Volatility spillovers among MIST stock markets

Deniz Sevinç; Deniz Sevinç

doi:10.3934/DSFE.2022004

Data Science in Finance and Economics

2022, Volume 2, Issue 2: 80-95. doi: 10.3934/DSFE.2022004

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Research article

Volatility spillovers among MIST stock markets

Deniz Sevinç ^,

Department of Business Administration, Anadolu University, Eskisehir, Turkey

Received: 19 February 2022 Revised: 29 April 2022 Accepted: 04 May 2022 Published: 12 May 2022
JEL Codes: D53, G11, G15

This paper examines the effects of volatility spillover between MIST stock markets. We used daily data from January 3, 2012, to November 16, 2021, and the test of causality in variance, including structural breaks. First, we observed no structural break for Turkey and Indonesia, but there is a structural break in each South Korean and Mexican stock markets. A structural break is found for Mexico the day, aka "El Betito" was arrested. Likewise, we detected a structural break on the first day of the Covid-19 case for South Korea. We surprisingly observed that Covid-19 did not cause a structural break in these markets during the period analyzed, except in South Korea. GARCH (1, 1) models, including structural breaks, show that all series are temporarily and permanently affected by their own shocks. The causality in variance test reveals that MIST countries have volatility spillover effects on each other. There is bidirectional causality in variance between all markets, except South Korea and Mexico. The Mexican stock market is a volatility transmitter for South Korea, but vice versa. The paper indicates a connection in terms of the financial markets of MIST countries, and they are affected by each other's shocks, according to the study results.
- volatility,
- spillover,
- causality in variance,
- structural break,
- MIST countries
Citation: Deniz Sevinç. Volatility spillovers among MIST stock markets[J]. Data Science in Finance and Economics, 2022, 2(2): 80-95. doi: 10.3934/DSFE.2022004

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Abstract

This paper examines the effects of volatility spillover between MIST stock markets. We used daily data from January 3, 2012, to November 16, 2021, and the test of causality in variance, including structural breaks. First, we observed no structural break for Turkey and Indonesia, but there is a structural break in each South Korean and Mexican stock markets. A structural break is found for Mexico the day, aka "El Betito" was arrested. Likewise, we detected a structural break on the first day of the Covid-19 case for South Korea. We surprisingly observed that Covid-19 did not cause a structural break in these markets during the period analyzed, except in South Korea. GARCH (1, 1) models, including structural breaks, show that all series are temporarily and permanently affected by their own shocks. The causality in variance test reveals that MIST countries have volatility spillover effects on each other. There is bidirectional causality in variance between all markets, except South Korea and Mexico. The Mexican stock market is a volatility transmitter for South Korea, but vice versa. The paper indicates a connection in terms of the financial markets of MIST countries, and they are affected by each other's shocks, according to the study results.

