Currency Market Contagion in the Next-11: An Application of EGARCH-BEKK Analysis

Muhammad Zia ur Rehman; Zain ul Abidin; Muhammad Mohsin; Sajjad Ahmad Baig

doi:10.62270/jirms.v6i2.112

Authors

Muhammad Zia ur Rehman Faisalabad Business School, National Textile University, Faisalabad, Pakistan https://orcid.org/0000-0003-3770-5883
Zain ul Abidin Faisalabad Business School, National Textile University, Faisalabad, Pakistan https://orcid.org/0000-0003-0716-1790
Muhammad Mohsin Hunan University of Humanities, Science and Technology, China https://orcid.org/0000-0003-2867-6910
Sajjad Ahmad Baig Faisalabad Business School, National Textile University, Faisalabad, Pakistan https://orcid.org/0009-0006-7450-1459

DOI:

https://doi.org/10.62270/jirms.v6i2.112

Keywords:

EGARCH Model, BEKK-GARCH, Currency Contagion, Next-11 Economies, Exchange Rate Volatility, Crisis Transmission, Emerging Markets

Abstract

Purpose—This study investigates volatility spillover effects among the Next-11 Countries' foreign exchange markets using advanced econometric methodologies. The aim is to check the contagion of the volatility of Next-11 emerging economies to provide stakeholders a better understanding while making policies for trading.

Methodology—We employ a Multivariate EGARCH model to examine volatility spillover patterns and a Diagonal-BEKK model to assess cross-market shock transmission effects. The analysis utilizes daily foreign exchange data spanning from January 3, 2000, to December 31, 2024, comprising approximately 6,248 observations across eleven emerging economies. The period of time is chosen to attend some of the major crisis periods so that the volatility pattern could be understood in these crises, and a forecast for future turmoil could be accessed.

Findings—Results demonstrate that Egypt, Indonesia, and Vietnam exhibit the strongest volatility transmission effects, with their previous return volatility significantly impacting all other Next-11 markets (p < 0.05). The BEKK model reveals significant bidirectional shock transmission across 87% of market pairs, indicating high interconnectedness among these emerging foreign exchange markets.

Implications—These findings provide crucial insights for international portfolio managers, currency traders, and policymakers in managing cross-border financial risks and developing effective hedging strategies in emerging market currencies. The study will also be very helpful for these economies in making trade policies with each other and creating more efficient ways to trade.

Originality/value—The work done is an original idea and is a literature-backed gap identified in the field of International Financial Management.

References

Abdelmalek, W., & Ben Abdallah, F. (2025). Correlations and volatility spillovers between clean and dirty cryptocurrencies and regional stock markets: new evidence from COVID-19 and Russia–Ukraine conflict. Studies in Economics and Finance. DOI: https://doi.org/10.1108/SEF-01-2024-0051

Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2019). Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios. Journal of International Financial Markets, Institutions and Money, 61, 37-51. DOI: https://doi.org/10.1016/j.intfin.2019.02.003

Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of applied econometrics, 18(1), 1-22. DOI: https://doi.org/10.1002/jae.659

Baker, S. R., Bloom, N., Davis, S. J., Kost, K., Sammon, M., & Viratyosin, T. (2020). The unprecedented stock market reaction to COVID-19. The review of asset pricing studies, 10(4), 742-758. DOI: https://doi.org/10.1093/rapstu/raaa008

Baruník, J., & Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296. DOI: https://doi.org/10.1093/jjfinec/nby001

Bauwens, L., Laurent, S., & Rombouts, J. V. (2006). Multivariate GARCH models: a survey. Journal of applied econometrics, 21(1), 79-109. DOI: https://doi.org/10.1002/jae.842

Bekaert, G., & Harvey, C. R. (1997). Emerging equity market volatility. Journal of Financial economics, 43(1), 29-77. DOI: https://doi.org/10.1016/S0304-405X(96)00889-6

Bubák, V., Kočenda, E., & Žikeš, F. (2011). Volatility transmission in emerging European foreign exchange markets. Journal of Banking & Finance, 35(11), 2829-2841. DOI: https://doi.org/10.1016/j.jbankfin.2011.03.012

Calvo, G. A., & Reinhart, C. M. (2002). Fear of floating. The Quarterly journal of economics, 117(2), 379-408. DOI: https://doi.org/10.1162/003355302753650274

Chancharat, S., & Sinlapates, P. (2024). Asymmetric volatility spillovers between Bitcoin, oil, and global stocks in economic uncertainty. International Journal of Monetary Economics and Finance, 17(2-3), 238-246. DOI: https://doi.org/10.1504/IJMEF.2024.139041

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. DOI: https://doi.org/10.1016/j.ijforecast.2011.02.006

Eklou, K. M. (2023). Dollar exchange rate volatility and productivity growth in emerging markets: Evidence from firm level data. International Monetary Fund. DOI: https://doi.org/10.2139/ssrn.4484773

Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 11(1), 122-150. DOI: https://doi.org/10.1017/S0266466600009063

Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The journal of Finance, 57(5), 2223-2261. DOI: https://doi.org/10.1111/0022-1082.00494

Fratzscher, M. (2012). Capital flows, push versus pull factors and the global financial crisis. Journal of international Economics, 88(2), 341-356. DOI: https://doi.org/10.1016/j.jinteco.2012.05.003

