Dynamics of currency exchange rates co-movements and volatility: Indian rupee against major trading currencies
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DOIhttp://dx.doi.org/10.21511/bbs.21(1).2026.09
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Article InfoVolume 21 2026, Issue #1, pp. 110-125
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Type of the article: Research Article
Abstract
Foreign exchange markets have intrigued not only corporations engaged in export and import, but also individuals and other entities seeking to achieve decent risk-adjusted returns and protect themselves from future currency exchange rate exposure. Hence, researchers are drawn to examine the volatility of returns and identify diversification and hedging opportunities to mitigate country and financial risks of the five largest trading currencies with respect to the Indian currency, the rupee. The study used historical daily exchange rate data for the Indian currency with respect to American dollar, euro, British pound, Japanese yen, and Australian dollar, spanning from January 1, 2008 to December 31, 2025.
American dollar has the highest average daily return among the five currencies, followed closely by euro and pound. Pound exhibits the highest standard deviation, and its volatility suggests greater uncertainty for investors dealing in these transactions. High correlations between dollar-euro and euro-pound indicate that they are influenced by similar economic factors or market sentiments. Frequent structural breaks highlight the possibility for currency exchange rates to shift dramatically due to unforeseen events. This is a crucial insight for risk management, as it signals the need for dynamic hedging strategies that can adapt to sudden changes in market conditions. Investors and policymakers can leverage these findings to optimize currency portfolios and reduce financial risk, especially when seeking diversification benefits and long-term stability amidst global market shifts.
- Keywords
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JEL Classification (Paper profile tab)F31, G15, C58
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References51
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Tables5
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Figures2
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- Figure A1. Volatility clustering on return data
- Figure A2. Graphical representation of all five countries’ exchange rates with respect to India
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- Table 1. Descriptive analysis of five major traded currencies with respect to the Indian rupee
- Table 2. Correlation matrix
- Table 3. Structure break analysis of the five exchange rates
- Table 4. Unit root test and ARCH effect
- Table 5. ARIMA(1,1) & GARCH(1,1) model for top five trading currencies with respect to INR
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- Abdullahi, S. A., Kouhy, R., & Muhammad, Z. (2014). Trading volume and return relationship in the crude oil futures markets. Studies in Economics and Finance, 31(4), 426-438.
- Ahmed, Y. N., Alnafissa, M., Negm, M. M., Gharieb, Y. M., Algarini, A., & Hassouba, T. A. A. (2024). Analyzing Exchange Rate Effects on Trade: Empirical Evidence. Sustainability, 16(10), 4177.
- 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, 102782.
- Asteriou, D., Katsikas, E., & Spanos, K. (2025). An Optimum Currency Area Index for BRICS: A Bayesian Prediction Model. Economic Modelling, 155, 107396.
- Awadzie, D. M., Attah-Botchwey, E., & Mawudor, B. G. (2024). Determinants of exchange rate threshold effect on economic growth: Evidence from Ghana. African Journal of Economic and Management Studies, 16(1), 47-60.
- Bettendorf, T., & Heinlein, R. (2023). Connectedness between G10 currencies: Searching for the causal structure. International Journal of Finance & Economics, 28(4), 3938-3959.
- Bhatia, S., & Tuteja, D. (2024). Contagion and linkages across international currencies. International Review of Financial Analysis, 94, 103301.
- 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, 102505.
- Boonman, T. M., & Fittje, J. C. (2024). Connectedness in exchange rates and news sentiment in the Asia-Pacific region. International Journal of Finance & Economics, 30(3), 2389-2406.
- 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, 101605.
- Carsamer, E. (2016). The pattern of exchange rate co-movement in selected African countries. Journal of Economic Studies, 43(6), 928-953.
- Chow, H. K. (2020). Connectedness of Asia Pacific forex markets: China’s growing influence. International Journal of Finance & Economics, 26(3), 3807-3818.
- Chu, M. (2020). Wavelet Analysis of the Euro and its Co-Movement with Four Exchange Rates. Eurasian Journal of Social Sciences, 8(3), 123-133.
- Chuang, W. I., Liu, H. H., & Susmel, R. (2012). The bivariate GARCH approach to investigating the relation between stock returns, trading volume, and return volatility. Global Finance Journal, 23(1), 1-15.
- Dai, Y., Yu, C., Xu, X., Zhou, J., & Teng, F. (2024). The macro driving factors of co-movement of RMB with other currencies in FX markets. International Review of Financial Analysis, 96, 103581.
- Das, S., & Roy, S. S. (2023). Following the leaders? A study of co-movement and volatility spillover in BRICS currencies. Economic Systems, 47(2), 100980.
- Djemo, C. R. T., & Eita, J. H. (2024). Modelling foreign exchange rate co-movement and its spatial dependence in emerging markets: A spatial econometrics approach. Empirical Economics, 66(3), 979-1011.
- Fang, S., Wei, Y., & Wang, S. (2024). 30 years of exchange rate analysis and forecasting: a bibliometric review. Journal of Economic Surveys, 38(3), 973-1007.
- Fasanya, I. O., Oyewole, O., Adekoya, O. B., & Odei-Mensah, J. (2021). Dynamic spillovers and connectedness between COVID-19 pandemic and global foreign exchange markets. Economic Research-Ekonomska Istraživanja, 34(1), 2059-2084.
- Gunay, S. (2021). Comparing COVID-19 with the GFC: A shockwave analysis of currency markets. Research in International Business and Finance, 56, 101377.
- He, S., Cheng, Z., Wang, W., & Luo, Z. (2024). What drives currency connectedness? Evidence from the BRICS currencies. Applied Economics, 57(1), 67-85.
