National Repository of Grey Literature 198 records found  1 - 10nextend  jump to record: Search took 0.01 seconds. 
Forks and airdrops in cryptomarkets: Investment opportunities or thin air?
Hotovec, Petr ; Krištoufek, Ladislav (advisor) ; Kurka, Josef (referee)
Cryptocurrencies present a relatively new field of study where not much re- search has been done on the effects of announcements on cryptocurrency re- turns. This thesis examines the effect of hard fork and airdrop announcements on cryptocurrency returns using the event study methodology. Fork and airdrop announcements are studied on 22 cryptocurrencies from the top 100 cryptocur- rencies ranked by their market capitalization and the results show that average abnormal returns are not statistically significant on the day of the announce- ment which is in stark contrast to most of the evidence from the stock markets and implies market inefficiency due to a 2 day lag before average abnormal re- turns become statistically significant. Our interpretation of the results is that information on cryptocurrencies are very confusing and unreliable and investors wait for their confirmation, hence the two day delay. Keywords cryptocurrency, airdrop, hard fork Title Forks and airdrops in cryptomarkets: Invest- ment opportunities or thin air? Author's e-mail Supervisor's e-mail
Modeling Dynamics of Correlations between Stock Markets with High-frequency Data
Lypko, Vyacheslav ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
In this thesis we focus on modelling correlation between selected stock markets using high-frequency data. We use time-series of returns of following indices: FTSE, DAX PX and S&P, and Gold and Oil commodity futures. In the first part of our empirical work we compute daily realized correlations between returns of subject instruments and discuss the dynamics of it. We then compute unconditional correlations based on average daily realized correlations and using feedforward neural network (FFNN) to assess how well the FFNN approximates realized correlations. We also forecast daily realized correlations of FTSE:DAX and S&P:Oil pairs using heterogeneous autoregressive model (HAR), autoregressive model of order p (AR(p)) and nonlinear autoregressive neural network (NARNET) and compare performance of these models.
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.
Impact of total transaction fees on the price of Bitcoin and Ethereum
Černý, David ; Krištoufek, Ladislav (advisor) ; Hronec, Martin (referee)
The aim of this thesis is to explore the price dynamics of Bitcoin and Ethereum with special emphasis on the role of transaction fees, which can provide insight into network congestion and user behaviour, and may also reflect the future economic viability of these networks. Previous research has shown intertwin- ing relationships between variables and suggested possible endogeneity in a cryptoasset environment. For these purposes, a system of two simultaneous equations for transaction fees and price was developed and subsequently esti- mated using the 2SLS method. The analysis covers relationships from both long-term and short-term perspectives. It turns out that the price dynamics of both assets is determined by a diverse mix of fundamental, economic and spec- ulative factors, despite the narrative that the price of cryptoassets is primarily driven by speculative factors. Furthermore, in the context of the fee-price re- lationship, it turned out that the relationship is a priori that the price impacts the fees, however, at some intervals, the opposite relationship is also shown, which is rather an exception. An important contribution could be the finding of a stable positive effect of the total number of active addresses in Bitcoin on transaction fees, which might bring new insights to the...
Price Dynamics of Automated Market Makers: Simulation-based Approach
Kubal, Jan ; Krištoufek, Ladislav (advisor) ; Čech, František (referee)
The aim of this thesis is to analyze the price dynamics implied by the Automated Market Makers used by Decentralized Exchanges of DeFi and to verify the presence of some behavioral patterns with a simulation-based approach. Returns from 10 representa- tive token pairs were collected over a 15-day period and their properties were compared against traditional stylized facts. A simulation that reproduces the observed price pro- cess was then developed, mimicking the realized swap orders and utilizing the constant product pricing equation of AMMs, incorporating two additional features that implement periods of hype and herding behavior. Analysis of the empirical data revealed that AMM token returns follow the stylized facts most of the time, with their distributional properties, autocorrelation patterns, and volatility clustering. No consistent trend in the leverage effect was found among tokens. The simulation then confirmed that a basic AMM model is sufficient in producing prices with similar returns, showing that this method of transaction settlement is robust and generates the expected price dynamics. The two behavioral mechanics added further increased the similarity between real and simulated return characteristics, indicating that the effects may also influence the actual price formation process. 1
DeFi Tokens: Stylized Facts
Francia, Nina Luz ; Krištoufek, Ladislav (advisor) ; Červinka, Michal (referee)
This thesis examines the price and return properties of the four major cryp- tocurrencies (Bitcoin, Ethereum, Binance, and Ripple), five DeFi coins (Uni- sawp, Chainlink, Maker, Pancakeswap, Aave), along with the two conventional financial assets (Euro/USD exchange rate, and S&P500 index). The daily data to January 2023 is used, with different starting dates for each asset depending on the data availability. The main focus of the examination is to examine whether the new class of financial assets show the statistical properties consistent with the stylized facts of the conventional financial assets. This exercise is important and have strong implications to many stakeholder and decision-makers in the finance in- dustry, in relation to whether these new assets show basic statistical properties consistent with those of the conventional financial assets. The properties exam- ined include return predictability (or information efficiency in the weak-form), departure from normality, volatility clustering, leverage effect, and return-risk relationship. Results show that the cryptocurrencies as well as DeFi coins exhibit the properties that are consistent with the stylized facts of the price and return financial assets, except that they show a substantially high degree of volatility and little degree of...
