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Cusp catastrophe theory: Application to the housing market
Kořínek, Vojtěch ; Kukačka, Jiří (advisor) ; Nevrla, Matěj (referee)
The bachelor's thesis applies the stochastic cusp catastrophe model to the housing market of the United States. Weekly data over the period from 2007 to 2017 are used. The current catastrophe theory literature related to the housing market is reviewed, the models found are assessed and expanded. Specifically, we have identified three deficiencies of the catastrophe models applied to housing market in the current literature and our contribution lies in the elimination of these deficiencies. In order to satisfy the constant volatility assumption of the model, the state variable is normalized by the estimated volatility derived from GARCH. Furthermore, multiple control variables are added to the model to represent the activity of fundamentalists and chartists. The results suggest that the cusp catastrophe model fits the data better than the linear and logistic models. The normalization of the state variable improves the model performance while the introduction of the additional control variables does not produce better results. Keywords Housing market, catastrophe theory, stochastic cusp catastrophe model, hous- ing bubble, real estate, fundamental investors, speculation. 1
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Good vs. Bad Volatility in Major Cryptocurrencies: The Dichotomy and Drivers of Connectedness
Šíla, Jan ; Kočenda, Evžen ; Kukačka, Jiří ; Krištoufek, Ladislav
Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. The results suggest that crypto connectedness reflects important events and exhibits more variable and cyclical dynamics than those of traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that information about market downturns spills over substantially faster than news about comparable market surges. Overall, the connectedness dynamics are predominantly driven by fundamental crypto factors, while the asymmetry measure also depends on macro factors such as the VIX index and the expected inflation.
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Comparison of Different Investment Opportunities during Unstable Times
Filonau, Ilya ; Kalabiška, Roman (advisor) ; Kukačka, Jiří (referee)
The goal of this diploma thesis is to take an insight into the world of investment during unstable economic times based on the example of the selected country, Germany. The thesis seeks to analyze the performance of five different investment options, real estate, REIT, Stock market, Gold and Bitcoin, and compare them, while identifying the most important macroeconomic factors influencing the value of the investment options. The methodology of the diploma thesis is represented by a time series analysis based on the time period between the first quarter of 2000 and the first quarter of 2023. Additionally, the technique of econometric estimation is applied, where, in total, six models are created. In the end, it is concluded that gold is the most superior investment choice, while the least attractive one is the REIT Index, representing indirect real estate investment due to its relatively unstable nature and unpredictability. JEL Classification J11, R30, D81, G11, E27 Keywords Real Estate, COVID-19, Investment, REIT, Stock Index, Gold, Bitcoin, Economic Recessions, Germany, Portfolio, Risk Title Comparison of Different Investment Opportunities during Unstable Times
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Prediction of Czech GDP using mixed-frequency machine learning models
Kotlan, Ivan ; Polák, Petr (advisor) ; Kukačka, Jiří (referee)
The goal of this study is first to provide superior predictions of Czech GDP growth to the o cial estimates of the Czech Statistical O ce and the proxy estimation of the Czech National Bank. Secondly, to expand the literature that focuses on machine-learning predictions that utilizes data with various sampling frequency. Although in the first goal, this thesis did not succeed as all models, namely Ridge and Random Forest, failed to beat the predictions of o cial institutes, the thesis contributes to the yet scarce literature on mixed-frequency machine-learning prediction. Since no machine-learning model accounts for data with various frequencies, the thesis shows how to transform variables so that any machine-learning model can utilize them. Furthermore, di erent dataset modifications are explored, such as the prediction time: end of the reference quarter (nowcast) and 40 days after the reference quarter (backcast), standardized and non-standardized datasets. And finally, for the superior Ridge model, the e ect of so-called high-frequency variables (sampled every week) is explored. While Random Forest showed little e ect by using di erent versions of the dataset, in the case of the Ridge model, the type of dataset had a significant e ect. While the non-standardized Ridge produces better overall...
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The Impact of News on Videogame Stock Market Prices and Volatility
Mertová, Veronika ; Čech, František (advisor) ; Kukačka, Jiří (referee)
The thesis investigates the impact of social media and news headline sentiment on stock prices, specifically comparing gaming firms to companies from other industries. Tweets and news headlines containing keywords referring to four selected gaming and four non-gaming companies were collected over 5 and 3 months, respectively. Both tweets and news collected came from the general users or media rather than focusing solely on financial ones. The data were aggregated into daily values. Daily stock price data were also collected for each examined company to derive returns and volatility. The data were analysed using a vector autoregression model in combination with Granger causality. The study found no significant differences between gaming and non-gaming sectors. The polarity of sentiment showed no effect on stock prices. However, when sentiment was divided into different emotions, some significance was observed, although the findings varied across individual firms regardless of their sectors. It was concluded that when using sentiment for market predictions, it is beneficial to either utilize specifically financial media or determine the specific type of sentiment that influences a particular stock. JEL Classification G14, G17, C32, C58 Keywords Tweets, News Headlines, Gaming Industry, Sentiment...
