Modeling Asymmetric Volatility Connectedness and Spillover Dynamics in Emerging Forex Markets Through the Lens of a TVP-VAR Framework
DOI:
https://doi.org/10.17010/ijf/2025/v19i10/175677Keywords:
volatility, asymmetric, connectedness, spillovers, emerging forex market, TVP-VAR.JEL Classification Codes : C58, F31, F36, F65, G15
Publication Chronology: Paper Submission Date : May 15, 2025 ; Paper sent back for Revision : July 25, 2025 ; Paper Acceptance Date : September 20, 2025 ; Paper Published Online : October 15, 2025
Abstract
Purpose : This article investigated asymmetric volatility connectedness and spillover dynamics in emerging forex markets, emphasizing BRICS countries, amid recent geopolitical and trade-tariff conflicts.
Methodology : The analysis employed the recently introduced TVP-VAR framework by Antonakakis et al. (2020) on daily exchange rate data from April 2015 to March 2025, sourced from Investing.com.
Findings : The study uncovered several key insights. Based on realized variance, the total connectedness index stood at 24.02, reflecting weak connectedness with South Africa and Brazil as net transmitters and Russia, China, and India as net receivers. Consistent with prior evidence, connectedness intensified in crises and weakened during stable periods. The spillover asymmetric measure highlighted the dominance of positive volatility, where RS+ was revealed to be low and RS– was suggested to have weak connectedness. Conversely, cross-market connectedness, estimated at 33.87, within a 2N × 2N dimensional framework, confirmed the dominance of bad volatility. Additionally, network-connectedness analysis identified Russia’s pivotal position in transmitting volatility across BRICS forex markets.
Implications : The study contributed to the literature by applying advanced methodology to emerging economies. For investors, it provided actionable insights for portfolio and risk management strategies. For policymakers, the weak connectedness underscored the need to strengthen economic and financial integration to advance the de-dollarization agenda and enhance regional market resilience.
Originality : This study offered a novel perspective on volatility spillover dynamics in emerging foreign exchange markets and introduced a unique framework for ranking spillover indices.
Downloads
Published
How to Cite
Issue
Section
References
1) Agarwal, S. (2022). Deep learning in financial analytics: Exchange rate modelling. Indian Journal of Finance, 16(9), 8–25. https://doi.org/10.17010/ijf/2022/v16i9/172157
2) Aggarwal, S., & Khurana, S. (2018). Empirical examination of stock market volatility: An international comparison. Indian Journal of Finance, 12(1), 47–61. https://doi.org/10.17010/ijf/2018/v12i1/120741
3) Aghion, P., Bacchetta, P., Ranciere, R., & Rogoff, K. (2009). Exchange rate volatility and productivity growth: The role of financial development. Journal of Monetary Economics, 56(4), 494–513. https://doi.org/10.1016/j.jmoneco.2009.03.015
4) Amudha, R., & Muthukamu, M. (2018). Modeling symmetric and asymmetric volatility in the Indian stock market. Indian Journal of Finance, 12(11), 23–36. https://doi.org/10.17010/ijf/2018/v12i11/138196
5) Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885–905. https://doi.org/10.2307/2527343
6) 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. https://doi.org/10.3390/jrfm13040084
7) Antonakakis, N., Cunado, J., Filis, G., Gabauer, D., & de Gracia, F. P. (2023). Dynamic connectedness among the implied volatilities of oil prices and financial assets: New evidence of the COVID-19 pandemic. International Review of Economics & Finance, 83, 114–123. https://doi.org/10.1016/j.iref.2022.08.009
8) Anwer, 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(Part B), Article ID 102782. https://doi.org/10.1016/j.frl.2022.102782
9) Ari, Y., Kurt, H., & Uçak, H. (2025). Volatility spillovers among EAGLE economies: Insights from frequency-based TVP-VAR connectedness. Mathematics, 13(8), 1256. https://doi.org/10.3390/math13081256
10) Asadi, M., Roubaud, D., & Tiwari, A. K. (2022). Volatility spillovers amid crude oil, natural gas, coal, stock, and currency markets in the US and China based on time and frequency domain connectedness. Energy Economics, 109, Article ID 105961. https://doi.org/10.1016/j.eneco.2022.105961
