Stock market comovements in Central and Eastern Europe during the COVID-19 pandemic and the Russian war in Ukraine.

• Název práce v češtině: Vzájemné pohyby akciových trhů ve střední a východní Evropě během pandemie COVID-19 a ruské války na Ukrajině.
• Název v anglickém jazyce: Stock market comovements in Central and Eastern Europe during the COVID-19 pandemic and the Russian war in Ukraine.
• Klíčová slova anglicky: comovements, stock market, Central Europe, Eastern Europe, Baltic, volatility modelling, Covid-19, Ukraine
• Akademický rok vypsání: 2022/2023
• Typ práce: bakalářská práce
• Jazyk práce: angličtina
• Ústav: Institut ekonomických studií (23-IES)
• Vedoucí / školitel: PhDr. František Čech, Ph.D.
• Řešitel: skrytý - zadáno vedoucím/školitelem
• Datum přihlášení: 19.06.2023
• Datum zadání: 19.06.2023
• Datum a čas obhajoby: 11.06.2024 09:00
• Místo konání obhajoby: Opletalova, O105, místnost č. 105
• Datum odevzdání elektronické podoby: 27.04.2024
• Datum proběhlé obhajoby: 11.06.2024
• Oponenti: Mgr. Lenka Nechvátalová

Seznam odborné literatury

Bibliography
1. Longin, F., & Solnik, B. (2001). Extreme Correlation of International Equity Markets. Journal of Finance, 56(2), 649–676.
2. Gjika, D., & Horvath, R. (2013). Stock market comovements in Central Europe: Evidence from the asymmetric DCC model. Economic Modelling, 33, 55–64
3. Nikkinen, J., Piljak, V., & Äijö, J. (2012). Baltic stock markets and the financial crisis of 2008–2009. Research in International Business and Finance, 26(3), 398–409
4. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327
5. Hardouvelis, G. A., Priestley, R., & Malliaropulos, D. (2001). EMU and European Stock Market Integration. Social Science Research Network.
6. Caporale, G. M., & Spagnolo, N. (2011). Stock Market Integration between Three CEECs, Russia, and the UK. Review of International Economics, 19(1), 158–169
7. He, Q., Liu, J., Wang, S., & Yu, J. Y. (2020). The impact of COVID-19 on stock markets. Economic and Political Studies, 8(3), 275–288.
8. Baig, A. S., Butt, H. A., Haroon, O., & Rizvi, S. S. (2021). Deaths, panic, lockdowns and US equity markets: The case of COVID-19 pandemic. Finance Research Letters, 38, 101701.
9. Whelsy Boungou, Alhonita Yatie. The impact of the Ukraine-Russia war on world stock market returns. 2022. ffhal-03623580f

Předběžná náplň práce v anglickém jazyce

Research question and motivation
Diversification is an essential part of portfolio management. The effectiveness of portfolio diversification is commonly quantified through the correlation coefficient of assets within the portfolio. During the 1990s and early 2000s investing into Central and Eastern European stock markets had been a common way of diversifying as they were considered more segmented than the developed markets, however, many have since argued that this benefit had diminished during the 2000’s due to increasing European market integration.
To illustrate this point we can look at Hardouvelis et. al (2001), who find significant stock market integration within the Eurozone during the 1990’s. Later on, Gjika & Horvath (2013) find increasing correlation between the stock markets of Czech Republic, Poland and Hungary and the STOXX50 index between the years 2001-2008. This increase is thought to be caused in large part by the accession of these countries into the European Union in 2004. However, the correlation increase seemingly stopped in 2009, possibly due to the fallout of the Great Financial Crisis. In addition, Caporale & Spagnolo (2011) find significant comovements between Czech, Polish and Hungarian and Russian stock markets for the period 1996-2008.
For the Baltic region, Nikkinen et. al (2012) find limited correlation between Baltic and Western European stock markets before the Great Financial Crisis, however, they find a substantial increase in correlation between the two regions during the crisis. This finding is in line with the well documented phenomenon of correlation between financial markets increasing during crises, see for example Longin (2001). These findings cast into serious doubt the efficacy of diversifying into Central and Eastern European markets.
With regards to the effects of the recent COVID-19 pandemic and the Russian war in Ukraine, He et. al (2020) find a short-term negative effect and a spill-over effect between the major Asian, American and European stock markets during the pandemic in 2020. Baig et. al (2021) suggest that in the US increases in COVID-19 cases are associated with increased market volatility. Lastly, Boungou & Yatie (2022) find a negative effect of the Russian war in Ukraine on the global markets during February-March 2022 with the effect being more pronounced in countries bordering Ukraine and Russia.
Contribution
In this thesis we will be assessing comovements between post-Soviet bloc countries and the Eurozone and Russia between the years 2013-2022. Closer attention will be given to the period 2020-2022 due to the COVID-19 pandemic and the Russian war in Ukraine and whether these events had a lasting impact in the markets examined. Similar studies have been done on American markets or European markets as a whole, see for example Baig et. al (2021) or Boungou & Yatie (2022). There have also been numerous studies on European market integration in Central and Eastern Europe during the 2000’s and 2010’s, see for example Gjika & Horvath (2013). Nevertheless, to the best of our knowledge, none have studied the microcosm of these regions and their relation to Western European and Russian markets during the most recent years. Furthermore, most of the papers usually focus on one subregion in particular, such as Central Europe or the Baltics.
We will attempt to ascertain the current relevance of investing into these regions as a diversification strategy and assess the degree of integration with the European markets and Russia. Furthermore, these findings may help in judging the vulnerability of these markets to instability in the region.
Methodology
We will use daily market data of the STOXX50 index, Visegrad Four countries, the Baltics, Romanian, Bulgarian and Russian indices from 2013-2022 available at Investing.com. To model the volatility we will use a multivariate GARCH model. The original GARCH model was proposed by Bollerslev (1986). The GARCH model and its modifications have since seen wide application in time series modelling. We will examine the effect of the COVID-19 pandemic and Russian war in Ukraine on the volatility and correlation of the indices mentioned above. We expect to find increasing correlation especially shortly after the initial COVID-19 outbreak and the initial outbreak of war in Ukraine.
Outline
1. Introduction
2. Literature Review
3. Methodology
4. Results
5. Conclusion