Short-term Electric Load Forecasting Using Czech Data
Řanda, Martin ; Krištoufek, Ladislav (advisor) ; Čech, František (referee)
Forecasting electric load accurately is a critical prerequisite to dependable power grid operation. It is thus in the best interests of the responsible institutions to develop and maintain performant models for predicting load. In this thesis, we analyze Czech electric load data and execute three pseudo-out-of-sample forecasting exercises. We employ standard econometric as well as machine learning methods and compare the results to benchmarks, including the predictions published by the Czech transmission system operator. The results of the first task examining the predictability of minute loads using 11 years of data indicate that the high-frequency load series is predictable. In the second and third exercises, we utilize hourly loads with additional explanatory variables. We generate one-step-ahead and 48-hours-ahead forecasts on the 2021 out- of-sample set and evaluate the performance of several methods. In both exercises, the most accurate results are produced by averaging forecasts of our specified recurrent neural network and the seasonal autoregressive integrated moving average model, achieving a mean absolute percentage error of less than 0.5% on the out-of-sample set in the one-step-ahead analysis and 2.3% in the 48-hours-ahead exercise, outperforming the operator's predictions.
The impact of oil-related events on volatility spillovers across oil-based commodities
Bartušek, Daniel ; Kočenda, Evžen (advisor) ; Krištoufek, Ladislav (referee)
Although oil-based commodities play a crucial role in the world from an indus- trial perspective, their prices are often heavily influenced by the occurrence of various events covered in the news. These events often trigger a sudden increase in volatility, that spills across all oil-based commodities. As a result, it becomes riskier to invest in this group of commodities. Furthermore, the increase in oil price volatility introduces friction in oil trade due to pricing uncertainty. In this thesis, we processed over 900 events related to oil from 1978 to 2022 and grouped them based on a set of repeating characteristics. Utilizing a novel bootstrap- after-bootstrap econometric framework developed by Greenwood-Nimmo et al. (2021), we identified over 20 historical events that triggered a sudden and per- sistent rise in volatility connectedness. We discover that geopolitical events are twice as likely to cause an increase in volatility spillovers than economic events. We did not find evidence for natural events influencing oil volatility spillover levels. Furthermore, a majority of the events after which the spillover levels increased share three common characteristics: they are negative, unexpected, and introduce fear of oil supply shortage. Investors and policymakers can use our findings to assess the...
Do Left-handers and Left-footers Have a Competitive Advantage in Sports?
Hadžić, Aner ; Krištoufek, Ladislav (advisor) ; Pavlovová, Anna (referee)
Left-sided athletes are often perceived as better performing as they can leverage their minority status within the sports world. While various specific left-sided athletes, such as Lionel Messi and Rafael Nadal, perform at the very top of their disciplines, these might be simply non-representative outliers. The current thesis puts the hy- pothesis of left-sided over-performance to test via a battery of tests and regressions. My thesis thoroughly analyses the prevalence and the performance of left-handed/left-footed athletes across 5 differ- ent sports. As majority of the current studies are focusing only on a few performance metrics in the given sport, my work broad- ens the knowledge on the topic since it compares the performance of left-sided and right-sided athletes in many categories in order to cover a great portion of the in-game action. Furthermore, this thesis also expands the current understanding of the (potential) left-sided advantage in direct encounters between both teams and individu- als, achieving so by implementing predictive Bradley-Terry models that are based on past matches. The overall results are rather sur- prising: in the majority of the performance comparisons between left-handers/left-footers and right-handers/right-footers, no signifi- cant difference between the two...
Cluster-based asset allocation strategies during market stress periods
Zacharová, Beáta ; Krištoufek, Ladislav (advisor) ; Čech, František (referee)
This thesis empirically examines the alternatives to traditional asset allocation strategies based on clustering mechanisms. Portfolio selection strategies utilizing hierarchical clustering are compared to the market benchmark and traditional methods: minimum-variance and equally weighted allocation, focusing on market stress periods. The allocation strategies are tested on daily stock prices of the S&P 100 index constituents from 2005 to 2021. The performance of Hierarchical Risk Parity (HRP) and Hierarchical Equal Risk Contribution (HERC) portfolios is evaluated across several market stress periods, including the financial crisis of 2007-2008 and the global coronavirus (COVID-19) pandemic in 2020. Empirical results do not prove the superiority of hierarchical clustering allocation strategies over traditional strategies in risk-adjusted performance. JEL Classification G01, G10, G11 Keywords portfolio selection, hierarchical clustering, HRP, HERC, market stress Title Cluster-based asset allocation strategies during market stress periods

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2 Krištoufek, L.
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