11) Barndorff-Nielsen, O. E., & Shephard, N. (2001). Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics. Journal of the Royal Statistical Society Series B: Statistical Methodology, 63(2), 167–241. https://doi.org/10.1111/1467-9868.00282
12) Barndorff-Nielsen, O. E., Kinnebrock, S., & Shephard, N. (2010). Chapter 7: Measuring downside risk—realised semivariance. In T. Bollerslev, J. Russell, & M. Watson (eds.), Volatility and time series econometrics: Essays in honor of Robert Engle (pp. 117–136). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199549498.003.0007
13) 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. https://doi.org/10.1016/j.finmar.2015.09.003
14) Baruník, J., Kočenda, E., & Vácha, L. (2017). Asymmetric volatility connectedness on the forex market. Journal of International Money and Finance, 77, 39–56. https://doi.org/10.1016/j.jimonfin.2017.06.003
15) Baruník, J., & Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271–296. https://doi.org/10.1093/jjfinec/nby001
16) Bhanumurthy, K. V., Singh, A. K., & Aggarwal, A. (2019). Macroeconomic antecedents of stock returns and exchange rate. Indian Journal of Finance, 13(6), 29–53. https://doi.org/10.17010/ijf/2019/v13i6/144848
17) Black, F. (1976). Studies of stock market volatility changes. In Proceedings of the 1976 Meeting of the Business and Economic Statistics Section (pp. 177–181). American Statistical Association. https://www.scirp.org/reference/ReferencesPapers?ReferenceID=1969803
18) Boakye, R. O., Mensah, L. K., Kang, S. H., & Osei, K. A. (2023). Foreign exchange market return spillovers and connectedness among African countries. International Review of Financial Analysis, 86, Article ID 102505. https://doi.org/10.1016/j.irfa.2023.102505
19) Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
20) Bouri, E., Lucey, B., Saeed, T., & Vo, X. V. (2020). Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis. International Review of Financial Analysis, 72, Article ID 101605. https://doi.org/10.1016/j.irfa.2020.101605
21) Chawla, S., & Sharma, P. (2014). A macroeconomic approach to foreign investment flows in India. Indian Journal of Finance, 8(5), 42–49. https://doi.org/10.17010/ijf/2014/v8i5/71917
22) Ciner, C. (2011). Information transmission across currency futures markets: Evidence from frequency domain tests. International Review of Financial Analysis, 20(3), 134–139. https://doi.org/10.1016/j.irfa.2011.02.010
23) Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
24) 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. https://doi.org/10.1016/j.ijforecast.2011.02.006
25) Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. https://doi.org/10.1016/j.jeconom.2014.04.012
26) Diko, N., & Sempijja, N. (2021). Does participation in BRICS foster South-South cooperation? Brazil, South Africa, and the Global South. Journal of Contemporary African Studies, 39(1), 151–167. https://doi.org/10.1080/02589001.2020.1837746
27) 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
28) Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38(1), 34–105. http://www.jstor.org/stable/2350752
29) Fernández-Rodríguez, F., & Sosvilla-Rivero, S. (2020). Volatility transmission between stock and foreign exchange markets: A connectedness analysis. Applied Economics, 52(19), 2096–2108. https://doi.org/10.1080/00036846.2019.1683143
30) 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, Article ID 100680. https://doi.org/10.1016/j.mulfin.2021.100680
31) Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48(5), 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
32) 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. https://doi.org/10.1007/s10690-023-09411-0
33) Huynh, T. L., Nasir, M. A., & Nguyen, D. K. (2023). Spillovers and connectedness in foreign exchange markets: The role of trade policy uncertainty. The Quarterly Review of Economics and Finance, 87, 191–199. https://doi.org/10.1016/j.qref.2020.09.001
34) Karim, S., & Naeem, M. A. (2022). Do global factors drive the interconnectedness among green, Islamic and conventional financial markets? International Journal of Managerial Finance, 18(4), 639–660. https://doi.org/10.1108/ijmf-09-2021-0407
35) Kaur, H., & Singh, R. (2019). Modelling volatility clustering and asymmetry: A study of Indian index futures markets. Indian Journal of Finance, 13(3), 51–65. https://doi.org/10.17010/ijf/2019/v13i3/142269
36) Khuntia, S., & Pattanayak, J. K. (2017). Dynamics of Indian Foreign Exchange market efficiency: An adaptive market hypothesis approach. Indian Journal of Finance, 11(9), 39–52. https://doi.org/10.17010/ijf/2017/v11i9/118088
37) Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4
38) Kumar, R. (2011). Governance and trusteeship philosophy in the 2G saga. Prabandhan: Indian Journal of Management, 4(4), 12–22. https://doi.org/10.17010/pijom/2011/v4i4/62411
39) Kumar, R., & Chandel, K. (2010). Nurturing corporate governance with the wisdom of the Gita: A gateway for good governance. Prabandhan: Indian Journal of Management, 3(7), 3–11. https://doi.org/10.17010/pijom/2010/v3i7/61059
40) Lee, W. S., & Lee, H. S. (2022). Asymmetric volatility transmission across Northeast Asian stock markets. Borsa Istanbul Review, 22(2), 341–351. https://doi.org/10.1016/j.bir.2021.06.004
41) Macdonald, R., & Marsh, J. (2004). Missing school: Educational engagement, youth transitions, and social exclusion. Youth & Society, 36(2), 143–162. https://doi.org/10.1177/0044118X04265156
42) Malik, F., & Umar, Z. (2019). Dynamic connectedness of oil price shocks and exchange rates. Energy Economics, 84, Article ID 104501. https://doi.org/10.1016/j.eneco.2019.104501
43) Mandelbrot, B. (1963). New methods in statistical economics. Journal of Political Economy, 71(5), 421–440. https://doi.org/10.1086/258792
44) Melath, R., & Samsudheen, K. (2025). Crises unveiled: Investigating the interplay between exchange rates and oil prices in global economic turmoil based on TVP-VAR model. Journal of Financial Economic Policy. https://doi.org/10.1108/jfep-12-2023-0396
45) Mensi, W., Maitra, D., Vo, X. V., & Kang, S. H. (2021). Asymmetric volatility connectedness among main international stock markets: A high frequency analysis. Borsa Istanbul Review, 21(3), 291–306. https://doi.org/10.1016/j.bir.2020.12.003
46) Mensi, W., Shafiullah, M., Vo, X. V., & Kang, S. H. (2022). Asymmetric spillovers and connectedness between crude oil and currency markets using high-frequency data. Resources Policy, 77, Article ID 102678. https://doi.org/10.1016/j.resourpol.2022.102678
47) Mishra, A. (2019). Foreign exchange, gold, and real estate markets in India: An analysis of return volatility and transmission. Indian Journal of Finance, 13(7), 50–64. https://doi.org/10.17010/ijf/2019/v13i7/145535
48) Naeem, M. A., Anwer, Z., Karim, S., & Tiwari, A. K. (2023). Are exchange rate contagions asymmetric? Evidence from emerging market economies. Emerging Markets Finance and Trade, 59(15), 4107–4124. https://doi.org/10.1080/1540496X.2023.2192347
49) Nagina, R. (2022). The nexus of stock markets among BRICS nations: An empirical analysis pre and post spread of the COVID-19 pandemic. Indian Journal of Finance, 16(10), 24–42. https://doi.org/10.17010/ijf/2022/v16i10/172386
50) Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370. https://doi.org/10.2307/2938260
51) Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. https://doi.org/10.1016/s0165-1765(97)00214-0
52) Polat, O. (2019). Systemic risk contagion in FX market: A frequency connectedness and network analysis. Bulletin of Economic Research, 71(4), 585–598. https://doi.org/10.1111/boer.12197
53) Rai, S. K., & Sharma, A. K. (2022). Do lagged terms affect exchange rate volatility in India? An analysis using ARIMA model. Indian Journal of Finance, 16(10), 8–23. https://doi.org/10.17010/ijf/2022/v16i10/172385
54) Reinhart, C. M., & Rogoff, K. S. (2008). Is the 2007 US sub-prime financial crisis so different? An international historical comparison. American Economic Review, 98(2), 339–344. https://doi.org/10.1257/aer.98.2.339
55) Rose, A. K. (2011). Exchange rate regimes in the modern era: Fixed, floating, and flaky. Journal of Economic Literature, 49(3), 652–672. https://doi.org/10.1257/jel.49.3.652
56) Rubaszek, M., Szafranek, K., & Uddin, G. S. (2025). Intraday volatility connectedness on the forex market: The role of uncertainty. Journal of International Money and Finance, 157, Article ID 103398. https://doi.org/10.1016/j.jimonfin.2025.103398
57) Salisu, A. A., Oyewole, O. J., & Fasanya, I. O. (2018). Modelling return and volatility spillovers in global foreign exchange markets. Journal of Information and Optimization Sciences, 39(7), 1417–1448. https://doi.org/10.1080/02522667.2017.1367507
58) Singh, R. K., Kumari, J., & Kumar, A. (2021). Gravity model analysis of India's trade linkages with ASEAN countries during pre and post AIFTA regime. Himachal Pradesh University Journal, 9(1), 60–82. https://www.researchgate.net/publication/365131420
