National Repository of Grey Literature 126 records found  1 - 10nextend  jump to record: Search took 0.01 seconds. 
Fractality of Stock Markets: A Comparative Study
Krištoufek, Ladislav ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
The main focus of the thesis is the introduction of new method for interpretation of fractality aspects of financial time series together with its application. We begin with description of various techniques of estimation of Hurst exponent - rescaled range, modified rescaled range and detrended fluctuation analysis. Further on, we present original theoretical results based on simulations of three mentioned procedures which have not been presented in literature yet. The results are then used in the new method of time-dependent Hurst exponent with confidence intervals developed in this thesis. Moreover, we show important advantage of using the mentioned techniques together to clearly distinguish between independent, trending, short-term dependent and long-term dependent properties of the time series. We eventually apply the proposed procedure on 13 different world stock indices and come to interesting results. To the author's best knowledge, the thesis presents the broadest application of timedependent Hurst exponent on stock indices yet.
Application of machine learning methods for estimating apartment prices in the Czech Republic
Nikodym, Jakub ; Krištoufek, Ladislav (advisor) ; Baruník, Jozef (referee)
In this thesis, we propose alternative ways to apartments' mass appraisal. This work enriches the current literature by combining several techniques of data extraction and price estimation. We are not aware of any similar work providing an in-depth overview of the Czech apartment market. Throughout the empirical analysis, five different methods (OLS, LASSO, decision tree, random forests, and kNN) are applied to the dataset of 15,848 classifieds. The aim of the study is to find the most accurate method of esti- mating offering prices, using structured variables as well as data extracted by text mining. We use various accuracy statistics and graphical analysis to vali- date our results. Tree-based methods, specifically the random forest algorithm, results with the highest accuracy in predicting offering prices. Additionally, text-based variables included in the model cause the reduction of errors on linear models. The last part of the analysis covers the main determinants of property value in Prague and the rest of the Czech Republic. We show that prices in Prague can be estimated with higher preciseness and with the lower number of independent variables.
Examining the Link between Financial Market Efficiency and Monetary Transmission Mechanism
Krejčí, Tadeáš ; Krištoufek, Ladislav (advisor) ; Vácha, Lukáš (referee)
In an effort to examine role of capital markets' efficiency in transmission of monetary policy, 28 time series of market efficiency development are estimated with use of long-term memory and fractal dimension measures and a panel of 27 inflation targeting countries is constructed to run a random effect regres- sion. The cases of Czech Republic and Austria are thereafter more closely examined with use a vector-autoregressive and threshold vector-autoregressive frameworks on macroeconomic data spanning from 1996:Q3 to 2018:Q4. The evidence obtained through the conducted analyses support the hypothesis, that a more efficiently functioning capital market better contributes to monetary policy pass-through, or conversely, that high transaction costs, barriers to cap- ital market entry, or poor information availability may hinder the effects of central bank's monetary policy. JEL Classification F12, F21, F23, H25, H71, H87 Keywords capital market efficiency, inflation targeting, monetary transmission mechanism Author's e-mail Supervisor's e-mail
Pairs Trading in Cryptocurrency Markets
Fil, Miroslav ; Krištoufek, Ladislav (advisor) ; Hronec, Martin (referee)
Pairs trading is a trading strategy which tries to exploit mean-reversion among prices of certain securities. It is market-neutral and self-financing, and has been shown to produce high excess returns in historical backtests. We employ the most common distance and cointegration approaches on cryp- tocurrency data from an exchange called Binance spanning the year 2018. The strategy is mostly unprofitable under transaction costs, but certain combinations of hyperparameters can perform well. Overall, the distance method performs far better, being able to achieve 3% monthly profit even in our baseline real-life con- ditions while the cointegration method always achieves only a slight loss. We also found that increasing the sampling frequency of the data from daily to hourly brings mixed results. Moreover, since we have to reuse estimates of real-life considerations from equity markets, it is unclear if our results are truly representative of the cryp- tocurrency market. The strategy is found to be very sensitive to execution diffi- culties and transaction costs, making their determination crucially important. It is somewhat easy to get returns in excess of 5% monthly under ideal conditions, but whether this could be achieved in real trading conditions is still unclear. Keywords pairs trading,...
Private Equity funds and their performance in the post-crisis period
Koníř, Štěpán ; Krištoufek, Ladislav (advisor) ; Kučera, Adam (referee)
The work covers the topic of private equity funds performance and attempt to identify the impact of macroeconomic conditions on the entire industry. The recent central banks' actions put a question about the impact of changes in interest rates on the private equity funds performance. With the sample of 100 observations provided by Cambridge Associates, we identified the significant negative effect of prevailing low interest rates on the growth of private equity funds performance. We further attempt to answer the question, whether private equity funds operating in post-crisis years has on average higher growth rate, however, we could not provide the answer as we failed to reject the null, neutral effect hypothesis. Additionally, with a sample of 3092 observations provided by Bloomberg, we found that the effect of cheap debt has increased on average in the postcrisis period, predicting that the private equity performance can suffer once the interest rates rises enough.
