa, E., Relationships among U.S. Oil Prices and Oil Industry Equity Prices. International Review of Economics & Finance, 2004, 13, 4, 427-453. https://doi.org/10.1016/S1059-0560(03)00011-X

[11] Maghyereh, A., Oil Price Shocks and Emerging Stock Markets: A Generalized VAR Approach. International Journal of Applied Econometrics and Quantitative Studies, 2004, 1, 2, 27-40. https://www.usc.es/economet/reviews/ijaeqs122.pdf

[12] Basher, S., Sadorsky, P., Oil Price Risk and Emerging Stock Markets. Global Finance Journal, 2006,17, 224-251. https://doi.org/10.1016/j.gfj.2006.04.001

[13] Nandha, M., Faff, R., Does Oil Move Equity Prices? A Global View. Energy Economics, 2008, 30, 3, 986-997. https://doi.org/10.1016/j.eneco.2007.09.003

[14] Tansuchat, R., Chang, C., McAleer, M., Conditional correlations and Volatility Spillovers between Crude Oil and Stock Index Returns. 2010. http://dx.doi.org/10.2139/ssrn.1534043

[15] Huang, A., Hu, W., Cheng, C., Chen, C., Relationship between Crude Oil Prices and Stock Prices of Alternative Energy Companies with Recent Evidence. Economic Bulletin, 2011, 31, 2434-2443. http://www.accessecon.com/Pubs/EB/2011/Volume31/EB-11-V31-I3-P218.pdf

[16] Yazdan, G. F., Ehsan, A. G., Hossein, S. M., Is Cointegration between Oil Price and Economic Growth? Case Study Iran. Procedia Social and Behavioral Sciences, 2012, 62, 1215-1219. https://doi.org/10.1016/j.sbspro.2012.09.207

[17] Bhunia, A., Association between Crude Price and Stock Indices: Empirical Evidence from Bombay Stock Exchange. Journal of Economics and Sustainable Development, 2012, 3, 25-34. https://core.ac.uk/download/pdf/234645527.pdf

[18] Ratti, R., Hasan, M., Oil Price Shocks and Volatility in Australian Stock Returns, 2013. http://mpra.ub.uni-muechen.de/49043/

[19] Tang, K., Xiong, W., Index Investment and the Financialization of Commodities. Financial Analysts Journal, 2012, 68, 6, 54-74. https://doi.org/10.2469/faj.v68.n6.5

[20] D’Ecclesia, R., Magrini, E., Montalbano, P., Triulzi, U., Understanding Recent Oil Price Dynamics: A Novel Empirical Approach. Energy Economics, 2014, 46, Supplement 1, S11-S17. https://doi.org/10.1016/j.eneco.2014.10.005

[21] Bauwens, L., Laurent, S., Rombouts, J., Multivariate GARCH models: a survey. Journal of Applied Econometrics, 2006, 71, 79-109. https://doi.org/10.1002/jae.842

[22] Bollerslev, T., Engle, R. D., Wooldridge, J. M., A capital asset pricing model with time varying covariances. Journal of Political Economy, 1988, 96, 116131. http://doi.org/10.1086/261527

[23] Ding, Z., Engle, R., Large Scale Conditional Covariance Matrix Modelling, Estimation and Testing. Academic Economic Papers, 2001, 29, 157-184. https://core.ac.uk/download/pdf/43022357.pdf

[24] Bollerslev, T., Wooldridge, J. M., Quasi-maximum likelihood estimation and inference in dynamical models with time varying covariances. Economic Reviews, 1992, 11, 2, 143-172. https://doi.org/10.1080/07474939208800229

[25] Harvey, A., Ruiz, E., Sentana, E., Unobservable component time series models with ARCH disturbances. Journal of Econometrics, 1992, 52, 129-158. Available at: https://core.ac.uk/download/pdf/30046432.pdf

[26] Fiorentini, G., Sentana, E., Shepard, N., Maximum likelihood estimation and inference in multivariate conditionally heteroskedastic dynamic regression models with Student t innovations. Journal of Business and Economic Statistics, 2003, 21, 532-546. https://www.cemfi.es/~sentana/es/student.pdf

[27] Caiado, J., Crato, N., Peña, D., A periodogram-based metric for time series classification. Computational Statistics and Data Analysis, 2006, 50, 10, 2668-2684. https://doi.org/10.1016/j.csda.2005.04.012

[28] Wang, X., Smith, K., Hyndman, R., Characteristic-Based Clustering for Time Series Data. Data Mining and Knowledge Discovery, 2006, 13, 335-364. https://doi.org/10.1007/s10618-005-0039-x

[29] Jain, A. K., Murthy, M. N., Flynn, P. J., Data clustering: a review. ACM Computing Surveys, 1999, 31, 265-323. https://doi.org/10.1145/331499.331504

[30] R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2015. http://www.R-project.org.

[31] Brockwell, P. J., Davis, R. A., Time Series: Theory and Methods, 2nd ed Springer, New York, 1991.

[32] Johnson, R., Wichern, D., Applied Multivariate Statistical Analysis, 6th ed Englewood Cliffs, NJ: Prentice-Hall, 2007.

[33] Kotkatvuori-Örnberg, J., Nikkinen, J., Äijö, J., Stock market correlations during the financial crisis of 2008-2009: Evidence from 50 equity markets. International Review of Financial Analysis, 2013, 28, 70-78. https://doi.org/10.1016/j.irfa.2013.01.009