References

[1]	Ahmad W, Sanjay S, Bhanumurthy NR (2013) Eurozone Crisis and BRIICKS Stock Markets: Contagion or Market Interdependence? Econ Model 33: 209–225. https://doi.org/10.1016/J.ECONMOD.2013.04.009 doi: 10.1016/J.ECONMOD.2013.04.009
[2]	Akca K, Ozturk SS (2016) The Effect of 2008 Crisis on the Volatility Spillovers among Six Major Markets. Int Rev Financ 16: 169–178. https://doi.org/10.1111/IRFI.12071 doi: 10.1111/IRFI.12071
[3]	Akkaya M (2021) An Analysis of the Stock Market Volatility Spread in Emerging Countries. Istanbul Bus Res 50: 215–233. http://dx.doi.org/10.26650/ibr.2021.50.861135 doi: 10.26650/ibr.2021.50.861135
[4]	Atukeren E, Çevik E I, Korkmaz T (2021) Volatility Spillovers between WTI and Brent Spot Crude Oil Prices: An Analysis of Granger Causality in Variance Patterns over Time. Res Int Bus Financ 56: 101385. https://doi.org/10.1016/J.RIBAF.2021.101385 doi: 10.1016/J.RIBAF.2021.101385
[5]	Bae KH, Karolyi GA (1994) Good News, Bad News and International Spillovers of Stock Return Volatility between Japan and the U.S. Pacific-Basin Financ J 2: 405–438. https://doi.org/10.1016/0927-538X(94)90003-5 doi: 10.1016/0927-538X(94)90003-5
[6]	Bhar R, Nikolova, B (2009) Return, Volatility Spillovers and Dynamic Correlation in the BRIC Equity Markets: An Analysis Using a Bivariate EGARCH Framework. Global Financ J 3: 203–218. https://doi.org/10.1016/J.GFJ.2008.09.005 doi: 10.1016/J.GFJ.2008.09.005
[7]	Bollerslev T (1986) Generalized Autoregressive Conditional Heteroskedasticity. J Econometrics 31: 307–327. https://doi.org/10.1016/0304-4076(86)90063-1 doi: 10.1016/0304-4076(86)90063-1
[8]	Cheung YW, Ng LK (1996) A Causality-in-Variance Test and Its Application to Financial Market Prices. J Econometrics 72: 33–48. https://doi.org/10.1016/0304-4076(94)01714-X doi: 10.1016/0304-4076(94)01714-X
[9]	Diebold F, Yilmaz K (2012) Better to give than to receive: Predictive directional measurement of volatility spillovers. Int J Forecasting 28: 57–66. https://doi.org/10.1016/J.IJFORECAST.2011.02.006 doi: 10.1016/J.IJFORECAST.2011.02.006
[10]	Dimitriou D, Kenourgios D, Simos T (2013) Global Financial Crisis and Emerging Stock Market Contagion: A Multivariate FIAPARCH–DCC Approach. Int Rev Financ Anal 30: 46–56. https://doi.org/10.1016/J.IRFA.2013.05.008 doi: 10.1016/J.IRFA.2013.05.008
[11]	Ewing BT, Malik F (2010) Estimating Volatility Persistence in Oil Prices Under Structural Breaks. Financ Rev 45: 1011–1023. https://doi.org/10.1111/J.1540-6288.2010.00283.X doi: 10.1111/J.1540-6288.2010.00283.X
[12]	Gamba-Santamaria S, Gomez-Gonzalez J, Hurtado-Guarin JL, et al. (2017) Stock Market Volatility Spillovers: Evidence for Latin America. Financ Res Lett 20: 207–216. https://doi.org/10.1016/J.FRL.2016.10.001 doi: 10.1016/J.FRL.2016.10.001
[13]	Gemici E, Polat M (2020) Causality-in-Mean and Causality-in-Variance among Bitcoin, Litecoin, and Ethereum. Stud Econ Financ 38: 861–872. https://doi.org/10.1108/SEF-07-2020-0251/FULL/PDF doi: 10.1108/SEF-07-2020-0251/FULL/PDF
[14]	Gilenko E, Fedorova E (2014) Internal and External Spillover Effects for the BRIC Countries: Multivariate GARCH-in-Mean Approach. Res Int Bus Financ 31: 32–45. https://doi.org/10.1016/J.RIBAF.2013.11.002 doi: 10.1016/J.RIBAF.2013.11.002
[15]	Guloglu B, Kaya P, Aydemir R (2016) Volatility Transmission among Latin American Stock Markets under Structural Breaks. Phys A 462: 330–340. https://doi.org/10.1016/J.PHYSA.2016.06.093 doi: 10.1016/J.PHYSA.2016.06.093
