Return and volatility spillover between India and leading Asian and global equity markets: an empirical analysis


  • Aswini Kumar Mishra Department of Economics and Finance, BITS Pilani – K K Birla Goa Campus, Zuarinagar, India
  • Saksham Agrawal Department of Economics and Finance, BITS Pilani – K K Birla Goa Campus, Zuarinagar, India
  • Jash Ashish Patwa Department of Economics and Finance, BITS Pilani – K K Birla Goa Campus, Zuarinagar, India


Equity markets, Return spillover, Volatility spillover, GARCH-BEKK model, Business cycle, Investor behaviour



The study uses the multivariate GARCH-BEKK model (which was first proposed by Baba et al. (1990) and then further developed by Engle and Kroner (1995)) to examine the return and volatility spillover between India and four leading Asian (namely, China, Japan, Singapore and Hong Kong) and two global (namely, the United Kingdom and the United States) equity markets.


The study employs a multivariate GARCH-BEKK model to quantify return correlation and volatility transmission across the pre- and post-2008 global financial crisis periods (apart from other conventional time series modelling like cointegration, Granger causality using vector error correction model (VECM)).


The results show a tendency of the Indian stock market index to move along with the US and Hong Kong market indices. The decrease in the value of the co-integration coefficient during the recession was explained by reduced investor confidence in developing countries. The result further shows a clear distinction in terms of volatility spillover between the Asian market vis-a-vis US and UK markets. Volatility transmission from India to Asian markets was found to be significantly higher as compared to the US and UK. So also, the study’s results show a puzzling result giving us comparable co-integration ranks for phase 2 (expansion) and phase 3 (slow-down) of the business cycle in most cases.

Research limitations/implications

In Granger causality testing, the results were unable to ascertain the difference between phase 2 (expansion) and phase 3 (slowdown). However, the multivariate GARCH (MGARCH)-BEKK model showed a clear reduction in volatility transmission to NIFTY50 (is the flagship index on the National Stock Exchange of India Ltd. (NSE)) as India entered slow-down. This shows that the Indian economy does go through different business cycles, and the changes in parameters hence prove hypothesis 3 to be true with respect to volatility transmission to India from International markets.


The results show that for all countries, the volatility transmitted to India increases significantly going from phase 1 (recession) to phase 2 (expansion) and reduces again once the countries enter slow-down in phase 3 (slowdown). This shows that during expansion shocks and impulses in international markets affect the Indian markets significantly, supporting the increase in co-integration in phase 2 (expansion). During expansion, developing markets like India become profitable for investors, due to the high growth rate when compared to developed countries. This implies that a significant amount of capital enters Indian markets, which is susceptible to the volatility of international markets. The volatility transmission from India to the US and UK was insignificant in phase 1 (recession and recovery) and phase 3 (slow-down) showing a weak linkage between the markets during volatile time periods.



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Agrawal, G., Srivastav, A.K. and Srivastava, A. (2010), “A study of exchange rates movement and stock market volatility”, International Journal of Business and Management, Vol. 5 No. 12, pp. 62-73.

Ahmad, K.M., Ashraf, S. and Ahmed, S. (2005), “Is the Indian stock market integrated with the US and Japanese markets? An empirical analysis”, South Asia Economic Journal, Vol. 6 No. 2, pp. 193-206.

Al Nasser, O.M. and Hajilee, M. (2016), “Integration of emerging stock markets with global stock markets”, Research in International Business and Finance, Vol. 36, pp. 1-12.

Baba, Y., Engle, R.F., Kraft, D.F. and Kroner, K.F. (1990), Multivariate Simultaneous Generalized ARCH. Unpublished Manuscript, University of California, San Diego.

Balli, F., Hajhoj, H.R., Basher, S.A. and Ghassan, H.B. (2015), “An analysis of returns and volatility spillovers and their determinants in emerging Asian and Middle Eastern countries”, International Review of Economics and Finance, Vol. 39, pp. 311-325.

Bekaert, G. and Harvey, C.R. (1997), “Emerging equity market volatility”, Journal of Financial Economics, Vol. 43 No. 1, pp. 29-77.

Bouri, E.I. (2013), “Correlation and volatility of the MENA equity markets in turbulent periods, and portfolio implications”, Economics Bulletin, Vol. 33, pp. 1575-1593.

Bouteska, A. and Regaieg, B. (2020), “Loss aversion, overconfidence of investors and their impact on market performance evidence from the US stock markets”, Journal of Economics, Finance and Administrative Science, Vol. 25 No. 50, pp. 451-478, doi: 10.1108/JEFAS-07-2017-0081.

