Financial Markets Comovements in Northern Europe
| Název práce v češtině: | Vzájemné pohyby na finančních trzích v severní Evropě |
|---|---|
| Název v anglickém jazyce: | Financial Markets Comovements in Northern Europe |
| Akademický rok vypsání: | 2018/2019 |
| 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: | 21.05.2019 |
| Datum zadání: | 21.05.2019 |
| Datum a čas obhajoby: | 09.06.2020 09:00 |
| Datum odevzdání elektronické podoby: | 07.05.2020 |
| Datum proběhlé obhajoby: | 09.06.2020 |
| Oponenti: | Mgr. Václav Brož, Ph.D. |
| Kontrola URKUND: | |
| Seznam odborné literatury |
| 1. Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.
2. Gomes, P., & Taamouti, A. (2016). In search of the determinants of European asset market comovements. International Review of Economics & Finance, 44, 103-117. 3. Bekaert, G., Hodrick, R. J., & Zhang, X. (2009). International stock return comovements. The Journal of Finance, 64(6), 2591-2626. 4. Chen, P. (2015). Global oil prices, macroeconomic fundamentals and China's commodity sector comovements. Energy Policy, 87, 284-294. 5. DUTT, P., & MIHOV, I. (2013). Stock Market Comovements and Industrial Structure. Journal of Money, Credit and Banking, 45(5), 891-911. 6. Griffin, J. M., & Karolyi, G. A. (1998). Another look at the role of the industrial structure of markets for international diversification strategies. Journal of financial economics, 50(3), 351-373. 7. Cao, D., Long, W. & Yang, W. (2013). Sector Indices Correlation Analysis in China's Stock Market. Procedia Computer Science. 17. 1241-1249. 10.1016/j.procs.2013.05.158. |
| Předběžná náplň práce v anglickém jazyce |
| Research question and motivation
To understand the connections and links on the financial market is crucial for all market players. This thesis will focus mainly on stock markets in Northern Europe (i.e. in Sweden, Denmark, Finland, Baltic countries, etc.). The primary goal of this thesis is to discover and describe relationships in these markets, with an emphasis on mutual interactions between countries and economic sectors in Northern Europe. To estimate patterns, we will model volatility using GARCH approach and estimate pairwise conditional correlation. Contribution The results of this analysis help us to understand patterns among financial markets. This knowledge is crucial for modern portfolio theory, which is one of the foundations for trading in financial markets. The finding might be used for portfolio diversification to minimize concentration risk and maximize long-run profit. This thesis may also serve as a grounding for further studies and analysis the way that financial market works, especially in terms of mutual inner interactions. In past, there were various attempts to find patterns in stock markets correlations. Gomes & Taamouti (2016) discovered a statistically significant high correlation between western European countries. They also bring evidence that comovements are driven mainly by global factors. Bekaert et al (2009) shown that sector comovements in a country are stronger than cross country comovements. Dutt & Mihov (2013) claims that countries with similar industry structure are more correlated than other countries. However, Griffin & Karolyi (1998) found that cross county correlations cannot be explained by industrial sector decomposition. Cao et al (2013) studied sector indices in China and found that sectors are more correlated during dynamic growth of China’s economy in 2007-2008, but the comovements almost disappear after that period. Even though many studies have been made on sector correlation, we still do not have a unified robust theory which would fit perfectly to the real-world data. Methodology We will use GARCH approach to model variances and covariances of financial markets sectors, countries etc. and then standardized into correlations. GARCH models are widely used to model volatility based on historical data. The standard historical data from stock markets will be used. Historical prices of stocks and indices are available on NASDAQ Nordic website and partially on the Yahoo inance website. These data will be employed to model correlations. We will introduce proper hypotheses based on existing literature on correlations between financial markets. We expect to find strong cross-country correlations and even stronger between industrial sectors in each country. We also expect a statistically significant correlation between the same industry across different countries. Expected Outline 1. Introduction 2. Theoretical background and literature review 3. Methodology 4. Data description 5. Result discussion 6. Overview of applications 7. Summary |