Forecasting oil prices volatility with Google searches
Tolstoguzova, Ekaterina ; Krištoufek, Ladislav (advisor) ; Zafeiris, Dimitrios (referee)
Oil market pricing is highly susceptible to geopolitical and economic events. With the rapid development of information technology, energy market can quickly get external information shocks through the Internet. This thesis examines the relationship between prices of three oil benchmarks, CBOE Crude Oil Volatility Index, and Google search queries. We built VAR model to study Granger causality and to provide impulse response analysis. Results indicate both one side and two-side causal relationship between oil-related series and most of the search queries. Out-of sample forecasting with measures of predictive accuracy and Diebold-Mariano test demonstrated that Google trends can improve short-run prediction potential only for models with WTI price and volatility index.
The impact of macroeconomic news announcements on the value and volatility of selected foreign exchange rates in EU
Bubniak, Peter ; Fanta, Nicolas (advisor) ; Krištoufek, Ladislav (referee)
Bibliographic note BUBNIAK, Peter. The impact of macroeconomic news announcements on the value and volatility of selected foreign exchange rates in EU. Prague 2019. 47 pp. Bachelor thesis (Bc) Charles University, Faculty of Social Sciences, Institute of Economic Studies. Thesis supervisor: Mgr Nicolas Fanta. Abstract This work analyzes the influence of positive and negative macroeconomic news on the value of exchange rate and volatility. We have chosen EUR/USD, EUR/CZK and USD/CZK as our exchange rates. The influence of macroeconomic news published by Czech national bank and European central bank were analysed. For our purposes were used econometric models GARCH(1,1) and EGARCH(1,1) with both Normal and Student's distribution of error terms. One of the major outcomes were the importance of macroeconomic news on value and volatility on the exhcange rates. For each exchange rate has effect different macroeconomic index. The crucial are: Consumer price index and Harmonised Index of Consumer Pirces, unemplyoment rate and PRIBOR and EURIBOR. Another conclusion was that our financial dataset displays the main nature of volatility. JEL Classification C22, E00, E52, E58, F3, F4, F31, G1, G13, G14 Key words financial market, exchange rate, ARCH model, GARCH model, volatility Authors e-mail
Capital market efficiency in the Ising model environment: Local and global effects
Krištoufek, Ladislav ; Vošvrda, Miloslav
Financial Ising model is one of the simplest agent-based models (building on a parallel between capital markets and the Ising model of ferromag- netism) mimicking the most important stylized facts of financial returns such as no serial correlation, fat tails, volatility clustering and volatility persistence on the verge of non-stationarity. We present results of Monte Carlo simulation study investigating the relationship between parameters of the model (related to herding and minority game behaviors) and crucial characteristics of capital market e ciency (with respect to the e cient market hypothesis). We find a strongly non-linear relationship between these which opens possibilities for further research. Specifically, the existence of both herding and minority game behavior of market participants are necessary for attaining the e cient market in the sense of the e cient market hypothesis.
The Profitability of Standard Trading Strategies in Cryptocurrency Markets
Duda, Miroslav ; Krištoufek, Ladislav (advisor) ; Brož, Václav (referee)
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 Supervisor's e-mail
Seasonality in Cryptocurrency Markets
Mošovský, Jan ; Krištoufek, Ladislav (advisor) ; Šíla, Jan (referee)
Ten years have passed since the emergence of Bitcoin and with it cryptocur- rencies as a new class of assets. Now, cryptocurrencies are not uncommon tool of investment and subject of academic research. This thesis focuses on investigating possible presence of weekly and monthly seasonal patterns in cryptocurrencies, namely Bitcoin, Litecoin, Ripple, Monero, Dash, Stellar and partly Ethereum, which are selected as representative sample. Insuffi- cient evidence is found for the day-of-the-week effect, the January effect is however revealed as significant by different methods in the whole sample, with cryptocurrencies generally exhibiting higher returns towards the end of the year and lowest from January to March. Examining probable causes of revealed seasonality, it is found that these are not likely to be caused by peculiar price development in 2017 and 2018, as well as the Chinese New Year or brought to the market by proposed price drivers of Bitcoin. How- ever, significant evidence for correlation of patterns followed by Bitcoin and other examined cryptocurrencies is found.

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