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Since the 1970s, economists have started studying the concept of inflation hedging as a way to protect investments. With the recent high inflation rates, investors might be interested if newly created assets such as cryptocurrencies can be effective against inflation. This thesis paper aims to find out whether thelargest crypto asset Bitcoin can be used as an inflation hedge. To answer this question, Fisher coefficient estimation and hedging demand for the US, Euro Area, and the Czech Republic for the period between November 2014 and October 2022 will be analyzed. In addition, the vector autoregressive model (VAR)will be used for the US market in the same time frame. The results showed overall positive Bitcoin returns but all three methods indicated no or negative correlation between inflation rates in three regions and Bitcoin returns. The thesis paper concludes that Bitcoin cannot be used as an inflation hedge as notall requirements are met. Keywords Cryptocurrency, Bitcoin, gold, inflation, inflation hedge, Fisher coefficient,VAR model Title Can Bitcoin serve as an inflation hedge asset in the US, Euro Area, andCzech markets?

Bitcoin is often compared to gold for its gold-like features such as a store of value, a limited supply, and a safe haven. However, due to Bitcoin's extreme price movements, investors might rather look for a safe haven against Bitcoin. In this thesis, we study such properties among traditional assets. Specifically, we analyze gold, oil, and stocks as safe havens for Bitcoin on a sample period from 2014 until March 2022. We find that gold acts as a strong safe haven suggesting gold's traditional role as a shelter during uncertainty holds also for this crypto asset. 1

The goal of the thesis is to identify factors that drive the price of Bitcoin and the hashrate of the Bitcoin network, which represents the total computing power dedicated to Bitcoin mining, and to explore the relationship between these two variables. In the Bitcoin system, four variables were assumed to be endogenous, thus for each of them, an equation was constructed. This was the case of the Bitcoin price, the hashrate of the Bitcoin network, the total transaction fees paid, and the search volume for the term "bitcoin". The system of four equations was then simultaneously estimated, utilizing the method of Two-stage least squares. Results revealed several statistically significant explanatory variables of the price and the hashrate, including the money supply of the United States dollar or the number of unique active addresses on the Bitcoin network. The hashrate was shown to drive the price positively, however, the estimated effect of the price on the hashrate was statistically insignificant. It was argued that it might have been caused by exogenous shocks affecting multiple variables, that could not be accounted for in the data. In addition, the factors affecting the hashrate were assessed from the environmental point of view, as the high environmental impact is one of the main points in the criticism...

The aim of this thesis is to investigate the Bitcoin mining profitability throughout the years 2014 to 2020 with the focus on the year 2020. The analysis is based on the break-even electricity price estimates which are obtained by using a set of variables entering the Bitcoin mining process such as the block reward, transaction fees, network hash rate or power consumption. The calculations are performed under the assumption that miner owns the most efficient mining hardware available at the time while disregarding the original investments in the necessary hardware. To further examine the relationship between the estimated break-even electricity price and the Bitcoin market price the cointegration analysis is performed employing a vector error correction model as the series seem to be nonstationary. The final results illustrate the substantial effect the Bitcoin market price has on the break-even electricity price estimates to the extent that there is rather long-term reaction in the break-even electricity price values to the shocks in the Bitcoin market price. The findings from the research offer insights to the Bitcoin mining process suggesting that an access to extremely low electricity prices is needed to earn any profits from the Bitcoin mining activity in 2020.

The topic of our thesis is the examination of the relationships among cryptocur- rencies using Google Trends. In our thesis, we concentrated on four cryptocur- rencies, namely: Bitcoin, Litecoin, Ethereum Classic and Ethereum. We obtained the data of daily opening prices, daily trading volumes and daily Google Trends queries in order to examine the relationships among the four cryptocurrencies. Applying the Vector autoregression model and Vector error correction model, we constructed four models. The first model contains only four time series of daily prices of cryptocurrencies. The second model is the first model enriched by the respective four time series of Google Trends queries. The third model contains the four time series of daily trading volumes of the four cryptocurrencies. The fourth model is the third model enriched by the four time series of Google Trends queries of respective cryptocurrencies. Then we applied the Impulse response analysis and the Forecast error variance decomposition in order to find some relationships among the variables. We found that there is some correlation among prices, volumes and Google Trends queries containing the names of the four cryptocurrencies. According to our results acquired by the Forecast error variance decomposition, in all our models, Bitcoin has the...

