Journal Press India®

MUDRA: Journal of Finance and Accounting
Vol 13 , Issue 1 , January - June 2026 | Pages: 26-50 | Research Paper

Exploring Return and Volatility Spillovers across Traditional and Emerging Financial Markets using Quantile VAR Analysis

 
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Author Details ( * ) denotes Corresponding author

1. * Arup Brahma Mohapatra, Research Scholar, Department of Commerce, Berhampur University, Berhampur, Orissa, India (mohapatra02ab@gmail.com)

This study examines the return and volatility spillover among major financial markets; those are the Nifty 50, Dow Jones, Brent crude oil, Gold, USDINR, and Bitcoin. This study used Diebold and Yilmaz model based on Quantile Vector Autoregressive (here after called QVAR-D&Y) to examine how returns and volatility spillover happening among these markets under different market phases those are bearish, neutral, and bullish. The data were selected on weekly basis from March 2016 to February 2024.The findings reveal that during bullish markets Gold, Bitcoin, and USDINR act as key transmitters of volatility, reflecting their speculative and currency driven roles. On the other side, Gold continues to serve as a safe haven asset, especially during bearish market. Volatility transmission is notably high during bull market phase.

Keywords

Volatility; Spillover; Quantile VAR; Diebold & Yilmaz; Bitcoin

  1. Almeida, J. & Gonçalves, T. C. (2022). Portfolio diversification, hedge and safe-haven properties in cryptocurrency investments and financial economics: A systematic literature review. Journal of Risk and Financial Management, 16(1), 3. Retrieved from https://doi.org/10.3390/jrfm16010003
  2. Alola, U. V., Cop, S. & Adewale Alola, A. (2019). The spillover effects of tourism receipts, political risk, real exchange rate, and trade indicators in Turkey. International Journal of Tourism Research, 21(6), 813-823.
  3. Arya, M. H. & Sharma, D. D. K. (2015). Presence of seasonality in stock market: A reference from India and us. PRASTUTI: Journal of Management & Research, 4(1), 24–31. Retrieved from https://doi.org/10.51976/gla.prastuti.v4i1.411504
  4. Awartani, B. & Maghyereh, A. I. (2013). Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries. Energy Economics, 36, 28–42. Retrieved from https://doi.org/10.1016/j.eneco.2012.11.024
  5. Bae, K.-H., Karolyi, G. & Stulz, R. (2003). A new approach to measuring financial contagion. Review of Financial Studies, 16, 717–763. Retrieved from https://doi.org/10.2139/ ssrn.241634
  6. Banik, M. S., Gope, D. A. & Deb, D. S. (2020). Global stock markets and COVID-19: A case study of Indian stock exchanges. FOCUS: Journal of International Business, 7(2), 117–134. Retrieved from https://doi.org/10.17492/jpi.focus.v7i2.722006
  7. Basher, S. A., Haug, A. A. & Sadorsky, P. (2012). Oil prices, exchange rates and emerging stock markets. Energy Economics, 34(1), 227–240. Retrieved from https://doi.org/10.1016/j.eneco.2011.10.005
  8. Baur, D. G. & Lucey, B. M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. Financial Review, 45(2), 217–229. Retrieved from https://doi.org/10.1111/j.1540-6288.2010.00244.x
  9. Bhardwaj, K., Garima, Lemma, H. R. & Refera, M. K. (2024). Does index options trading destabilize Indian stock market volatility: An application of ARCH and GARCH models. Cogent Business & Management, 11(1), 2413391. Retrieved from https://doi.org/10.1080/23311975.2024.2413391
  10. Bhullar, P. S., Gupta, P. K., Kiranmai J. & Tandon, D. (2024). Interdependence between energy commodities and global stock indices: A volatility transmission approach. FIIB Business Review, 23197145241288974. Retrieved from https://doi.org/10.1177/23197145241288974
  11. Bouri, E., Jalkh, N., Molnár, P. & Roubaud, D. (2017). Bitcoin for energy commodities before and after the December 2013 crash: Diversifier, hedge or safe haven? Applied Economics, 1–11. Retrieved from https://doi.org/10.1080/00036846.2017.1299102
  12. Bouri, E., Kanjilal, K., Ghosh, S., Roubaud, D. & Saeed, T. (2021). Rare earth and allied sectors in stock markets: Extreme dependence of return and volatility. Applied Economics, 53(49), 5710–5730. Retrieved from https://doi.org/10.1080/00036846.2021.1927971
  13. Brini, A. & Lenz, J. (2024). A comparison of cryptocurrency volatility-benchmarking new and mature asset classes. Financial Innovation, 10(1), 122. Retrieved from https://doi.org/10.1186/s40854-024-00646-y
  14. Chen, J.-H. (2011). The spillover and leverage effects of ethical exchange traded fund. Applied Economics Letters, 18(10), 983–987. Retrieved from https://doi.org/10.1080/13504851.2010.520663