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Gambler's Fallacy in Investors' Decision-making
Javůrková, Tereza ; Kukačka, Jiří (advisor) ; Červinka, Michal (referee)
This thesis focuses on the Gambler's Fallacy and its effect on the behavior of investors operating in the stock market. The aim is to incorporate the psychological findings about this behavioral phenomenon to the field of finance. This allows us to analyze the dynamics of the stock market that results from human misconceptions about the probabilities of independent events. More specifically, we analyze the profitability of two types of virtual investors whose decision-making is affected by distorted probabilities based on the Gambler's Fallacy. We further define two other trivial benchmark investors' strategies with different levels of randomness. We examine investors' gains in a simulated efficient market as well as in the real S&P 500 index constituents. Our analysis builds on three different approaches: simulation analysis, empirical frequency analysis, and asset pricing models. By applying the simulation approach together with frequency analysis on the historical stock prices, we find that investors affected by the Gambler's Fallacy gain statistically higher returns than a random investor. Then, we ap- ply both the three-factor and five-factor Fama & French asset pricing model to stocks sorted into portfolios based on their previous earnings per share evo- lution. Our findings reveal a negative...
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Determinants of Used Car Prices
Žiačik, Jan ; Baruník, Jozef (advisor) ; Kukačka, Jiří (referee)
With regard to the market share, used car market is on equal footing with market with new cars. Given its relevancy, there is an incentive to better understand its inner workings. One of the questions that can be posed, relevant especially to private individuals that want to buy or sell their car, is how the prices on used car market are determined. This research question was already focus of several previous studies, nevertheless, there are several methodological issues with them, the major being that they do not deal with model uncertainty arising from large number of possible determinants of used car prices. Therefore, the goal of this thesis is to address this shortcoming by implementing a new approach, Frequentist Model Averaging. To analyze the research question, we utilize a newly collected data set of more than 470 000 used car advertisements from several different European countries from website carvago.com. In addition to well established influence of technical attributes on the valuation of car, we also find evidence that the emission standard of car or the country that the car manufacturer is originating in, have also statistically significant effect on its valuation. Further, we also find, that there are significant regional differences in effects of different attributes. JEL...
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Analysis of herd behavior across cryptocurrencies
Krouská, Kateřina ; Kukačka, Jiří (advisor) ; Fanta, Nicolas (referee)
This thesis studies herding behavior in the cryptocurrency market between 2017 and 2022. Results from the static model reveal significant imitative behavior in the up market and during the bull year 2017. In addition, this thesis ranks among the first papers that study the effect of the early stage of the war in Ukraine on the market-wide herding behavior. Furthermore, due to Bitcoin's dominant position among other coins, closer attention is devoted to studying its influence on the herding behavior in the market. However, herding seems to be present only during extreme Bitcoin movements. In response to these results, five dominant coins (Bitcoin, Ethereum, XRP, Litecoin and Dogecoin) are excluded from the sample and their influence on the rest of the market is studied. The evidence suggests strong herding behavior of the rest of the market around these five giants. Therefore, the return of smaller coins seems to be influenced by the performance of larger coins, rather by solely that of Bitcoin. JEL Classification G02, G15, G40, C22, C58 Keywords Cryptocurrencies, Herding behavior, Bitcoin, COVID-19 Title Analysis of herd behavior across cryptocurren- cies Author's e-mail 43789078@fsv.cuni.cz Supervisor's e-mail jiri.kukacka@fsv.cuni.cz
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Application of a Financial Agent-Based Model to the Cryptocurrency Market
Bielaková, Tatiana ; Kukačka, Jiří (advisor) ; Petrásek, Lukáš (referee)
Motivated by the occurrence of financial stylized facts (also) in the cryptocur- rency markets, we study their dynamics by applying one of the most well- known financial agent-based models to them. Based on interactions between two boundedly rational types of traders, this modeling framework nests eight submodels using four attractiveness specifications and two switching mecha- nisms between the trading strategies. The analysis is based on three types of datasets - S&P500 to receive a benchmark to the previous research and a comparison with crypto markets, Bitcoin, and a hypothetical market-weighted Top20 cryptocurrency index. For the estimation, we utilize the simulated method of moments, a technique commonly used in complex models where analytical solutions are not feasible. Overall, the results for cryptocurrency datasets imply a very promising application of agent-based models to the anal- ysis of crypto markets. Particularly, for Bitcoin, all submodels produce data in close agreement with the empirical data-generating process. We attribute the robust rank of results to the low level of rationality of the studied markets. However, we are unable to directly interpret the evolution of the trading groups due to the lack of the resulting group dynamics. We identify a similar prob- lem in several...
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Prospect Theory in the Cryptocurrency Market
Coufalová, Kristýna ; Kukačka, Jiří (advisor) ; Kučera, Tomáš (referee)
This thesis investigates the potential of cumulative prospect theory to ex- plain future cryptocurrencies' returns. Moreover, the study aims to determine whether the predictive power of cumulative prospect theory value persists when cumulative prospect theory value is computed by plugging the percentage form of return (for instance, 5%) instead of the decimal form (for instance, 0.05). Using a rolling sample of 200 cryptocurrencies with the highest market capitali- sation for each month from March 2017 to March 2023, we found that regardless of using returns in percentage or decimal form, the cumulative prospect theory value function produces comparative abnormal portfolio returns and confirms the hypothesis that cryptocurrencies with high (low) cumulative prospect the- ory value earn low (high) subsequent returns. JEL Classification G11, G12, G41 Keywords Prospect theory, Cumulative Prospect Theory, Cryptocurrency, Behavioural Economics Title Prospect Theory in the Cryptocurrency Market Author's e-mail 31078966@fsv.cuni.cz Supervisor's e-mail jiri.kukacka@fsv.cuni.cz
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