- He, X., Kinkyo, T., & Hamori, S. (2025). Currency cluster and volatility co-movement: The role of common global factors. The Singapore Economic Review, 1-29.
- Huynh, T. L. D., 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.
- Inagaki, K. (2007). Testing for volatility spillover between the British pound and the euro. Research in International Business and Finance, 21(2), 161-174.
- Jiang, X., Han, L., & Yin, L. (2019). Can skewness predict currency excess returns? The North American Journal of Economics and Finance, 48, 628-641.
- Kakran, S., Bajaj, P. K., Pandey, D. K., & Kumar, A. (2025). Interconnectedness and return spillover among APEC currency exchange rates: A time-frequency analysis. Research in International Business and Finance, 73, 102572.
- Karatas, C., & Unal, G. (2021). Co-movement, fractal behaviour and forecasting of exchange rates. International Journal of Dynamics and Control, 9(4), 1818-1831.
- Kim, Y. M., & Lee, S. (2023). Spillover shifts in the FX market: Implication for the behavior of a safe haven currency. The North American Journal of Economics and Finance, 65, 101885.
- Kočenda, E., & Moravcová, M. (2019). Exchange rate comovements, hedging and volatility spillovers on new EU forex markets. Journal of International Financial Markets, Institutions and Money, 58, 42-64.
- Kumar, M. (2013). Returns and volatility spillover between stock prices and exchange rates: Empirical evidence from IBSA countries. International Journal of Emerging Markets, 8(2), 108-128.
- Kumar, M., Patil, A. A., Randive, K., & Gaurav, K. (2025). Influence of key economic factors on exchange rate using vector error correction method: The case of India. Investment Management and Financial Innovations, 22(2), 279-292.
- Kumar, S. (2017). Revisiting the price-volume relationship: A cross-currency evidence. International Journal of Managerial Finance, 13(1), 91-104.
- Kumar, S. (2019). The relationship between trading volume and exchange rate volatility: Linear or nonlinear? International Journal of Managerial Finance, 15(1), 19-38.
- Lee, T., Moutzouris, I. C., Papapostolou, N. C., & Fatouh, M. (2024). Foreign exchange hedging using regime-switching models: The case of pound sterling. International Journal of Finance & Economics, 29(4), 4813-4835.
- Liu, M., Liu, H. F., & Liu, S. (2024). Dynamic volatility spillovers between currencies of ASEAN member countries and China: Evidence from TVP-VAR approach. The Singapore Economic Review, 70(2), 1-25.
- Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. The Journal of Business, 36(4), 394-419.
- Marisetty, N. (2024). Assessing currency market equilibrium: Cointegration and correlation analysis of USD/INR and major global currencies. International Journal of Research in Finance and Management, 7(2), 396-406.
- Mittal, A., Sehgal, S., Mittal, A., & McMillan, D. (2019). Dynamic currency linkages between select emerging market economies: An empirical study. Cogent Economics & Finance, 7(1).
- Mo, W. S., Yang, J. J., & Chen, Y. L. (2023). Exchange rate spillover, carry trades, and the COVID-19 pandemic. Economic Modelling, 121, 106222.
- Mougoué, M., & Aggarwal, R. (2011). Trading volume and exchange rate volatility: Evidence for the sequential arrival of information hypothesis. Journal of Banking & Finance, 35(10), 2690-2703.
- 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.
- Park, B. J. (2011). Asymmetric herding as a source of asymmetric return volatility. Journal of Banking & Finance, 35(10), 2657-2665.
- Qureshi, S., Aftab, M., & Hegerty, S. (2023). The interdependence of foreign exchange vulnerability in emerging markets. Asia-Pacific Journal of Business Administration, 15(2), 203-224.
- Sadhwani, R. (2020). Cointegration analysis of selected currency pairs traded in Indian foreign exchange market. International Journal of Management, 11(5), 476-485.
- Sibande, X., Gupta, R., Demirer, R., & Bouri, E. (2021). Investor Sentiment and (Anti) Herding in the Currency Market: Evidence from Twitter Feed Data. Journal of Behavioral Finance, 24(1), 56-72.
- Singh, S., Kumar, S., Sharma, J., Mandadapu, P., & Tondapu, N. (2026). Analyzing Currency Fluctuations: A Comparative Study of GARCH, EWMA, and IV Models for GBP/USD and EUR/GBP Pairs. In Saraswat, M., Rajan, A., Chakravorty, A. (Eds.), Congress on Smart Computing Technologies (pp. 283-304). Singapore: Springer Nature Singapore.
- Vo, H. L., & Vo, D. H. (2023). The purchasing power parity and exchange-rate economics half a century on. Journal of Economic Surveys, 37(2), 446-479.
- Wang, M., Liu, J., & Yang, B. (2024). Does the strength of the US dollar affect the interdependence among currency exchange rates of RCEP and CPTPP countries? Finance Research Letters, 62, 105110.
- Wen, T., & Wang, G. J. (2020). Volatility connectedness in global foreign exchange markets. Journal of Multinational Financial Management, 54, 100617.
- Yeboah, S. D., Owusu Junior, P., Idun, A. A. A., Akorsu, P. K., & Fumey, M. P. (2025). Dynamic interdependence of major currencies and the US dollar: a wavelet coherence approach. Future Business Journal, 11(1), 222.
- Zhang, Z., & Zhang, T. (2022). Has the dependence structure of the BRICS exchange rates changed after the financial crisis? Evidence from R-Vine copula model. International Journal of Emerging Markets, 17(10), 2490-2509.