Cardona, L., Gutierrez, M. and Agudelo, D.A. (2017), “Volatility transmission between US and Latin American stock markets: testing the decoupling hypothesis”, Research in International Business and Finance, Vol. 39 No. A, pp. 115-127.

Chang, C.-L. and McAleer, M. (2018), “The fiction of full BEKK: pricing fossil fuels and carbon emissions”, Finance Research Letters, March, Vol. 28, pp. 11-19.

Dhal, S. (2009), “Integration of India's stock market with global and major regional markets”, Journal of Economic Integration, Vol. 24 No. 4, pp. 778-805.

Chougala, P. and Srivatsa, H.S. (2016), “Analytical study of correlation between Indian and international stock market”, Journal of Management and Commerce, MSRUAS, Vol. 2 No. 2, pp. 27-30.

Dickey, D.A. and Fuller, W.A. (1981), “Likelihood ratio statistics for autoregressive time series with a unit root”, Econometrica, Vol. 49 No. 4, pp. 1057-1072.

Doryab, B. and Salehi, M. (2018), “Modeling and forecasting abnormal stock returns using the nonlinear Gray Bernoulli model”, Journal of Economics, Finance and Administrative Science, Vol. 23 No. 44, pp. 95-112, doi: 10.1108/JEFAS-06-2017-0075.

Durbin, J. and Watson, G. (1992), “Testing for serial correlation in least squares regression”, Biometrika, Vol. 37 Nos 3-4, pp. 409-428.

Engle, R.F. and Kroner, K.F. (1995), “Multivariate simultaneous Generalized Arch”, Econometric Theory, Vol. 11 No. 1, pp. 122-150.

Gangadharan, S.R. and Yoonus, C. (2012), “Global financial crisis and stock market integration: a study on the impact of global financial crisis on the level of financial integration between the US and Indian stock markets”, Asia-Pacific Journal of Management Research and Innovation, Vol. 8 No. 2, pp. 101-110.

Georgoutsos, D.A. and Kouretas, G.P. (2001), “Common stochastic trends in international stock markets: testing in an integrated framework”, Working Papers 0104, University of Crete, Department of Economics, Greece.

Granger, C.W.J. (1969), “Investigating causal relations by econometric models and cross-spectral methods”, Econometrica, Vol. 37 No. 3, pp. 424-438.

Grubel, H.G. (1968), “Internationally diversified portfolios: welfare gains and capital flows”, The American Economic Review, Vol. 58 No. 5, pp. 1299-1314.

xGupta, R. and Guidi, F. (2012), “Cointegration relationship and time varying co-movements among Indian and Asian developed stock markets”, International Review of Financial Analysis, Vol. 21, pp. 10-22.

Hamao, Y., Masulis, R.W. and Ng, V. (1990), “Correlations in price changes and volatility across international stock markets”, The Review of Financial Studies, Vol. 3 No. 2, pp. 281-307.

Hansda, S.K. and Ray, P. (2002), “BSE and Nasdaq: Globalisation, information Technology and stock prices”, Economic and Political Weekly, Vol. 37 No. 5, pp. 459-468.

Hung, N.T. (2018), “Dynamics of volatility spillover between stock and foreign exchange market: empirical evidence from Central and Eastern European Countries”, The conference’s proceedings of ECMS 2018, Wilhelmshaven, Germany, May 22-25, 2018, European Council for Modeling and Simulation, pp. 27-34, doi: 10.7148/2018-0027.

Hung, N.T. (2019), “Return and volatility spillover across equity markets between China and Southeast Asian countries”, Journal of Economics, Finance and Administrative Science, Vol. 24 No. 47, pp. 66-81, doi: 10.1108/JEFAS-10-2018-0106.

Jarque, C.M. and Bera, A.K. (1987), “A test for Normality of Observations and regression residuals”, International Statistical Review/Revue Internationale de Statistique, Vol. 55 No. 2, pp. 163-172.

Jebran, K., Chen, S., Ullah, I. and Mirza, S.S. (2017), “Does volatility spillover among stock markets varies from normal to turbulent periods? Evidence from emerging markets of Asia”, The Journal of Finance and Data Science, Vol. 3 Nos 1/4, pp. 20-30.

Jin, X. and An, X. (2016), “Global financial crisis and emerging stock market contagion: a volatility impulse response function approach”, Research in International Business and Finance, Vol. 36, pp. 179-195.

Johansen, S. (1988), “Statistical analysis of cointegration vectors”, Journal of Economic Dynamics and Control, Vol. 12 Nos 2/3, pp. 231-254.

Jung, R. and Maderitsch, R. (2014), “Structural breaks in volatility spillovers between international financial markets: contagion or mere interdependence?”, Journal of Banking and Finance, Vol. 47 No. October 2014, pp. 331-342.