Cryptocurrencies are well known asset that almost everyone at least heard of. Some of these cryptocurrencies have all of its supply already in circulation, while others do not. Two biggest cryptocurrencies, Ethereum and Bitcoin, have their supply being increased every day through the process known as mining, which many never even heard of. This process and its interaction with price are the main topic of this thesis. Price and hash rate, which is good and flexible index of difficulty of mining, of Bitcoin and Ethereum are studied and it is shown, that there is an interplay among both currencies but also withing the currency itself.

The thesis attempts to determine how strategies used for forecasting and trad- ing on foreign exchange and stock markets perform when applied to cryptocur- rency markets. The approaches explored are ARIMA, VAR, MA Crossover, and Granger Causality using gold prices and S&P 500. The currencies traded are Bitcoin, Ethereum, Binance Coin, and Basic Attention Token. The models are trained on logarithmically transformed and differenced time series composed of the currencies' daily and hourly closing prices. Applying these strategies mostly leads to ambiguous results, with MA Crossover generally performing better than VAR, which in turn performs better than ARIMA. However, every strategy was moderately successful for at least one of the currencies examined. Trading on the hourly dataset was negatively influenced by sudden price jumps. ARIMA and VAR perform better in the inter-bubble periods. No significant Granger causality was found. Keywords Cryptocurrency, Trading, Bitcoin, Ethereum, Binance Coin, Basic Attention Token, ARIMA, VAR, MA Crossover, Granger Causality Title The Profitability of Standard Trading Strategies in Cryptocurrency Markets Author's e-mail miroslav.duda11@gmail.com Supervisor's e-mail ladislav.kristoufek@fsv.cuni.cz

Cryptocurrencies are newly emerging asset class that has received a lot of at- tention recently. Many investors are considering investing in them as a way of portfolio diversification. This thesis examines whether cryptocurrencies are gambling assets which could be important for investors' decision making and also for better understanding of the cryptocurrencies themselves. Gambling asset is understood in terms of stocks and therefore a comparison of crypto- currency and stock lottery characteristics is made. It is shown that in most time periods it cannot be said that cryptocurrencies exhibit larger lottery characteristics. Furthermore, it is shown that Litecoin, Ethereum, Ripple, Dash and Monero would classify as gambling asset, however, Bitcoin would not. JEL classification C12, C38, C55, G11, O33 Keywords cryptocurrencies, Bitcoin, gambling, gambling asset, investment Author's e-mail novotnyf1@gmail.com Supervisor's e-mail ladislav.kristoufek@fsv.cuni.cz 1

This paper examines statistical properties of crypto-currencies' price variations in comparison with statistical properties of price variations in common financial markets. Price data of Bitcoin, ripple and Litecoin have been directly compared with price data of euro currency and stock index S&P500. Additionally, and compared with set of stylized facts of asset returns. The properties in scope of this work include an autocorrelation of day-to-day returns, a shape of return distributions, a volatility clustering, a leverage effect and a volume/volatility correlation. To answer the question of this thesis, we have tried to find unique differences in the way prices of crypto-currencies behave. After every point of the data analysis has been checked, we have concluded that the only major difference is in the shape and the significance of autocorrelation in day-to-day returns. While crypto-currencies seem to autocorrelate, there has been no such a cross-autocorrelation found in the benchmark values. Therefore, we argue that it is the most distinctive sign of crypto-currencies and the reason for crypto-currencies to be regarded as separate asset class. Powered by TCPDF (www.tcpdf.org)

The purpose of this thesis is to explain how Bitcoin works, analyze the Bitcoin total variation and to separate the jump component of realized variance from the continuous part. In order to do so, we use estimates of quadratic variation and integrated variance. We detect jumps using a test which is based on the difference between realized variance and bipower variation. The results for BTC/USD exchange rate are then compared with the results for EUR/USD exchange rate, price of gold and for the S&P 500 index. In case of all datasets, we use data with five-minute frequency. It seems that no other work analyzing the Bitcoin total variation using the same methods to separate the jump component from the continuous part of a price process has been written so far. We found that jumps in the Bitcoin total variation are stronger than for other analyzed instruments. The results also suggest that the duration between jumps for Bitcoin considerably prolonged during the monitored period which may indicate that the behavior of price of bitcoin has stabilized over time. We also found out that the variance of price of bitcoin is higher during the monitored period in comparison with other analyzed instruments. Powered by TCPDF (www.tcpdf.org)