Energy Markets and EMEA Stocks: Asymmetric TVP-VAR Connectedness and Investment Strategies
DOI:
https://doi.org/10.14419/cngcy585Keywords:
TVP-VAR, Energy Market, EMEA Stocks, Connectedness, Investments strategiesAbstract
This study examines the dynamic transmission of shocks between key energy markets and selected equity markets in the Europe, Middle East, and Africa (EMEA) region using an asymmetric time-varying parameter vector autoregression (TVP-VAR) framework. Our daily dataset includes Clean Energy, Natural Gas, Crude Oil, and Heating Oil and eleven EMEA stocks (Czech Republic, Hungary, Kuwait, Qatar, Saudi Arabia, Poland, UAE, Egypt, Greece, South Africa, and Turkey) spanning from May 1, 2015 to Dec 31, 2024. The analysis reveals a moderate Total Connectedness Index (TCI) under aggregate shocks, with spikes during major global disruptions such as oil and climate related crisis, the COVID-19 pandemic, Russia-Ukraine war, and the Iran Israel war. Positive shocks exhibit stronger spillovers, peaking at over 60% TCI, driven by synchronized recovery periods, while negative shocks show lower but significant connectedness. Clean Energy consistently emerges as a dominant net transmitter across all regimes, while Natural Gas and certain equity markets, such as the UAE and Greece, act as net receivers. Network visualizations highlight Qatar’s prominence in positive shock regimes and Clean Energy’s systemic role during downturns. These findings underscore the asymmetric nature of shock transmission, offering critical insights for portfolio diversification, hedging strategies, and systemic risk monitoring in a globally integrated financial environment.
References
- Aizenman, J., Chinn, M. D., & Ito, H. (2016). Monetary policy spillovers and the trilemma in the new normal: Periphery country sensitivity to core country conditions. Journal of International Money and Finance, 68, 298-330.
- Aloui, C., & Hkiri, B. (2014). Co-movements of GCC emerging stock markets: New evidence from wavelet coherence analysis. Economic Model-ling, 36, 421-431.
- Alshater, M. M., Alqaralleh, H., & El Khoury, R. (2023). Dynamic asymmetric connectedness in technological sectors. The Journal of Economic Asymmetries, 27, e00287.
- Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13(4), 84.
- Antonakakis, N., Gabauer, D., Gupta, R., & Plakandaras, V. (2018). Dynamic connectedness of uncertainty across developed economies: A time-varying approach. Economics letters, 166, 63-75.
- Answer, Z., Naeem, M. A., Hassan, M. K., & Karim, S. (2022). Asymmetric connectedness across Asia-Pacific currencies: Evidence from time-frequency domain analysis. Finance Research Letters, 47, 102782.
- Apergis, N., Baruník, J., & Lau, M. C. K. (2017). Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electrici-ty markets? Energy Economics, 66, 108-115.
- Baruník, J., Kočenda, E., & Vácha, L. (2016). Asymmetric connectedness on the US stock market: Bad and good volatility spillovers. Journal of Financial Markets, 27, 55-78.
- Baruník, J., & Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Economet-rics, 16(2), 271-296.
- Beirne, J., Caporale, G. M., Schulze-Ghattas, M., & Spagnolo, N. (2010). Global and regional spillovers in emerging stock markets: A multivariate GARCH-in-mean analysis. Emerging Markets Review, 11(3), 250-260.
- Bekiros, S., Boubaker, S., Nguyen, D. K., & Uddin, G. S. (2017). Black swan events and safe havens: The role of gold in globally integrated emerging markets. Journal of International Money and Finance, 73, 317-334.
- BenSaïda, A. (2019). Good and bad volatility spillovers: An asymmetric connectedness. Journal of Financial Markets, 43, 78-95.
- Bostanci, G., & Yilmaz, K. (2020). How connected is the global sovereign credit risk network? Journal of Banking & Finance, 113, 105761.
- Bouri, E., Shahzad, S. J. H., Roubaud, D., Kristoufek, L., & Lucey, B. (2020). Bitcoin, gold, and commodities as safe havens for stocks: New in-sight through wavelet analysis. The Quarterly Review of Economics and Finance, 77, 156-164.