[16]	Hafner CM, Herwartz H (2006) A Lagrange Multiplier Test for Causality in Variance. Econ Lett 93: 137–141. https://doi.org/10.1016/J.ECONLET.2006.04.008 doi: 10.1016/J.ECONLET.2006.04.008
[17]	Hamao Y, Masulis RW, Ng V (1990) Correlations in Price Changes and Volatility across International Stock Markets. Rev Financ Stud 3: 281–307. https://doi.org/10.1093/RFS/3.2.281 doi: 10.1093/RFS/3.2.281
[18]	Hong Y (2001) A Test for Volatility Spillover with Application to Exchange Rates. J Econometrics 103: 183–224. https://doi.org/10.1016/S0304-4076(01)00043-4 doi: 10.1016/S0304-4076(01)00043-4
[19]	Jacobs LM, Rossem R (2014) The BRIC Phantom: A Comparative Analysis of the BRICs as a Category of Rising Powers. J Pol Model 36: S47–66. https://doi.org/10.1016/J.JPOLMOD.2013.10.008 doi: 10.1016/J.JPOLMOD.2013.10.008
[20]	Karolyi GA (1995) A Multivariate GARCH Model of International Transmissions of Stock Returns and Volatility: The Case of the United States and Canada. J Bus Econ Stat 13: 25. https://doi.org/10.2307/1392517 doi: 10.2307/1392517
[21]	Kirkulak Uludag B, Khurshid M (2019) Volatility Spillover from the Chinese Stock Market to E7 and G7 Stock Markets. J Econ Stud 46: 90–105. https://doi.org/10.1108/JES-01-2017-0014/FULL/PDF doi: 10.1108/JES-01-2017-0014/FULL/PDF
[22]	Kilic E, Polat M (2020) The Return and Volatility Interaction Between Stock Markets and Exchange Rates of MIST Countries. Gaziantep Univ J Soc Sci 19: 1463–1479. https://doi.org/10.21547/JSS.706726 doi: 10.21547/JSS.706726
[23]	Korkmaz T, Çevik EI, Atukeren E (2012) Return and Volatility Spillovers among CIVETS Stock Markets. Emerg Mark Rev 13: 230–252. https://doi.org/10.1016/J.EMEMAR.2012.03.003 doi: 10.1016/J.EMEMAR.2012.03.003
[24]	Lee, SJ (2009) Volatility Spillover Effects Amongsix Asian Countries. Appl Econ Lett 16: 501–508. https://doi.org/10.1080/13504850601018700 doi: 10.1080/13504850601018700
[25]	Li Y, Giles DE (2015) Modelling Volatility Spillover Effects Between Developed Stock Markets and Asian Emerging Stock Markets. Int J Financ Econ 20: 155–177. https://doi.org/10.1002/IJFE.1506 doi: 10.1002/IJFE.1506
[26]	Li W (2021) COVID-19 and asymmetric volatility spillovers across global stock markets. North Am J Econ Financ 58: 101474. https://doi.org/10.1016/j.najef.2021.101474 doi: 10.1016/j.najef.2021.101474
[27]	Madhavan V (2017) How Interrelated Are MIST Equity Markets with the Developed Stock Markets of the World? Cogent Econ Financ 5. https://doi.org/10.1080/23322039.2017.1362822 doi: 10.1080/23322039.2017.1362822
[28]	Majdoub J, Sassi S (2017) Volatility Spillover and Hedging Effectiveness among China and Emerging Asian Islamic Equity Indexes. Emerg Mark Rev 31: 16–31. https://doi.org/10.1016/J.EMEMAR.2016.12.003 doi: 10.1016/J.EMEMAR.2016.12.003
[29]	Mensi W, Hammoudeh S, Kang SH (2017) Dynamic Linkages between Developed and BRICS Stock Markets: Portfolio Risk Analysis. Financ Res Lett 21: 26–33. https://doi.org/10.1016/J.FRL.2016.11.016 doi: 10.1016/J.FRL.2016.11.016
[30]	Moon GH, Yu WC (2010) Volatility Spillovers between the US and China Stock Markets: Structural Break Test with Symmetric and Asymmetric GARCH Approaches. Global Econ Rev 39: 129–149. https://doi.org/10.1080/1226508X.2010.483834 doi: 10.1080/1226508X.2010.483834
[31]	Morazán P I, Knoblauch K, Schäfer T (2012) The Role of BRICS in the Developing World. Belgium.
