A New Perspective on Energy Contagion in Colombia: Evidence from Wavelet Analysis and Co-Movement Dynamics
Abstract
Purpose: The aim of this study was to examine the existence of energy contagion from the most important energy indicators—oil, natural gas, and coal—to spot electricity prices in Colombia.
Design/Methodology: The methodology employed here was correlational, with a quantitative approach. Daily data from February 2011 to December 2018 were used, excluding the 2008 financial crisis and the Covid-19 pandemic. The data were sourced from Refinitiv and XM. Wavelet analysis and co-movement dynamics were applied. Additionally, cross-correlation was used to analyze financial contagion from energy indicators to spot electricity prices.
Findings: This study demonstrated that there are significant long-term correlations between energy indicators and spot electricity prices. It also determined the presence of energy contagion from natural gas and Brent crude oil to spot electricity prices during crisis periods. Regarding coal, there is no clear evidence of contagion. These findings are relevant for understanding how changes in the global energy market can affect electricity prices in the long term in an emerging economy like Colombia.
Conclusions: Energy contagion impacts the global economy, especially in energy-dependent emerging markets. This study emphasizes the need to understand and mitigate risks in the energy market, offering key information to companies, investors, and policymakers.
Originality: Advanced methods were employed here to analyze the impact of international fuel prices on the Colombian electricity market, identifying contagion periods and highlighting the vulnerability of emerging economies to changes in the global energy market.
References
Algieri, B., & Leccadito, A. (2017). Assessing contagion risk from energy and non-energy commodity markets. Energy Economics, 62, 312-322. https://doi.org/10.1016/j.eneco.2017.01.006
Bae, K.-H., Karolyi, G. A., & Stulz, R. M. (2003). A new approach to measuring financial contagion. The Review of Financial studies, 16(3), 717-763. https://doi.org/10.1093/rfs/hhg012
Benhmad, F. (2013). Bull or bear markets: A wavelet dynamic correlation perspective. Economic Modelling, 32, 576-591. https://doi.org/10.1016/j.econmod.2013.02.031
Belhassine, O., & Karamti, C. (2021). Volatility spillovers and hedging effectiveness between oil and stock markets: Evidence from a wavelet-based and structural breaks analysis. Energy Economics, 102, issue C, S0140988321003959. https://EconPapers.repec.org/RePEc:eee:eneeco:v:102:y:2021:i:c:s0140988321003959
Boako, G., Alagidede, I. P., Sjo, B., & Uddin, G. S. (2020). Commodities price cycles and their interdependence with equity markets. Energy Economics, 91, 104884. https://doi.org/10.1016/j.eneco.2020.104884
Boubaker, H., & Raza, S. A. (2017). A wavelet analysis of mean and volatility spillovers between oil and BRICS stock markets. Energy Economics, 64, 105-117. https://doi.org/10.1016/j.eneco.2017.01.026
Briones Pinargote, C. J. (2023). Competitividad internacional del sector atunero: una aplicación al sector ecuatoriano. Interciencia, 48(4), 184-196. https://dialnet.unirioja.es/servlet/articulo?codigo=8946148
Calvo, G. A., Leiderman, L., & Reinhart, C. M. (1996). Inflows of Capital to Developing Countries in the 1990s. Journal of Economic Perspectives, 10(2), 123-139. https://doi.org/10.1257/jep.10.2.123
Cărăuşu, D. N., Filip, B. F., Cigu, E., & Toderaşcu, C. (2018). Contagion of Capital Markets in CEE Countries: Evidence from Wavelet Analysis. Emerging Markets Finance and Trade, 54(3), 618-641. https://doi.org/10.1080/1540496X.2017.1410129
Centeno, M. A., Nag, M., Patterson, T. S., Shaver, A., & Windawi, A. J. (2015). The emergence of global systemic risk. Annual Review of Sociology, 41, 65-85. https://doi.org/10.1146/annurev-soc-073014-112317
Chakraborty, U. K. (2008). Advances Differential Evolution. Springer.
