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Dynamic Network Risk across main U.S. sectors
Malecha, Jan ; Baruník, Jozef (advisor) ; Čech, František (referee)
We study the effects of financial networks formed by the connectedness of stock return volatilities within sectors of the S&P 500 Index. We test whether the risk arising from dynamic volatility connections is priced in the cross-section of stock returns. Separately, for each sector, we estimate the dynamic network formed by firm-level realized volatilities from 2006 to 2018. We study how connectedness differs across sectors. Comparing the sector results, we conclude that there is a homogeneous pattern that describes the development of volatility connectedness. The pattern holds across all sectors throughout the studied period and is shaped by major financial events. We create risk factors that attempt to assess the risk arising from dynamic volatility connections. For each sector, we create a factor model that we test using the Fama-Macbeth regression. The results provide evidence that the created risk factors are priced in four out of ten sectors, that is, significant results are found in the Energy, Financials, Industrials, and Consumer Discretionary sectors.
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Innovation Indicator Analysis in the European Union: A Machine Learning Approach
Malecha, Jan ; Pleticha, Petr (advisor) ; Semerák, Vilém (referee)
The European Commission annually publishes a European Innovation Scoreboard (EIS) as a tool to measure the innovation performance of the EU Member States. This thesis extends the analysis published in the EIS 2018 in two different manners. The first part, a clustering analysis, examines the partition of the EU Member States to innovation performance groups. The thesis comes with a unique scheme of partition created by using hierarchical clustering. A comparison with the existing scheme shows that the general trends are similar in both schemes. The only main exception is the differentiation of the British Isles and Luxembourg apart from the other high performing countries. The proposed scheme provides insight about the within-cluster similarities, such as the similarity of Finland, Sweden and Denmark and their relative distinction from France, although they belong to one cluster. The second part, a regression analysis, attempts to examine the impact of innovations on real labour productivity. Contrary to existing literature, we do not find a statistically significant relationship between productivity and the components of the EIS. Additionally, the analysis is extended by the lasso estimation that provides a variable selection. The latter approach improves our findings and identifies four EIS...
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