[32]	Ng A (2000) Volatility Spillover Effects from Japan and the US to the Pacific–Basin. J Int Money Financ 19: 207–233. https://doi.org/10.1016/S0261-5606(00)00006-1 doi: 10.1016/S0261-5606(00)00006-1
[33]	Nikmanesh L, Mohd NA (2016) Causality-in-Variance Between the Stock Market and Macroeconomic Variables in Singapore. Singapore Econ Rev 64: 1299–1317. https://doi.org/10.1142/S0217590816500363 doi: 10.1142/S0217590816500363
[34]	Panda P, Vasudevan S, Panda B (2021) Dynamic Connectedness among BRICS and Major Countries Stock Markets. J Public Aff 21: 1–20. https://doi.org/10.1002/PA.2265 doi: 10.1002/PA.2265
[35]	Pao HT, Li Y, Fu H (2014) Clean Energy, Non-Clean Energy, and Economic Growth in the MIST Countries. Energy Policy 67: 932–942. https://doi.org/10.1016/J.ENPOL.2013.12.039 doi: 10.1016/J.ENPOL.2013.12.039
[36]	Sansó A, Carrion JL, Aragó V (2004) Testing for Changes in the Unconditional Variance of Financial Time Series. Rev Econ Financ 4: 32–53. http://dspace.uib.es/xmlui/handle/11201/152078
[37]	Singh P, Kumar B, Pandey A (2010) Price and Volatility Spillovers across North American, European and Asian Stock Markets. International Rev Financ Anal 19: 55–64. https://doi.org/10.1016/J.IRFA.2009.11.001 doi: 10.1016/J.IRFA.2009.11.001
[38]	Syriopoulos T, Makram B, Boubaker A (2015) Stock Market Volatility Spillovers and Portfolio Hedging: BRICS and the Financial Crisis. Int Rev Financ Anal 39: 7–18. https://doi.org/10.1016/J.IRFA.2015.01.015 doi: 10.1016/J.IRFA.2015.01.015
[39]	Theodossiou P, Lee U (1993) Mean and Volatility Spillovers Across Major National Stock Markets: Further Empirical Evidence. J Financl Res 16: 337–350. https://doi.org/10.1111/J.1475-6803.1993.TB00152.X doi: 10.1111/J.1475-6803.1993.TB00152.X
[40]	Trivedi J, Spulbar C, Ramona B, et al. (2021) Modelling volatility spillovers, cross-market correlation and co-movements between stock markets in European Union: an empirical case study. Bus Manage Econ Eng 19: 70–90. https://doi.org/10.3846/BMEE.2021.13588 doi: 10.3846/BMEE.2021.13588
[41]	Verma P, Ozuna T (2007) International Stock Market Linkages and Spillovers: Evidence from Three Latin American Countries. Latin Am Bus Rev 8: 60–81. https://doi.org/10.1080/10978520802114672 doi: 10.1080/10978520802114672
[42]	Wang P, Wang P (2010) Price and Volatility Spillovers between the Greater China Markets and the Developed Markets of US and Japan. Global Financ J 21: 304–317. https://doi.org/10.1016/J.GFJ.2010.09.007 doi: 10.1016/J.GFJ.2010.09.007
[43]	Worthington A, Higgs H (2004) Transmission of Equity Returns and Volatility in Asian Developed and Emerging Markets: A Multivariate Garch Analysis. Int J Financ Econ 9: 71–80. https://doi.org/10.1002/IJFE.222/FORMAT/PDF doi: 10.1002/IJFE.222/FORMAT/PDF
[44]	Yarovaya L, Lau M (2016) Stock Market Comovements around the Global Financial Crisis: Evidence from the UK, BRICS and MIST Markets. Res Int Bus Financ 37: 605–619. https://doi.org/10.1016/J.RIBAF.2016.01.023 doi: 10.1016/J.RIBAF.2016.01.023
[45]	Yavas B, Rezayat F (2016) Country ETF Returns and Volatility Spillovers in Emerging Stock Markets, Europe and USA. Inte J Emerg Mark 11: 419–437. https://doi.org/10.1108/IJOEM-10-2014-0150/FULL/PDF doi: 10.1108/IJOEM-10-2014-0150/FULL/PDF
[46]	Yousaf I, Ahmed J (2018) Mean and Volatility Spillover of the Latin American Stock Markets. J Bus Econ 10: 51–63. http://journals.au.edu.pk/ojs/index.php/jbe/article/view/105

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