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GDPNow for the Czech Republic
Kutman, Jan ; Havránek, Tomáš (advisor) ; Kukačka, Jiří (referee)
The gross domestic product (GDP) is an essential measure of the state of economic activity and serves as a crucial tool for policymakers, investors, or businesses. However, the official GDP estimate in the Czech Republic is only available with a lag of approximately 60 days, and the Czech National Bank (CNB) announces its GDP forecast once in each quarter. This thesis focuses on predicting GDP growth in the current quarter, referred to as nowcasting. I employ several methods to nowcast the real GDP growth in the Czech Republic in a pseudo-real-time setting and compare their performance. Additionally, I investigate the possibility of creating an ensemble model by using a weighted average of several nowcasting models. The results suggest that the Dynamic Factor Model (DFM) performs best in the GDP nowcasting task, and its predictive accuracy is comparable with the official CNB nowcast. Furthermore, the model averaging process yields accuracy close to the best individual model while addressing model uncertainty. The GDP nowcast of the DFM will be made available to the public in real-time on a website and updated with a daily frequency.
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Herd Behaviour in Financial Markets: Evidence from the Technology Sector
Máca, Jaroslav ; Kukačka, Jiří (advisor) ; Hronec, Martin (referee)
This thesis provides an evidence of herd behaviour in financial markets with an emphasis on the technology sector. The adjusted closing prices for the NASDAQ-100 index constituents are analysed on a daily basis during the period 2011-2020. Regarding methodology, the commonly utilized measures of cross-sectional standard deviation of returns and of cross-sectional absolute deviation of returns are considered. The examination reveals no evidence of herd behaviour, even when filtering trading sessions based on extraordinary market volatility or trading volume. However, a closer look at 2020, in which financial markets movements were heavily affected by the ongoing COVID-19 pandemic, shows that herd behaviour contributed to the sharp and significant crash as well as to the subsequent skyrocketing recovery. Furthermore, this thesis presents an innovative way of using an external factor in regression models. Due to their dominant position, the so-called technology giants are excluded from the US stock market and they newly constitute the world market. This specification reveals that the dispersions of the technology giants are contagiously amplified to the rest of the technology sector. Therefore, investors should be aware of the risks associated with a possible cooling of the entire technology sector following...
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The influence of watching videogame streams on purchase decisions of gamers and their willingness to pay, evidence from the Czech Republic
Mertová, Veronika ; Polák, Petr (advisor) ; Kukačka, Jiří (referee)
This thesis aims to understand the relationshipbetween viewership of video game streams and purchase decisions players make. Furthermore, the price they are willing to pay is explored. The emphasis is on understandingthe difference in these effects for big blockbuster games and small independentlydevelopedtitles. The data was collectedusingan online survey distributed in gaming-focusedgroups on social media. The data on purchase decisions was analysedusing a logit model. It showed that trust in streamer's recommendations increases the chance of purchase along with the number of preferred genres and games bought for indie titles. Moreover,it showed a positive relationshipbetweenbeing a student and purchasinga big game after watching. A standard ordinary least squares model was used to analyze the price and showed that hardcore gamers, people who buy on release day, and people with a wider range of interest in games tend to pay more. On the other hand, older people, students, and people who prefer to buy games on sale are willing to pay less. Keywords Video games, streaming,indie, AAA, willingness to pay, logit,Czech Republic Title The influence of watching videogame streams on purchase decisions of gamers and their willingness to pay, evidence from the Czech Republic
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High-Frequency Food Prices: Evidence from the Czech Republic
Pavlovová, Anna ; Havránek, Tomáš (advisor) ; Kukačka, Jiří (referee)
With increasing engagement in on-line shopping accelerated by the events of 2020, what can we learn about prices and their rigidity in the on-line sector? We collect an extensive dataset of scraped daily prices for four on-line grocery retailers from the Czech Republic from January 2020 to April 2021. We find substantial di erences in pricing among the retailers, including the impact of interest rate changes and the introduction of lockdowns on the probability of price change. Price rigidity depends significantly on the retailer and the assumptions imposed on temporary price changes. The mean number of all price changes among retailers ranges between 3.10 and almost 11 per year. Depending on the definition of excluded temporary price changes, retailers change prices permanently on average between 0.68 to 4.04 times per year. We show that a more in-depth analysis of temporary price changes is crucial for a robust assessment of price rigidity. JEL Classification E30, L81, C55, D22 Keywords on-line grocery shopping, price flexibility, tem- porary price changes, scraped prices Title High-Frequency Food Prices: Evidence from the Czech Republic
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Forecasting with neural network during covid-19 crisis
