This paper quantitatively investigated the historical transition of return transmission, volatility spillovers, and correlations between the US, UK, and Japanese stock markets. Applying a vector autoregressive (VAR)-dynamic conditional correlation (DCC)-multivariate exponential generalized autoregressive conditional heteroscedasticity (MEGARCH) model, we derived new evidence for four historical periods between 1984 and 2024. First, we found that the return transmission from the US to the other markets has historically become stronger, whereas recently, the return transmission from the UK to the US has disappeared. Second, we clarified that volatility spillovers from the US to the other markets have historically become stronger, whereas recently, volatility spillovers from the UK to the US have also disappeared. Third, our analyses of the historical constant correlations and DCCs revealed that stock market connectedness has gradually tightened between the US and Japan and between the UK and Japan, whereas recently, the connectedness between the US and UK has weakened. Fourth, our VAR-DCC analyses also revealed that volatility spillovers between the US, UK, and Japanese stock markets have been asymmetric. Fifth, we further showed that the skew-t errors incorporated into our VAR-DCC model are effective in estimating the dynamic stock return linkages between the US, the UK, and Japan. Finally, based on our findings, we derived many significant and beneficial interpretations and implications for historically and deeply considering return transmission, volatility spillovers, and DCCs between international stock markets.
Citation: Chikashi Tsuji. The historical transition of return transmission, volatility spillovers, and dynamic conditional correlations: A fresh perspective and new evidence from the US, UK, and Japanese stock markets[J]. Quantitative Finance and Economics, 2024, 8(2): 410-436. doi: 10.3934/QFE.2024016
This paper quantitatively investigated the historical transition of return transmission, volatility spillovers, and correlations between the US, UK, and Japanese stock markets. Applying a vector autoregressive (VAR)-dynamic conditional correlation (DCC)-multivariate exponential generalized autoregressive conditional heteroscedasticity (MEGARCH) model, we derived new evidence for four historical periods between 1984 and 2024. First, we found that the return transmission from the US to the other markets has historically become stronger, whereas recently, the return transmission from the UK to the US has disappeared. Second, we clarified that volatility spillovers from the US to the other markets have historically become stronger, whereas recently, volatility spillovers from the UK to the US have also disappeared. Third, our analyses of the historical constant correlations and DCCs revealed that stock market connectedness has gradually tightened between the US and Japan and between the UK and Japan, whereas recently, the connectedness between the US and UK has weakened. Fourth, our VAR-DCC analyses also revealed that volatility spillovers between the US, UK, and Japanese stock markets have been asymmetric. Fifth, we further showed that the skew-t errors incorporated into our VAR-DCC model are effective in estimating the dynamic stock return linkages between the US, the UK, and Japan. Finally, based on our findings, we derived many significant and beneficial interpretations and implications for historically and deeply considering return transmission, volatility spillovers, and DCCs between international stock markets.
| [1] |
Abakah EJA, Gil-Alana LA, Madigu G, et al. (2020) Volatility persistence in cryptocurrency markets under structural breaks. Int Rev Econ Financ 69: 680−691. https://doi.org/10.1016/j.iref.2020.06.035 doi: 10.1016/j.iref.2020.06.035
|
| [2] |
Agyei-Ampomah S (2011) Stock market integration in Africa. Manage Financ 37: 242−256. https://doi.org/10.1108/03074351111113306 doi: 10.1108/03074351111113306
|
| [3] |
Akyildirim E, Cepni O, Molnár P, et al. (2022) Connectedness of energy markets around the world during the COVID-19 pandemic. Energy Econ 109: 105900. https://doi.org/10.1016/j.eneco.2022.105900 doi: 10.1016/j.eneco.2022.105900
|
| [4] |
Asadi M, Roubaud D, Tiwari AK (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 Econ 109: 105961. https://doi.org/10.1016/j.eneco.2022.105961 doi: 10.1016/j.eneco.2022.105961
|
| [5] |
Bae KH, Zhang X (2015) The cost of stock market integration in Emerging Markets. Asia−Pacific J Financ Stud 44: 1−23. https://doi.org/10.1111/ajfs.12079 doi: 10.1111/ajfs.12079
|
| [6] |