59) Singh, R. K. (2013). Corporate social responsibility: A business solution for sustainable development and inclusive development, Prabandhan: Indian Journal of Management, 6(12), 5–17. https://doi.org/10.17010/pijom/2013/v6i12/60049
60) Singh, R. K., & Kumar, A. (2019). Analysis of Indo-BIMSTEC trade relations: Opportunities and challenges. Himachal Pradesh University Journal, 7(1), 50–69. https://www.researchgate.net/profile/Ajay-Kumar-575/publication/365131326
61) Singh, R. K., Chauhan, A. K., & Kumar, A. (2023a). Assessing India's trade relations and potential with BRICS countries using trade intensity and revealed comparative advantage approaches. International Journal of Research and Analytical Reviews,10(4), 502–516. https://www.researchgate.net/publication/376858998
62) Singh, R. K., Chauhan, A. K., & Kumar, A. (2023b). Mapping India's export relations with BRICS countries: Empirical evidence from gravity model theory. International Journal of Research and Analytical Reviews, 10(4), 591–604. https://www.researchgate.net/publication/376885884
63) Singh, R. K., Kumar, A., & Kumari, J. (2022). An empirical application of gravity model theory to Indo-BIMSTEC business relations. Journal of Polity and Society, 14(1), 119–137. https://journalspoliticalscience.com/index.php/i/article/view/6
64) Singh, R. K., Sharma, M., Kumar, A., & Singh, Y. (2022). Corporate governance mechanism - Financial performance nexus: Empirical evidence from Indian banks. Indian Journal of Finance, 16(7), 23–41. https://doi.org/10.17010/ijf/2022/v16i7/170633
65) Singh, R. K., Kumar, A., Kumari, J., & Singh, Y. (2022a). Empirical study of India's trade and investment relations with ASEAN countries: Evidence from the gravity model theory. Arthshastra Indian Journal of Economics & Research, 11(3), 8–28. https://doi.org/10.17010/aijer/2022/v11i3/172680
66) Singh, R. K., Kumar, A., Kumari, J., & Singh, Y. (2022b). Navigating determinants of India's business relations with GCC countries: Evidence from the gravity model theory. Prabandhan: Indian Journal of Management, 15(6), 8–27. https://doi.org/10.17010/pijom/2022/v15i6/170021
67) Singh, R. K., Kumar, A., & Singh, Y. (2024). Mapping the influence of the COVID-19 pandemic on the Indian economy: Insights from the smart PLS-SEM approach with IPMA. Indian Journal of Finance, 18(7), 25–43. https://doi.org/10.17010/ijf/2024/v18i7/174031
68) Singh, R. K., Kumar, N., Singh, Y., Kumar, A., & Kumar, S. (2025). Moderating role of stock return volatility in corporate governance firm value structural linkages: Insights from a PLS-SEM with IPMA approach. Indian Journal of Finance, 19(1), 8–32. https://doi.org/10.17010/ijf/2025/v19i1/174693
69) Singh, R. K., Singh, B., & Chauhan, D. (2018). India's trade relations with SAARC countries in the pre and post-SAFTA periods: An evaluation of trade flows by using multiple regression model. Arthshastra Indian Journal of Economics & Research, 7(6), 43–59. https://doi.org/10.17010/aijer/2018/v7i6/141433
70) Singh, R. K., Singh, Y., Kumar, S., Kumar, A., & Alruwaili, W. S. (2024). Mapping risk–return linkages and volatility spillover in BRICS stock markets through the lens of linear and non-linear GARCH models. Journal of Risk and Financial Management, 17(10), 437. https://doi.org/10.3390/jrfm17100437
71) Singh, R. K., Saxena, C., Aggarwal, S., Prerna, & Singh, Y. (2025). Bibliometric insights into the 'Indian Journal of Finance': Mapping intellectual contributions. Indian Journal of Finance, 19(5), 8–36. https://doi.org/10.17010/ijf/2025/v19i5/175042
72) Singh, A. K., & Shrivastav, R. K. (2018). Evaluating the impact of the global financial crisis on the stock markets of BRICS countries. Indian Journal of Finance, 12(7), 7–27. https://doi.org/10.17010/ijf/2018/v12i7/129967
73) Switzer, L. N., & Yue, H. (2019). Effects of the short sale circuit breaker on the stock market. Journal of International Financial Management & Accounting, 30(3), 250–274. https://doi.org/10.1111/jifm.12105
74) Yadav, S. (2016). Integration of exchange rate and stock market: Evidence from the Indian stock market. Indian Journal of Finance, 10(10), 56–63. https://doi.org/10.17010/ijf/2016/v10i10/103015
75) Zhou, W., Rao, W., & Luo, D. (2020). Policy effectiveness analysis of China's circuit breaker mechanism. Complexity. https://doi.org/10.1155/2020/9024723