Dash, S. R., & Maitra, D. (2019). The relationship between emerging and developed market sentiment: A wavelet-based time-frequency analysis. Journal of Behavioral and Experimental Finance, 22, 135-150. https://doi.org/10.1016/j.jbef.2019.02.006
Díaz, G., Coto, J., & Gómez-Aleixandre, J. (2019). Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression. Applied Energy, 239, 610-625. https://doi.org/10.1016/j.apenergy.2019.01.213
Dornbusch, R., Park, Y. C., & Claessens, S. (2000). Contagion: Understanding How It Spreads. The World Bank Research Observer, 15(2), 177-197. https://doi.org/10.1093/wbro/15.2.177
Eichengreen, B., Rose, A. K., & Wyplosz, C. (1999). Contagious Currency Crises. National Bureau of Economic Research, 29-56. https://doi.org/10.3386/w5681
Fang, S., & Egan, P. (2018). Measuring contagion effects between crude oil and Chinese stock market sectors. The Quarterly Review of Economics and Finance, 68, 31-38. https://doi.org/10.1016/j.qref.2017.11.010
Fernández-Macho, J. (2018). Time-localized wavelet multiple regression and correlation. Physica A: Statistical Mechanics and its Applications, 492, 1226-1238. https://doi.org/10.1016/j.physa.2017.11.050
Ftiti, Z., & Hadhri, S. (2019). Can economic policy uncertainty, oil prices, and investor sentiment predict Islamic stock returns? A multi-scale perspective. Pacific-Basin Finance Journal, 53, issue C, 40-55. https://EconPapers.repec.org/RePEc:eee:pacfin:v:53:y:2019:i:c:p:40-55
Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: Measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261. https://doi.org/10.1111/0022-1082.00494
Fry, R., Martin, V. L., & Tang, C. (2010). A new class of tests of contagion with applications. Journal of Business y Economic Statistics, 28(3), 423-437. https://doi.org/10.1198/jbes.2010.06060
Fry-McKibbin, R., Hsiao, C.-L., & Tang, C. (2014). Contagion and global financial crises: lessons from nine crisis episodes. Open Economies Review, 25(3), 521-570. https://doi.org/10.1007/s11079-013-9289-1
Fry-McKibbin, R., & Hsiao, C. Y-L. (2018). Extremal dependence tests for contagion. Econometric Reviews, 37(6), 626-649. https://doi.org/10.1080/07474938.2015.1122270
Gallegati, M. (2012). A wavelet-based approach to test for financial market contagion. Computational Statistics y Data Analysis, 56(11), 3491-3497. https://doi.org/10.1016/j.csda.2010.11.003
Garcia, J., & Pérez-Libreros, A. F. (2019). El precio spot de la electricidad y la inclusión de energía renovable no convencional: evidencia para Colombia. SSRN. http://dx.doi.org/10.2139/ssrn.3443910
Ghorbel, A., & Boujelbene, Y. (2013). Contagion effect of the oil shock and US financial crisis on the GCC and BRIC countries. International Journal of Energy Sector Management, 7(4), 430-447. https://doi.org/10.1108/IJESM-04-2012-0002
Ghosh, I., Sanyal, M. K., & Jana, R. K. (2020). Co-movement and Dynamic Correlation of Financial and Energy Markets: An Integrated Framework of Nonlinear Dynamics, Wavelet Analysis and DCC-GARCH. Computational Economics, 57, 503–527. https://doi.org/10.1007/s10614-019-09965-0
Grubel, H. G., & Fadner, K. (1971). The interdependence of international equity markets. The Journal of Finance, 26(1), 89-94. https://doi.org/10.1111/j.1540-6261.1971.tb00591.x
Guesmi, K., Abid, I., Creti, A., & Chevallier, J. (2018). Oil Price Risk and Financial Contagion. The Energy Journal, 39(2). https://doi.org/10.5547/01956574.39.SI2.kgue
Hamdi, B., Aloui, M., Alqahtani, F., & Tiwari, A. (2019). Relationship between the oil price volatility and sectoral stock markets in oil-exporting economies: Evidence from wavelet nonlinear denoised based quantile and Granger-causality analysis. Energy Economics, 80, 536-552. https://doi.org/10.1016/j.eneco.2018.12.021
Hergety, S. W. (2012). Exchange market pressure, commodity prices, and contagion in Latin America. The Journal of International Trade & Economic Development, 23(1), 56-77. https://doi.org/10.1080/09638199.2012.679292
Hong, Y., Tu, J., & Zhou, G. (2007). Asymmetries in Stock Returns: Statistical Tests and Economic Evaluation. The Review of Financial Studies, 20(5), 1547-1581. https://doi.org/10.1093/rfs/hhl037
Kaminsky, G. L., & Reinhart, C. M. (2000). On crises, contagion, and confusion. Journal of International Economics, 51(1), 145-168. https://doi.org/10.1016/S0022-1996(99)00040-9
Kilian, L., & Vigfusson, R.J. (2011). Are the responses of the U.S. economy asymmetric in energy price increases and decreases? Quantitative Economics, 2, 419-453. https://doi.org/10.3982/QE99
Lahmiri, S., Uddin, G. S., & Bekiros, S. (2017). Clustering of short and long-term co-movements in international financial and commodity markets in wavelet domain. Physica A: Statistical Mechanics and its Applications, 486, 947-955. https://doi.org/10.1016/j.physa.2017.06.012