Baruník J, Kočenda E, Vácha L (2016) Asymmetric connectedness on the U.S. stock market: Bad and good volatility spillovers. J Financ Mark 27: 55−78. https://doi.org/10.1016/j.finmar.2015.09.003 doi: 10.1016/j.finmar.2015.09.003
|
| [7] |
Bauwens L, Laurent S (2005) A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models. J Bus Econ Stat 23: 346−354. https://doi.org/10.1198/073500104000000523 doi: 10.1198/073500104000000523
|
| [8] |
Bekaert G, Harvey CR (1995) Time-varying world market integration. J Financ 50: 403−444. https://doi.org/10.1111/j.1540-6261.1995.tb04790.x doi: 10.1111/j.1540-6261.1995.tb04790.x
|
| [9] |
Billio M, Getmansky M, Lo AW, et al. (2012) Econometric measures of connectedness and systemic risk in the finance and insurance sectors. J Financ Econ 104: 535−559. https://doi.org/10.1016/j.jfineco.2011.12.010 doi: 10.1016/j.jfineco.2011.12.010
|
| [10] |
Bouri E, Demirer R, Gabauer D, et al. (2022) Financial market connectedness: The role of investors' happiness. Financ Res Lett 44: 102075. https://doi.org/10.1016/j.frl.2021.102075 doi: 10.1016/j.frl.2021.102075
|
| [11] |
Chen Y, Xu J, Hu M (2022) Asymmetric volatility spillovers and dynamic correlations between crude oil price, exchange rate and gold price in BRICS. Resour Policy 78: 102857. https://doi.org/10.1016/j.resourpol.2022.102857 doi: 10.1016/j.resourpol.2022.102857
|
| [12] |
Diebold FX, Yilmaz K (2012) Better to give than to receive: Predictive directional measurement of volatility spillovers. Int J Forecast 28: 57−66. https://doi.org/10.1016/j.ijforecast.2011.02.006 doi: 10.1016/j.ijforecast.2011.02.006
|
| [13] | Engle RF (2002) Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J Bus Econ Stat 20: 339−350. |
| [14] |
Fratzscher M (2002) Financial market integration in Europe: On the effects of EMU on stock markets. Int J Financ Econ 7: 165−193. https://doi.org/10.1002/ijfe.187 doi: 10.1002/ijfe.187
|
| [15] |
Geng JB, Chen FR, Ji Q, et al. (2021) Network connectedness between natural gas markets, uncertainty and stock markets. Energy Econ 95: 105001. https://doi.org/10.1016/j.eneco.2020.105001 doi: 10.1016/j.eneco.2020.105001
|
| [16] |
Gong C, Tang P, Wang Y (2019) Measuring the network connectedness of global stock markets. Physica A 535: 122351. https://doi.org/10.1016/j.physa.2019.122351 doi: 10.1016/j.physa.2019.122351
|
| [17] |
Goodell JW, Yadav MP, Ruan J, et al. (2023) Traditional assets, digital assets and renewable energy: Investigating connectedness during COVID-19 and the Russia-Ukraine war. Financ Res Lett 58, 104323. https://doi.org/10.1016/j.frl.2023.104323 doi: 10.1016/j.frl.2023.104323
|
| [18] |
Horváth R, Petrovski D (2013) International stock market integration: Central and South Eastern Europe compared. Econ Systems 37: 81–91. https://doi.org/10.1016/j.ecosys.2012.07.004 doi: 10.1016/j.ecosys.2012.07.004
|
| [19] |
Hunter DM (2006) The evolution of stock market integration in the post-liberalization period – A look at Latin America. J Int Money Financ 25: 795–826. https://doi.org/10.1016/j.jimonfin.2006.06.001 doi: 10.1016/j.jimonfin.2006.06.001
|
| [20] |
Ji Q, Bouri E, Lau CKM, et al. (2019) Dynamic connectedness and integration in cryptocurrency markets. Int Rev Financ Anal 63: 257−272. https://doi.org/10.1016/j.irfa.2018.12.002 doi: 10.1016/j.irfa.2018.12.002
|
| [21] |
Ji Q, Zhang D, Geng JB (2018) Information linkage, dynamic spillovers in prices and volatility between the carbon and energy markets. J Clean Prod 198: 972–978. https://doi.org/10.1016/j.jclepro.2018.07.126 doi: 10.1016/j.jclepro.2018.07.126
|
| [22] |
Jiang S, Li Y, Lu Q, et al. (2022) Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets. Res Int Bus Financ 59: 101543. https://doi.org/10.1016/j.ribaf.2021.101543 doi: 10.1016/j.ribaf.2021.101543
|
| [23] |
Koutmos D (2018) Return and volatility spillovers among cryptocurrencies. Econ Lett 173: 122–127. https://doi.org/10.1016/j.econlet.2018.10.004 doi: 10.1016/j.econlet.2018.10.004
|
| [24] |
Liang Q, Lu Y, Li Z (2020) Business connectedness or market risk? Evidence from financial institutions in China. China Econ Rev 62: 101503. https://doi.org/10.1016/j.chieco.2020.101503 doi: 10.1016/j.chieco.2020.101503
|
| [25] |
McMillan DG, Speight AEH (2010) Return and volatility spillovers in three euro exchange rates. J Econ Bus 62: 79–93. https://doi.org/10.1016/j.jeconbus.2009.08.003 doi: 10.1016/j.jeconbus.2009.08.003
|
| [26] |
Mensi W, Al-Yahyaee KH, Kang SH (2019) Structural breaks and double long memory of cryptocurrency prices: A comparative analysis from Bitcoin and Ethereum. Financ Res Lett 29: 222−230. https://doi.org/10.1016/j.frl.2018.07.011 doi: 10.1016/j.frl.2018.07.011
