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Dynamics of the volume-volatility relationship in the currency markets
Tůma, Adam ; Baruník, Jozef (advisor) ; Komárek, Luboš (referee)
This work investigates the volume-volatility relationship dynamics in the currency markets using data of five currency pairs in the period between 2010 and 2022. By employing multiple specifications of the HAR model with volume- related regressors and also with time-varying parameters (TVP), we examine the relationships' changing dynamics over time with a focus on improving volatility forecasting performance. Our main findings suggest a strong correlation between volume and volatility. The TVP-HARV model shows significantly changing dy- namics of the volume-volatility relationship, especially during periods affected by politics, changing monetary policies or global crises. The proposed models, however, do not improve out-of-sample volatility forecasting performance com- pared to the benchmark HAR model. The causal effect in the volume-volatility relationship in the currency markets is slightly more substantial in the direction of volatility towards volume, where we find slight forecasting improvements. Our findings conclude that volume and volatility in the currency markets are mainly moving simultaneously with a very strong correlation and much weaker and often insignificant causal effects on both sides, which supports the mixture of distributions hypothesis.
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Comparison analysis of selected Sustainable and Conventional Exchange-traded funds
Lapčáková, Kateřina ; Polák, Petr (advisor) ; Baruník, Jozef (referee)
In this thesis, we conduct a comparison analysis of sustainable and conventional exchange-traded funds in terms of their monthly returns in the global market during the sample period from 2018 to 2022. This thesis employs a multi-factor model approach using the CAPM, Fama-French 3-factor and Carhart 4-factor models that are used in the empirical analysis. In addition, risk-adjusted mea- sures such as Sharpe and Treynor ratio and Jensen's alpha are employed. The results imply that there is no significant di erence between the financial per- formance of the conventional and sustainable and socially responsible ETFs. JEL Classification F12, F21, F23, H25, H71, H87 Keywords exchange-traded funds, ETF, sustainability, ESG criteria, sustainable and responsible invest- ing, SRI, passive investing, performance mea- surement, comparison analysis Title Comparison Analysis Between Selected Sustain- able and Conventional Exchange Traded funds
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Examining the Interaction between the Cryptocurrency Market Development and Activity on Leading Social Networks
Doškář, Jakub ; Fanta, Nicolas (advisor) ; Baruník, Jozef (referee)
In this thesis, analyses are conducted to determine whether various measurements of social media activity, including the sentiment value of posts, can be drivers or even predictors of a change in a selected metrices of cryptocurrencies. The analyses are performed on data collected in one-hour and fifteen-minute time intervals from the February of 2021 until the November 2022. The results of the analysis show that variability of the closing price of Bitcoin can be to some extent explained by sentiment derivatives only. Furthermore, it was proven that sentiment derived from social media is significant when used as a predictor of a direction of a price change, under specific circumstances. These results oppose the previous studies, where sentiment was not recognized significant. Moreover, it was determined that considering one- hour intervals returns marginally better outcomes than in the case of shorter time intervals. The thesis outlines the challenges researchers can face when using this technique in their work. Keywords Cryptocurrencies, Bitcoin, Social sentiment, Sentiment analysis, Twitter, Social media, Cryptocurrency exchange Title Examining the Interaction between the Cryptocurrency Market Development and Activity on Leading Social Networks
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Three Essays on Data-Driven Methods in Asset Pricing and Forecasting
Gregor, Barbora ; Baruník, Jozef (advisor) ; Chen, Cathy Yi-Hsuan (referee) ; Baumohl, Eduard (referee) ; Vácha, Lukáš (referee)
This dissertation thesis consists of three papers focusing on applications of data-driven methods in asset pricing and forecasting. In the first paper, we decompose the term structure of crude oil futures prices using dynamic Nelson-Siegel model and propose to forecast them with the generalized regression framework based on neural networks. We find the neural networks to produce significantly more accurate forecasts as compared to several benchmark models. The second paper demonstrates how time-varying coefficients model can help to explore dynamics in risk-return trade-off on sovereign bond market across entire term structure. Our extensive 12-year dataset of high-frequency data of U.S. and German sovereign bond prices of 2-year, 5-year, 10-year and 30-year tenors allows us to construct realized measures of risk as well as exploring risk-return relationship under various market conditions. In addition to realized volatility, we find realized kurtosis to be priced in bond returns. Importantly, we detect the risk factor captured by realized kurtosis to have positive effect on returns in crisis turning to negative values in calm periods. In the third paper, we use time- varying coefficients methodology and higher realized moments in bond volatility forecasting challenging the HAR model. We detect realized...