  15. Corbet, S., Hou, Y. G., Hu, Y. & Oxley, L. (2021). Volatility spillovers during market supply shocks: The case of negative oil prices. Resources Policy, 74. Retrieved from https://doi.org/10.1016/j.resourpol.2021.102357
  16. Corbet, S., Meegan, A., Larkin, C., Lucey, B. & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28–34. Retrieved from https://doi.org/10.1016/j.econlet.2018.01.004
  17. 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. Retrieved from https://doi.org/10.1111/j.1468-0297.2008.02208.x
  18. 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. Retrieved from https://doi.org/10.1016/j.ijforecast.2011.02.006
  19. Diebold, F. X. & Yılmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119–134. Retrieved from https://doi.org/10.1016/j.jeconom.2014.04.012
  20. Du, R. & Xu, P. (2024). Time-varying spillover effects between clean and traditional energy under multiple uncertainty risk: Evidence from the U.S. market. Sustainability, 16(21), 9164. Retrieved from https://doi.org/10.3390/su16219164
  21. Elsayed, A. H., Naifar, N., Nasreen, S. & Tiwari, A. K. (2022). Dependence structure and dynamic connectedness between green bonds and financial markets: Fresh insights from time-frequency analysis before and during COVID-19 pandemic. Energy Economics, 107. Retrieved from https://doi.org/10.1016/j.eneco.2022.105842
  22. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987. Retrieved from https://doi.org/10.2307/1912773
  23. Evrim Mandacı, P., Cagli, E. Ç. & Taşkın, D. (2020). Dynamic connectedness and portfolio strategies: Energy and metal markets. Resources Policy, 68. Retrieved from https://doi.org/10.1016/j.resourpol.2020.101778
  24. Forbes, K. J. & Rigobon, R. (2002). No contagion, only interdependence: Measuring stock market comovements. The Journal of Finance, 57(5), 2223–2261. Retrieved from https://doi.org/10.1111/0022-1082.00494
  25. Gao, R., Zhao, Y. & Zhang, B. (2021). The spillover effects of economic policy uncertainty on the oil, gold, and stock markets: Evidence from China. International Journal of Finance and Economics, 26(2), 2134–2141. Retrieved from https://doi.org/10.1002/ijfe.1898
  26. Garman, M. B. & Klass, M. J. (1980). On the estimation of security price volatilities from historical data. The Journal of Business, 53(1), 67. Retrieved from https://doi.org/10.1086/296072
  27. Gayathri, M. & Sophia, S. (2025). Electoral shockwaves: A novel analysis of market volatility surrounding India’s Prime ministerial elections. SAGE Open, 15(2), 21582440251343955. Retrieved from https://doi.org/10.1177/21582440251343955
  28. Gil-Alana, L. A., Abakah, E. J. A. & Rojo, M. F. R. (2020). Cryptocurrencies and stock market indices. Are they related? Research in International Business and Finance, 51, 101063. Retrieved from https://doi.org/10.1016/j.ribaf.2019.101063
  29. Gil-Alana, L. A., Mudida, R., Yaya, O. S., Osuolale, K. A. & Ogbonna, A. E. (2021). Mapping US presidential terms with S&P500 index: Time series analysis approach. International Journal of Finance and Economics, 26(2), 1938–1954. Retrieved from https://doi.org/10.1002/ijfe.1887
  30. He, Z., Qian, W., Miftah, B. & Zoynul Abedin, M. (2025). Quantile time-frequency spillovers among climate policy uncertainty, energy markets, and stock markets. International Review of Economics & Finance, 103, 104428. Retrieved from https://doi.org/10.1016/j.iref.20 25.104428
  31. Hung, N. T. (2022). Asymmetric connectedness among S&P 500, crude oil, gold and Bitcoin. Managerial Finance, 48(4), 587–610. Retrieved from https://doi.org/10.1108/MF-08-2021-0355
  32. Husain, S., Tiwari, A. K., Sohag, K., & Shahbaz, M. (2019). Connectedness among crude oil prices, stock index and metal prices: An application of network approach in the USA. Resources Policy, 62, 57–65. Retrieved from https://doi.org/10.1016/j.resourpol.2019.03.011
  33. Kilian, L., & Park, C. (2009). The impact of oil price shocks on the U.S. stock market. International Economic Review, 50(4), 1267–1287. Retrieved from https://doi.org/10.1111/j.1468-2354.2009.00568.x
  34. Koop, G., Pesaran, M. H. & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119–147. Retrieved from https://doi.org/10.1016/0304-4076(95)01753-4
  35. Liu, Q., Xu, C. & Xie, J. (2024). Comparative analysis of spillover effects in the global stock market under normal and extreme market conditions. International Journal of Financial Studies, 12(2), 53. Retrieved from https://doi.org/10.3390/ijfs12020053
  36. Mensi, W., Al Rababa’a, A. R., Alomari, M., Vo, X. V. & Kang, S. H. (2022). Dynamic frequency volatility spillovers and connectedness between strategic commodity and stock markets: US-based sectoral analysis. Resources Policy, 79. Retrieved from https://doi.org/10.1016/j.resourpol.2022.102976