Luu Danh, Tiep ; Baruník, Jozef (advisor) ; Kukačka, Jiří (referee)
The thesis concerns the topic of forecasting using Neural Networks, particu- larly the return and volatility forecasting in the volatile period of Covid-19. The thesis uses adjusted close daily data from Jan 1, 2000, until Jan 1, 2021, of the S&P index and Prague Exchange Stock index (PX). The comparison was between the vanilla econometrical model, a neural network model, and a hybrid neural network model. Hybrid neural networks were constructed with an additional feature column of the fitted econometrical model. Additionally to comparing the prediction, a risk-return trade-o analysis of the forecasted series was conducted. The test period for all models was from Jan 1, 2020, until Jan 1, 2021, where predictions were made. During the test period, MSE be- tween predicted and true values was extracted and compared. The results are that the hybrid model outperformed both econometrical as well as only neural networks models. Furthermore, the risk-return trade-o forecast provided by the hybrid model fares better than the other ones. JEL Classification C53, C81 Keywords Financial Time Series, Forecasting, Neural Net- works, ARIMA, GARCH Title Forecasting with Neural Network during Covid- 19 Crisis Author's e-mail tiep.luud@gmail.com Supervisor's e-mail barunik@fsv.cuni.cz
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Careless society: Drivers of (un)secure passwords
Nedvěd, Vojtěch ; Baruník, Jozef (advisor) ; Kukačka, Jiří (referee)
Careless Society: Drivers of (Un)Secure Passwords Thesis abstract Vojtěch Nedvěd May 2, 2021 Vulnerabilities related to poor cybersecurity are a dangerous global economic issue. This thesis aims to explain two examples of poor password management. First, why users use similar password and username and second, why they reuse their passwords, as the main drivers of this behaviour are unknown. We examined the effects of selected macroeconomic variables, gender, password length and password complexity. Additionally, this thesis suggest how to estimate sentiment in passwords using models build on Twitter posts. The results are verified on large password data, including password leaks from recent years. There are four main findings. First, a higher cybersecurity index and diversity of a password seem to be related to the lower similarity between a username and a password. Second, it seems that there are structural differences between countries and languages. Third, the sentiment seems to be a significant determinant too. Fourth, password reuse seems to be positively affected by the cybersecurity level. The thesis contributes to the study of password management. It proposes how to model the relationship, derive the data, split the passwords into words, model the sentiment of passwords, what variables might be...
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Evaluating the predictability of virtual exchange rates using daily data
Řanda, Martin ; Polák, Petr (advisor) ; Kukačka, Jiří (referee)
Virtual worlds have garnered the attention of researchers from various disci- plines and are viewed as particularly valuable to economists due to their open- ended design. In this thesis, we review a popular online multiplayer game's economy and focus on exchange rate predictability in a virtual setting as only a limited body of literature investigated this topic. The well-established unpre- dictability puzzle is addressed by exploiting a unique daily time series dataset using a vector autoregressive framework. Apart from a significant Granger- causal relationship between the virtual exchange rate and the player popula- tion, the system is shown to be less interconnected than expected. Furthermore, an out-of-sample exercise is conducted, and the forecasting performance of our models is examined in comparison to that of a simple no-change benchmark in the short term. Based on the evaluation methods used, the two measures of the virtual exchange rate are found to be somewhat predictable. We suggest two explanations for this inconsistency between the virtual and real-world exchange rates: data frequency and lack of complexity in the considered online economy.
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Is hype really that powerful? The correlation between mass and social media and cryptocurrency rates fluctuations
Ilina, Viktoriia ; Král, Michal (advisor) ; Kukačka, Jiří (referee)
Twelve years after Satoshi Nakamoto published the paper describing the functioning mechanism and principals of cryptocurrency that maintains secure and anonymous digital transactions beyond any banks, cryptocurrencies have become a multi-billion-dollar industry comprising millions of investors, miners, developers and profiteers. However, the actual price determinants and ways to forecast future price changes remain an open question yet to discover the answer for. This study attempts to figure out whether media hype exerts that much influence upon cryptocurrencies price movements and whether it can be used as the basis for future movements prediction. Two cryptocurrencies, Bitcoin and Tezos, and 7 mass and social media factors for each of them were considered on daily basis from 08-01-2018 to 10-31-2020. To explore the interdependence between media drivers and cryptocurrencies' prices in short, medium and long timespan, this study deploys wavelet coherence approach. There was found, that price changes turn to be the supreme prior to hype, even though the growing ado may push the prices even higher. Thus, hype is failing to prove itself as a reliable cryptocurrency price predictor. Crypto investors, though, should anyways take the news background into account while building trading strategies,...
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