Katircioğlu, S., Abasiz, T., Sezer, S. and Katırcıoglu, S. (2019), “Volatility of the alternative energy input prices and spillover effects: a VAR [MA]-MGARCH in BEKK approach for the Turkish economy”, Environmental Science and Pollution Research, Vol. 26 No. 11, pp. 10738-10745.

Katsiampa, P., Corbet, S. and Lucey, B. (2019), “Volatility spillover effects in leading cryptocurrencies: a BEKK-MGARCH analysis”, Finance Research Letters, Vol. 29, pp. 68-74.

Kocaarslan, B., Sari, R. and Soytas, U. (2017), “Are there any diversification benefits among global finance center candidates in Eurasia?”, Emerging Markets Finance and Trade Vol. 53 No 2, pp. 357-374.

Kotha, K.K. and Mukhopadhyay, C. (2002), “Equity market Interlinkages: transmission of volatility: a case of US and India”, ResearchGate, Vol. 1 No. 1, pp. 1-40.

Koutmos, G. and Booth, G.G. (1995), “Asymmetric volatility transmission in international stock markets”, Journal of International Money and Finance, Vol. 14 No. 6, pp. 747-762.

Lobo, B.J., Wong, W.-K. and Chen, H. (2016), “Links between the Indian, U.S. And Chinese stock markets”, Departmental Working Papers wp0602, Links between the Indian, U.S. And Chinese Stock Markets, Departmental Working Papers wp0602, National University of Singapore, Department of Economics, Singapore, pp. 1-27.

Lucas, R.J.E. (1980), “Methods and problems in business cycle theory”, Journal of Money, Credit and Banking, Vol. 12 No. 4, pp. 696-715.

Mishra, A.K. and Ghate, K. (2022), “Dynamic connectedness in non-ferrous commodity markets: evidence from India using TVP-VAR and DCC-GARCH approaches”, Resources Policy, Vol. 76, 102572.

Mishra, A.K., Ghate, K., Renganathan, J., Kennet, J.J. and Rajderkar, N.P. (2022), “Rolling, recursive evolving and asymmetric causality between crude oil and gold prices: evidence from an emerging market”, Resources Policy, Vol. 75, 102474.

Mohammadi, H. and Tan, Y. (2015), “Return and volatility spillovers across equity markets in Mainland China, Hong Kong and the United States”, Econometrics, Vol. 3 No. 2, pp. 215-232.

Mukherjee, D. (2007), “Comparative analysis of Indian stock market with international markets”, Great Lakes Herald, Vol. 1 No. 1, pp. 39-71.

Ng, A. (2000), “Volatility spillover effects from Japan and the US to the Pacific Basin”, Journal of International Money and Finance, Vol. 19 No. 2, pp. 207-233.

Ng, H. and Lam, K.P. (2006), “How does sample size affect GARCH models?”, 2006 Joint Conference on Information Sciences, JCIS, Kaohsiung, Taiwan, 2006.

Oliveira, F., Maia, S., Jesus, D. and Besarria, C. (2018), “Which information matters to market risk spreading in Brazil? Volatility transmission modelling using MGARCH-BEKK, DCC, t-Copulas”, North American Journal of Economics and Finance, July, Vol. 45, pp. 83-100.

Samarakoon, L.P. (2011), “Stock market interdependence, contagion, and the U.S. financial crisis: the case of emerging and Frontier markets”, Journal of International Financial Markets, Institutions and Money, Vol. 21 No. 5, pp. 724-742.

Soriano Felipe, P. and Climent Diranzo, F.J. (2005), Volatility Transmission Models: A Survey, Universidad de Valencia, pp. 1-46.

Syriopoulos, T., Makram, B. and Boubaker, A. (2015), “Stock market volatility spillovers and portfolio hedging: BRICS and the financial crisis”, International Review of Financial Analysis, Vol. 39, pp. 7-18.

Vo, X.V. and Ellis, C. (2018), “International financial integration: stock return linkages and volatility transmission between Vietnam and advanced countries”, Emerging Markets Review, Vol. 36, pp. 19-27.

Yang, Z. and Zhou, Y. (2017), “Quantitative easing and volatility spillovers across countries and asset classes”, Management Science, Vol. 63, pp. 333-354.

Yousaf, I., Ali, S. and Wong, W.-K. (2020), “Return and volatility transmission between world-leading and Latin American stock markets: portfolio implications”, Journal of Risk and Financial Management, Vol. 137, p. 148.




How to Cite

Kumar Mishra, A., Agrawal, . S., & Ashish Patwa, J. (2022). Return and volatility spillover between India and leading Asian and global equity markets: an empirical analysis. Journal of Economics, Finance and Administrative Science, 27(54), 294–312. Retrieved from

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