|
| [27] |
Papathanasiou S, Dokas I, Koutsokostas D (2022) Value investing versus other investment strategies: A volatility spillover approach and portfolio hedging strategies for investors. N Am J Econ Financ 62: 101764. https://doi.org/10.1016/j.najef.2022.101764 doi: 10.1016/j.najef.2022.101764
|
| [28] |
Papathanasiou S, Kenourgios D, Koutsokostas D, et al. (2023) Can treasury inflation‑protected securities safeguard investors from outward risk spillovers? A portfolio hedging strategy through the prism of COVID-19. J Asset Manage 24: 198−211. https://doi.org/10.1057/s41260-022-00292-y doi: 10.1057/s41260-022-00292-y
|
| [29] | Papathanasiou S, Vasiliou D, Magoutas A, et al. (2024) The dynamic connectedness between private equities and other high-demand financial assets: A portfolio hedging strategy during COVID-19. Aust J Manage forthcoming. https://doi.org/10.1177/03128962231184658 |
| [30] |
Reboredo JC, Ugolini A (2020) Price connectedness between green bond and financial markets. Econ Model 88: 25−38. https://doi.org/10.1016/j.econmod.2019.09.004 doi: 10.1016/j.econmod.2019.09.004
|
| [31] |
Sadorsky P (2012) Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energ Econ 34: 248−255. https://doi.org/10.1016/j.eneco.2011.03.006 doi: 10.1016/j.eneco.2011.03.006
|
| [32] |
Samitas A, Papathanasiou S, Koutsokostas D, et al. (2022a) Are timber and water investments safe-havens? A volatility spillover approach and portfolio hedging strategies for investors. Financ Res Lett 47: 102657. https://doi.org/10.1016/j.frl.2021.102657 doi: 10.1016/j.frl.2021.102657
|
| [33] |
Samitas A, Papathanasiou S, Koutsokostas D, et al. (2022b) Volatility spillovers between fine wine and major global markets during COVID-19: A portfolio hedging strategy for investors. Int Rev Econ Financ 78: 629−642. https://doi.org/10.1016/j.iref.2022.01.009 doi: 10.1016/j.iref.2022.01.009
|
| [34] |
Savva CS, Aslanidis N (2010) Stock market integration between new EU member states and the Euro-zone. Empir Econ 39: 337–351. https://doi.org/10.1007/s00181-009-0306-6 doi: 10.1007/s00181-009-0306-6
|
| [35] |
So MKP, Chu AMY, Chan TWC (2021) Impacts of the COVID-19 pandemic on financial market connectedness. Financ Res Lett 38: 101864. https://doi.org/10.1016/j.frl.2020.101864 doi: 10.1016/j.frl.2020.101864
|
| [36] |
Tsuji C (2018) Return transmission and asymmetric volatility spillovers between oil futures and oil equities: New DCC-MEGARCH analyses. Econ Model 74: 167−185. https://doi.org/10.1016/j.econmod.2018.05.007 doi: 10.1016/j.econmod.2018.05.007
|
| [37] |
Tsuji C (2020) Correlation and spillover effects between the US and international banking sectors: New evidence and implications for risk management. Int Rev Financ Anal 70: 101392. https://doi.org/10.1016/j.irfa.2019.101392 doi: 10.1016/j.irfa.2019.101392
|
| [38] |
Virk N, Javed F (2017) European equity market integration and joint relationship of conditional volatility and correlations. J Int Money Financ 71: 53–77. https://doi.org/10.1016/j.jimonfin.2016.10.007 doi: 10.1016/j.jimonfin.2016.10.007
|
| [39] |
Wang P, Moore T (2008) Stock market integration for the transition economies: Time-varying conditional correlation approach. The Manchester School 76: 116–133. https://doi.org/10.1111/j.1467-9957.2008.01083.x doi: 10.1111/j.1467-9957.2008.01083.x
|
| [40] |
Wu F (2020) Stock market integration in East and Southeast Asia: The role of global factors. Int Rev Financ Anal 67: 101416. https://doi.org/10.1016/j.irfa.2019.101416 doi: 10.1016/j.irfa.2019.101416
|
| [41] |
Yilmaz K (2010) Return and volatility spillovers among the East Asian equity markets. J Asian Econ 21: 304−313. https://doi.org/10.1016/j.asieco.2009.09.001 doi: 10.1016/j.asieco.2009.09.001
|
| [42] |
Zhang D (2017) Oil shocks and stock markets revisited: Measuring connectedness from a global perspective. Energy Econ 62: 323−333. https://doi.org/10.1016/j.eneco.2017.01.009 doi: 10.1016/j.eneco.2017.01.009
|
| [43] |
Zhang D, Broadstock DC (2020) Global financial crisis and rising connectedness in the international commodity markets. Int Rev Financ Anal 68: 101239. https://doi.org/10.1016/j.irfa.2018.08.003 doi: 10.1016/j.irfa.2018.08.003
|
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Chikashi Tsuji. The historical transition of return transmission, volatility spillovers, and dynamic conditional correlations: A fresh perspective and new evidence from the US, UK, and Japanese stock markets[J]. Quantitative Finance and Economics, 2024, 8(2): 410-436. doi: 10.3934/QFE.2024016
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