- Chatziantoniou, I., & Gabauer, D. (2021). EMU risk-synchronisation and financial fragility through the prism of dynamic connectedness. The Quar-terly Review of Economics and Finance, 79, 1-14.
- Demirer, R., Ferrer, R., & Shahzad, S. J. H. (2020). Oil price shocks, global financial markets and their connectedness. Energy Economics, 88, 104771.
- Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of forecasting, 28(1), 57-66.
- Égert, B., & Kočenda, E. (2007). Interdependence between Eastern and Western European stock markets: Evidence from intraday data. Economic Systems, 31(2), 184-203.
- Ferreira, M. A., & Gama, P. M. (2007). Does sovereign debt ratings news spill over to international stock markets? Journal of Banking & Finance, 31(10), 3162-3182.
- Gabauer, D. (2021). Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system. Journal of Multinational Financial Management, 60, 100680.
- Graham, M., Kiviaho, J., & Nikkinen, J. (2012). Integration of 22 emerging stock markets: A three-dimensional analysis. Global Finance Journal, 23(1), 34-47.
- Hung, N. T. (2022). Asymmetric connectedness among S&P 500, crude oil, gold and Bitcoin. Managerial Finance, 48(4), 587-610.
- IEA. (2025). Global Trends. https://www.iea.org/reports/global-energy-review-2025/global-trends
- Kao, L.-J., Chiu, C.-C., Lu, C.-J., & Chang, C.-H. (2013). A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting. Decision Support Systems, 54(3), 1228-1244.
- Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of econometrics, 74(1), 119-147.
- Li, H. (2007). International linkages of the Chinese stock exchanges: A multivariate GARCH analysis. Applied Financial Economics, 17(4), 285-297.
- Li, Y., & Giles, D. E. (2015). Modelling volatility spillover effects between developed stock markets and Asian emerging stock markets. Interna-tional Journal of Finance & Economics, 20(2), 155-177.
- Lin, C.-H. (2012). The comovement between exchange rates and stock prices in the Asian emerging markets. International Review of Economics & Finance, 22(1), 161-172.
- Lin, Y., Yan, Y., Xu, J., Liao, Y., & Ma, F. (2021). Forecasting stock index price using the CEEMDAN-LSTM model. The North American Journal of Economics and Finance, 57, 101421.
- Majdoub, J., & Mansour, W. (2014). Islamic equity market integration and volatility spillover between emerging and US stock markets. The North American Journal of Economics and Finance, 29, 452-470.
- Mensi, W., Hammoudeh, S., Nguyen, D. K., & Kang, S. H. (2016). Global financial crisis and spillover effects among the US and BRICS stock markets. International Review of Economics & Finance, 42, 257-276.
- Mensi, W., Hkiri, B., Al-Yahyaee, K. H., & Kang, S. H. (2018). Analyzing time–frequency co-movements across gold and oil prices with BRICS stock markets: A VaR based on wavelet approach. International Review of Economics & Finance, 54, 74-102.
- Mensi, W., Rehman, M. U., 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.
- Mikhaylov, A. Y. (2018). Pricing in oil market and using probit model for analysis of stock market effects. International Journal of Energy Econom-ics and Policy, 8(2), 69-73.
- Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics letters, 58(1), 17-29.
- Saleh, H. M., & Hassan, A. I. (2024). The challenges of sustainable energy transition: A focus on renewable energy. Applied Chemical Engineering, 7(2), 2084.
- Shaik, M., & Rehman, M. Z. (2023). The dynamic volatility connectedness of major environmental, social, and governance (ESG) stock indices: Evidence based on DCC-GARCH model. Asia-Pacific Financial Markets, 30(1), 231-246.
- Singh, P., Kumar, B., & Pandey, A. (2010). Price and volatility spillovers across North American, European and Asian stock markets. International Review of Financial Analysis, 19(1), 55-64.
- Tsai, I.-C. (2017). The source of global stock market risk: A viewpoint of economic policy uncertainty. Economic Modelling, 60, 122-131.
- Yarovaya, L., Brzeszczyński, J., & Lau, C. K. M. (2016). Intra-and inter-regional return and volatility spillovers across emerging and developed markets: Evidence from stock indices and stock index futures. International Review of Financial Analysis, 43, 96-114.
Downloads
How to Cite
Received date: July 25, 2025
Accepted date: August 7, 2025
Published date: August 14, 2025