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Climate Change Risk Premium, Stock Returns and Volatility Analysis in Relation to ESG Score
Barotov, Timur ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
The purpose of this study is to provide the evidence in regards to how the ESG score integration in the investment strategies affects the stock portfolio performances. The 10 year long panel data on European stocks were used to test how does the corporate ESG score correlate with returns and volatility on corporate stocks and does it (if at all) hold any explanatory power if added to popularly used asset pricing models. Data sample was divided in two based on long and short ESG reporting periods, where on each the analysis was performed separately. Furthermore, both the single sort and double sort analyses were performed to isolate size and ESG effects. Using Fama-MacBeth regression the results seem to suggest that investors are already pricing in the climate related risks as shown by the negative risk premium associated with high ESG firms. Returns and volatility of corporate stocks tend to be lower with higher ESG score, although not uniformly nor very significantly. Comparing Leaders portfolio showed that high (European) ESG scorers underperfomed S&P 500 index both in terms of return and volatility.
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Stock Trading Using a Deep Reinforcement Learning and Text Analysis
Benk, Dominik ; Baruník, Jozef (advisor) ; Vácha, Lukáš (referee)
The thesis focuses on exploiting imperfections on the stock market by utilizing state-of-the-art learning methods and applying them to algorithmic trading. The automated decisions are expected to have the capability of outperforming professional traders by considering much more information, reacting almost instantly and being unaffected by emotions. As an alternative to traditional supervised learning, the proposed model of reinforcement learning employs a principle of trial-and-error, which is essential for learning behaviours of all organisms. In the context of stocks, this allows to consider the involved uncer- tainty and therefore more precisely estimate the long-run returns. To collect the most relevant information for each trading decision, additionally to tech- nical indicators the models build on investor's opinion - financial sentiment. This is derived from two textual sources, news and social media, and the main goal is to compare their relative contribution to trading. Models are applied to 11 different stocks and later combined into portfolio for greater robustness of results. The textual analysis proves to be important for the learning process, especially in case of stocks with good media coverage. The Twitter is found to provide more valuable information compared to news, but their...
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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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How Does Bitcoin React to Economic Uncertainty Volatility Shocks?
Láža, Jakub ; Šíla, Jan (advisor) ; Baruník, Jozef (referee)
This thesis explores the volatility connectedness between Bitcoin and economic uncertainty. We aim to model reactions of Bitcoin's volatility to shocks in economic uncertainty to uncover whether Bitcoin can provide protection from an economic unrest. The uncertainty is assessed from the media-based Eco- nomic Policy Uncertainty (EPU) Index, the market-based VIX Index and the public-based Economic Queries Related Uncertainty (EURQ) Index. Using the dynamic network connectedness measure, it is possible to track the time evolu- tion of directional volatility spillovers in each time point of our dataset spanning from April 2015 to February 2022. Our results show several significant periods when Bitcoin receives volatility spillovers from economic uncertainty. However, in most cases, the e ect is weak. One exception is the COVID-19 crisis, during which Bitcoin forms a substantial volatility connectedness with the VIX Index. We also show that before 2020, Bitcoin reacts to several shocks driven by the EPU Index. Further, amid inflation fears at the end of 2021, the volatility spillovers mainly originate from the EURQ Index.
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Gold as a Stable Asset in Economic Recession: An Econometric Analysis
Petrželka, Václav ; Baruník, Jozef (advisor) ; Kukačka, Jiří (referee)
Due to its reliability, durability and rarity, gold has been seen for centuries as a safe haven investment that should prevent large losses during financial crises. However, the question arises whether this characteristic is still relevant for gold. In our thesis, we distinguish between two main aspects of a safe haven asset, namely the degree of volatility and the ability to predict as accurately as possible the evolution of the volatility of a given asset. The major economic crises of the 20th century show us that the volatility of gold during them was lower than that of other assets. We therefore follow up with a detailed analy- sis comparing the volatility of daily returns for gold, stocks, commodities and cryptocurrencies over the period 2006-2021. We find that gold volatility was indeed lowest during the Great Recession after 2007 and after the outbreak of the Covid-19 pandemic in 2020. We also confirm an asymmetric response to negative returns for stocks and commodities, which is not the case for gold and cryptocurrencies. We test the ability to predict assets by comparing predicted daily volatilities and realized daily volatilities over more than a six-month inter- val in 2014 and 2021. We find no relationship to confirm that gold has higher predictability than other assets. Our findings...
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Influence of stock market variables on correlations among S&P sectors
Coufal, Matěj ; Čech, František (advisor) ; Baruník, Jozef (referee)
This thesis investigates the influence of the exogenous variables (S&P 500 Index, 10-year US Treasury Note, crude oil, and CBOE Volatility Index (VIX)) on the dynamics of correlations among S&P sectors. We concentrate on daily and weekly investment horizons, and employ the bivariate Dynamic Conditional Correlation (DCC) model. Changes in correlations implied by the DCC model are further modelled using the exogenous variables. The results indicate that VIX has the best ability to predict future changes in correlations. An increase in VIX on day (week) t is expected to cause a rise in correlations on day (week) t + 1. Next, correlations of the Energy sector tend to increase in weeks when crude oil prices are falling. Further, correlations of the Information Technology sector are likely to increase on days of rising yield on the 10-year US Treasury Note. Although we detect a certain power to predict future changes in correlations, very little of these changes is actually explained. 1
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