Li, J., Xie, C., & Long, H. (2019). The roles of inter-fuel substitution and inter-market contagion in driving energy prices: Evidences from China’s coal market. Energy Economics, 84, 104525. https://doi.org/10.1016/j.eneco.2019.104525
Lin, L., Kuang, Y., Jiang, Y., & Su, X. (2019). Assessing risk contagion among the Brent crude oil market, London gold market and stock markets: Evidence based on a new wavelet decomposition approach. The North American Journal of Economics and Finance, 50, 101035. https://doi.org/10.1016/j.najef.2019.101035
Lunde, A., & Timmermann, A. (2004). Duration dependence in stock prices: An analysis of bull and bear markets. Journal of Business & Economic Statistics, 22(3), 253-273. https://doi.org/10.1198/073500104000000136
Mahadeo, S. M. R., Heinlein, R., & Legrenzi, G. D. (2019). Energy contagion analysis: A new perspective with application to a small petroleum economy. Energy Economics, 80, 890-903. https://doi.org/10.1016/j.eneco.2019.02.007
Mensi, W., Ur Rehman, M., Al-Yahyaee, K. H., & Vo, X. V. (2023). Frequency dependence between oil futures and international stock markets and the role of gold, bonds, and uncertainty indices: Evidence from partial and multivariate wavelet approaches. Resources Policy, 80, 103161. https://doi.org/10.1016/j.resourpol.2022.103161
Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal Applied Econometrics, 18(1), 511-532. https://doi.org/10.1002/jae.664
Pan, Z., Zheng, X., & Gong, Y. (2015). A model-free test for contagion between crude oil and stock markets. Economics Letters, 130, 1-4. https://doi.org/10.1016/j.econlet.2015.02.023
Pericoli, M., & Sbracia, M. (2003). A Primer on Financial Contagion. Journal of Economic Surveys, 17(4), 571-608. https://doi.org/10.1111/1467-6419.00205
Ranta, M. (2013). Contagion among major world markets: a wavelet approach. International Journal of Managerial Finance, 9(2), 133-149. http://dx.doi.org/10.1108/17439131311307556
Reboredo, J. C., & Rivera-Castro, M. A. (2013). A wavelet decomposition approach to crude oil price and exchange rate dependence. Economic Modelling, 32, 42-57. https://doi.org/10.1016/j.econmod.2012.12.028
Reboredo, J. C., & Rivera-Castro, M. A. (2014). Wavelet-based evidence of the impact of oil prices on stock returns. International Review of Economics & Finance, 29, 145-176. https://doi.org/10.1016/j.iref.2013.05.014
Roy, A., Anchal, S., & Soudeep, D. (2023). A wavelet-based methodology to compare the impact of pandemic versus Russia–Ukraine conflict on crude oil sector and its interconnectedness with other energy and non-energy markets. Energy Economics, 124, 106830. https://doi.org/10.1016/j.eneco.2023.106830
Samarakoon, L. P. (2011). Stock market interdependence, contagion, and the U.S. financial crisis: The case of emerging and frontier markets. Journal of International Financial Markets, Institutions and Money, 21(5), 724-742. https://doi.org/10.1016/j.intfin.2011.05.001
Sharpe, W. F. (1964). Capital asset prices: a theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x
Shrestha, K., Subramaniam, R., Peranginangin, Y., y Philip, S. S. S. (2018). Quantile hedge ratio for energy markets. Energy Economics, 71, 253-272. https://doi.org/10.1016/j.eneco.2018.02.020
Uribe, J. (2011). Contagio financiero: una metodología para su evaluación mediante coeficientes de dependencia asintótica. Lecturas de Economía, (75), 29-57. http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-25962011000200003&lng=en&tlng=es
Wen, X., Wei, Y., & Huang, D. (2012). Measuring contagion between energy market and stock market during financial crisis: A copula approach. Energy Economics, 34(5), 1435-1446. https://doi.org/10.1016/j.eneco.2012.06.021
XM. (s.f.). Precio de bolsa y escasez. https://www.xm.com.co/transacciones/cargo-por-confiabilidad/precio-de-bolsa-y-escasez
Zhao, Z., Wen, H., & Li, K. (2021). Identifying bubbles and the contagion effect between oil and stock markets: New evidence from China. Economic Modelling 94, 780-788. https://doi.org/10.1016/j.econmod.2020.02.018
Zhou, Z., Lin, L., & Li, S. (2018). International stock market contagion: A CEEMDAN wavelet analysis. Economic Modelling, 72, 333-352. https://doi.org/10.1016/j.econmod.2018.02.010
Zhu, H., Wu, H., Ren, Y., & Yu, D. (2022). Time-frequency effect of investor sentiment, economic policy uncertainty, and crude oil on international stock markets: evidence from wavelet quantile analysis. Applied Economics, 54(53), 6116-6146. https://doi.org/10.1080/00036846.2022.2057912
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