  37. Merton, R. (1990). Chapter 11 Capital market theory and the pricing of financial securities. In Handbook of Monetary Economics (Vol. 1, pp. 497–581). Elsevier. Retrieved from https://doi.org/10.1016/S1573-4498(05)80014-1
  38. Ntare, H. B., Muteba Mwamba, J. W. & Adekambi, F. (2025). Asymmetric volatility spillovers in varying market conditions and portfolio performance analysis of the South African Foreign Exchange market. Economies, 13(8), 232. Retrieved from https://doi.org/10.3390/economies13080232
  39. Pandey, M. A. & Kumar, M. S. (2024). An empirical analysis of exchange rate volatility in India during COVID-19 pandemic and Russia-Ukraine war. MUDRA: Journal of Finance and Accounting, 11(1), 85–103. Retrieved from https://doi.org/10.17492/jpi.mudra.v11i1.1112405
  40. Pesaran, H. H. & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17–29. Retrieved from https://doi.org/10.1016/S0165-1765(97)00214-0
  41. Roy, M. & Roy, S. S. (2016). International trade and international finance: Explorations of contemporary issues. International Trade and International Finance: Explorations of Contemporary Issues, 1–595. Retrieved from https://doi.org/10.1007/978-81-322-2797-7
  42. Sarkar, R., Guha Deb, S. & Panda, A. (2025). Information transmission performance of the GIFT Nifty futures: Evidence from high-frequency data. Journal of Risk and Financial Management, 18(9), 527. Retrieved from https://doi.org/10.3390/jrfm18090527
  43. Shahrier, N. A., Anwer, Z. & Ishaq Bhatti, M. (2025). Pure vs. Fundamental contagion. International Review of Economics & Finance, 103, 104592. Retrieved from https://doi.org/10.1016/j.iref.2025.104592
  44. Shahzad, S. J. H., Naeem, M. A., Peng, Z. & Bouri, E. (2021). Asymmetric volatility spillover among Chinese sectors during COVID-19. International Review of Financial Analysis, 75. Retrieved from https://doi.org/10.1016/j.irfa.2021.101754
  45. Shakeel, M., Rabbani, M. R., Hawaldar, I. T., Chhabra, V. & Zaidi, F. K. (2023). Is there an intraday volatility spillover between exchange rate, gold and crude oil? Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100094. Retrieved from https://doi.org/10.1016/j.joitmc.2023.100094
  46. Sharma, M. P. C. (2018). A study on affiliation between Indian stock market and macro economic variables. MUDRA: Journal of Finance and Accounting, 5(1), 117–129. Retrieved from https://doi.org/10.17492/mudra.v5i01.13040
  47. Singh, P. A. K. & Shrivastav, D. R. K. (2018). An empirical study of testing financial integration between Indian and New York stock market. Delhi Business Review, 19(1), 107–116. Retrieved from https://doi.org/10.51768/dbr.v19i1.191201816
  48. Statman, M. (1987). How many stocks make a diversified portfolio? The Journal of Financial and Quantitative Analysis, 22(3), 353. Retrieved from https://doi.org/10.2307/2330969
  49. Tiwari, A. K., Abakah, E. J. A., Dwumfour, R. A. & Mefteh-Wali, S. (2022). Connectedness and directional spillovers in energy sectors: International evidence. Applied Economics, 54(22), 2554–2569. Retrieved from https://doi.org/10.1080/00036846.2021.1998326
  50. Tiwari, A. K., Nasreen, S., Ullah, S. & Shahbaz, M. (2021). Analysing spillover between returns and volatility series of oil across major stock markets. International Journal of Finance and Economics, 26(2), 2458–2490. Retrieved from https://doi.org/10.1002/ijfe.1916
  51. Xia, T., Yao, C.-X. & Geng, J.-B. (2020). Dynamic and frequency-domain spillover among economic policy uncertainty, stock and housing markets in China. International Review of Financial Analysis, 67. Retrieved from https://doi.org/10.1016/j.irfa.2019.101427
  52. Yadav, N., Singh, A. B. & Tandon, P. (2023). Volatility spillover effects between Indian stock market and global stock markets: A DCC-GARCH model. FIIB Business Review, 23197145221141186. Retrieved from https://doi.org/10.1177/23197145221141186
  53. Yilmaz, K. (2009). Return and volatility spillovers among the East Asian equity markets. Koç University-TUSIAD Economic Research Forum Working Papers, Article 0907. Retrieved from https://ideas.repec.org//p/koc/wpaper/0907.html
  54. Yousaf, I., Ali, S., Naveed, M. & Adeel, I. (2021). Risk and return transmissions from crude oil to Latin American stock markets during the crisis: Portfolio implications. SAGE Open, 11(2). Retrieved from https://doi.org/10.1177/21582440211013800
  55. Zhang, J., Cai, K. & Wen, J. (2024). A survey of deep learning applications in cryptocurrency. iScience, 27(1). Retrieved from https://doi.org/10.1016/j.isci.2023.108509
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