Greenwood-Nimmo, M., Steenkamp, D., & Van Jaarsveld, R. (2025). Risk and return spillovers among developed and emerging market currencies. Journal of International Financial Markets, Institutions and Money, 98, 102086. DOI: https://doi.org/10.1016/j.intfin.2024.102086

Hung, N. T., Nguyen, L. T. M., & Vo, X. V. (2022). Exchange rate volatility connectedness during Covid-19 outbreak: DECO-GARCH and Transfer Entropy approaches. Journal of International Financial Markets, Institutions and Money, 81, 101628. DOI: https://doi.org/10.1016/j.intfin.2022.101628

Hussain, I., Ali, N., Ahmad, H. B., & Ashraf, S. (2024). Volatility spillover effect between cryptocurrency and stock market using MGARCH BEKK model. Natural and Applied Sciences International Journal (NASIJ), 5(2), 32-55. DOI: https://doi.org/10.47264/idea.nasij/5.2.3

Hussain, M., Bashir, U., & Rehman, R. U. (2024). Exchange rate and stock prices volatility connectedness and spillover during pandemic induced-crises: Evidence from BRICS countries. Asia-Pacific Financial Markets, 31(1), 183-203. DOI: https://doi.org/10.1007/s10690-023-09411-0

Iuga, I. C., Nerișanu, R.-A., & Dragolea, L.-L. (2024). Volatility and spillover analysis between cryptocurrencies and financial indices: a diagonal BEKK and DCC GARCH model approach in support of SDGs. Cogent Economics & Finance, 12(1), 2437002. DOI: https://doi.org/10.1080/23322039.2024.2437002

Kanas, A. (2000). Volatility spillovers between stock returns and exchange rate changes: International evidence. Journal of business finance & accounting, 27(3‐4), 447-467. DOI: https://doi.org/10.1111/1468-5957.00320

Kayani, U., Khan, M., Nawaz, F., Nasseredine, H., & Dhabi, A. (2024). 危机会影响回报溢出吗?: 来自亚太发达股市的证据.

Khan, M., Kayani, U. N., Khan, M., Mughal, K. S., & Haseeb, M. (2023). COVID-19 pandemic & financial market volatility; evidence from GARCH models. Journal of Risk and Financial Management, 16(1), 50. DOI: https://doi.org/10.3390/jrfm16010050

Khurshid, M., Azeem, M., & Ahmad, N. (2023). Volatility Spillovers From The Japanese Stock Market To Emerging Stock Markets. Bulletin of Business and Economics (BBE), 12(2), 118-125.

Kumar, A. S., & Anandarao, S. (2019). Volatility spillover in crypto-currency markets: Some evidences from GARCH and wavelet analysis. Physica A: statistical mechanics and its applications, 524, 448-458. DOI: https://doi.org/10.1016/j.physa.2019.04.154

Li, X., Liang, C., & Ma, F. (2023). Financial stress spillover network across Asian countries in the context of COVID-19. Applied Economics Letters, 30(7), 965-974. DOI: https://doi.org/10.1080/13504851.2022.2030852

Ma, J., Feng, J., Chen, J., & Zhang, J. (2024). Volatility Spillover from Carbon Prices to Stock Prices: Evidence from China’s Carbon Emission Trading Markets. Journal of Risk and Financial Management, 17(3), 123. DOI: https://doi.org/10.3390/jrfm17030123

Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of business, 36(4), 394. DOI: https://doi.org/10.1086/294632

Melas, K. D., Faitatzoglou, A., Michail, N., & Artemiou, A. (2024). Volatility Spillovers among the Major Commodities: A Review. Journal of Risk and Financial Management, 17. DOI: https://doi.org/10.2139/ssrn.4813830

Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the econometric society, 347-370. DOI: https://doi.org/10.2307/2938260

Salim, K., Disli, M., Nagayev, R., Ilyas, A., & Aysan, A. F. (2024). A Pandemic's grip: Volatility spillovers in Asia-Pacific equity markets during the onset of Covid-19. Borsa Istanbul Review, 24(5), 898-907. DOI: https://doi.org/10.1016/j.bir.2024.05.001

Tabash, M. I., Chalissery, N., Nishad, T. M., & Al-Absy, M. S. M. (2024). Market shocks and stock volatility: Evidence from emerging and developed markets. International Journal of Financial Studies, 12(1), 2. DOI: https://doi.org/10.3390/ijfs12010002

Tse, Y. K., & Tsui, A. K. C. (2002). A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business & Economic Statistics, 20(3), 351-362. DOI: https://doi.org/10.1198/073500102288618496

Wang, Y., Xiang, Y., Lei, X., & Zhou, Y. (2022). Volatility analysis based on GARCH-type models: Evidence from the Chinese stock market. Economic research-Ekonomska istraživanja, 35(1), 2530-2554. DOI: https://doi.org/10.1080/1331677X.2021.1967771

Yıldız, N. B. (2024). VOLATILITY SPILLOVER EFFECTS BETWEEN STOCK MARKETS DURING THE CRISIS PERIODS: DIAGONAL BEKK APPROACH. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(2024), 229-246.

Zhang, Y., Lo, S.-t., & Sutthiphisal, D. (2025). Inter-Market Mean and Volatility Spillover Dynamics Between Cryptocurrencies and an Emerging Stock Market: Evidence from Thailand and Sectoral Analysis. Risks, 13(4). DOI: https://doi.org/10.3390/risks13040077