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Interest rate pass-through : Does it change with financial distress? : the Czech experience
Kazaziová, Gledis ; Horváth, Roman (advisor) ; Krištoufek, Ladislav (referee)
The aim of this thesis is to investigate the behavior of the interest rate transmission from money market rates to bank retail rates on the Czech banking market during the period from January 2004 to January 2010, and to detect potential changes occurred as a result of current financial crisis. Using Ordinary Least Squares, Recursive Coefficients estimates and Impulse Response analysis we explore that bank retail rates reflect Pribor rate changes more strongly than changes in Euribor rates. We reveal that interest rate pass-through is rather incomplete and sluggish in the majority of cases and the adjustment level decreases noticeably during the period influenced by the financial crises.
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Efficiency, predictability and liquidity in the commodity futures markets
Čermák, Vojtěch ; Krištoufek, Ladislav (advisor) ; Čech, František (referee)
This thesis examines efficiency of several CME commodity futures and its relation to market liquidity over the ten years period. The goal is to find ARMA model that is better than white noise in terms of forecasting power and carry out analysis of market liquidity if we find such model. This is done by comparing selected ARMA models to white noise. In order to do that, we use Diebolt - Mariano test on forecast errors obtained by pseudo out - of - sample analysis using rolling window with re - estimation. Concern of furhter analysis are factors, that can influence the DM statistics. Main findings are, that we are able to find such ARMA model for small enough time period within the ten years period for almost all commodities. For most commodities, this sub period is not long enough to violate efficient market hypothesis. Only for palladium and lean hog futures this period is longer than one year. These two futures shows strong signs of inefficiency, as its predictability is not out - weighted by liquidity restrictions.
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Analysis of stock market anomalies: US cross-sectoral comparison
Jílek, Lukáš ; Krištoufek, Ladislav (advisor) ; Šopov, Boril (referee)
The purpose of this thesis is to analyze anomalies in the US stock market. Special attention is put on Day of the week effect, January effect, and Part of the month effect. We focus on comparison of companies with low and high capitalization. We perform an analysis across 6 major industrial sectors. Then, we discuss the findings with results of past projects and finally, we try to find a speculative investment strategy. We found out that neither Day of the week effect nor January effect do not appear in US stock market nowadays. Part of the month effect was the only anomaly, which was observed in our data. Keywords Stock market anomalies, financial markets, cross-sectoral analysis, Jannuary effect, Day of the week effect, Part of the month effect Author's e-mail jileklukas@gmail.com Supervisor's e-mail kristoufek@gmail.com
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How do the efficient portfolios at various investment horizons differ?
Růžek, Pavel ; Krištoufek, Ladislav (advisor) ; Křehlík, Tomáš (referee)
The Efficient Market Theory that assumes the homogeneity of investors' ex- pectations has several shortcomings and has failed to predict development of fi- nancial markets many times, recently. Previous research, therefore, has focused more intensively on incorporation of some aspects from Behavioural Finance to their models. This thesis implements another form of heterogeneity coming from different investment horizon preferences, and investigates the impacts on the selection of the efficient portfolios compared to the original Markowitz's framework. We employed the mean-variance model adjusted for the purpose of the work, and, additionally, suggested extensions that assure robustness of the model and the highest possible objectivity of the empirical results inde- pendently on the choice of data sets. The findings from our research strongly confirmed proposed hypotheses that the efficient portfolios do differ at the var- ious investment horizons and that the efficient portfolios for long investment horizons are less risky. JEL Classification G10, G11 Keywords portfolio selection, mean-variance, optimization, investment horizons, Dow Jones Index Author's e-mail pavel.ruzek.ies@gmail.com Supervisor's e-mail kristoufek@ies-prague.org
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Modeling Conditional Quantiles of Central European Stock Market Returns
Burdová, Diana ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
Most of the literature on Value at Risk concentrates on the unconditional nonparametric or parametric approach to VaR estimation and much less on the direct modeling of conditional quantiles. This thesis focuses on the direct conditional VaR modeling, using the flexible quantile regression and hence imposing no restrictions on the return distribution. We apply semiparamet- ric Conditional Autoregressive Value at Risk (CAViaR) models that allow time-variation of the conditional distribution of returns and also different time-variation for different quantiles on four stock price indices: Czech PX, Hungarian BUX, German DAX and U.S. S&P 500. The objective is to inves- tigate how the introduction of dynamics impacts VaR accuracy. The main contribution lies firstly in the primary application of this approach on Cen- tral European stock market and secondly in the fact that we investigate the impact on VaR accuracy during the pre-crisis period and also the period covering the global financial crisis. Our results show that CAViaR models perform very well in describing the evolution of the quantiles, both in abso- lute terms and relative to the benchmark parametric models. Not only do they provide generally a better fit, they are also able to produce accurate forecasts. CAViaR models may be therefore used as a...
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Non-Linear Classification as a Tool for Predicting Tennis Matches
Hostačný, Jakub ; Baniar, Matúš (advisor) ; Krištoufek, Ladislav (referee)
Charles University Faculty of Social Sciences Institute of Economic Studies MASTER'S THESIS Non-Linear Classification as a Tool for Predicting Tennis Matches Author: Be. Jakub Hostacny Supervisor: RNDr. Matus Baniar Academic Year: 2017/2018 Abstract In this thesis, we examine the prediction accuracy and the betting performance of four machine learning algorithms applied to men tennis matches - penalized logistic regression, random forest, boosted trees, and artificial neural networks. To do so, we employ 40 310 ATP matches played during 1/2001-10/2016 and 342 input features. As for the prediction accuracy, our models outperform current state-of-art models for both non-grand-slam (69%) and grand slam matches (79%). Concerning the overall accuracy rate, all model specifications beat backing a better-ranked player, while the majority also surpasses backing a bookmaker's favourite. As far as the betting performance is concerned, we develop six profitable betting strategies for betting on favourites applied to non-grand-slam with ROI ranging from 0.8% to 6.5%. Also, we identify ten profitable betting strategies for betting on favourites applied to grand slam matches with ROI fluctuating between 0.7% and 9.3%. We beat both bench mark rules - backing a better-ranked player as well as backing a bookmaker's...
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Testing the Effects of Parameter Changes in the Bornholdt's Model
Chrz, Štěpán ; Krištoufek, Ladislav (advisor) ; Seman, Vojtěch (referee)
In this work we thoroughly analyze Bornholdt's version of Ising model of ferromagnetism, with emphasis on its ability to mimic some basic stylized facts of financial series. Initially, we provide a breakdown of model definition and analysis of underlying dynamics. Subsequently, we examine and confirm model's ability to mimic stylized facts of financial series. To examine robustness of this ability to parameter change, we conduct simulations over a set of parameter combinations. We conclude that there is a wide set of combinations that yields acceptable simulation results. We also note that the seemingly best results are obtained at parameter values close to border of this set. Powered by TCPDF (www.tcpdf.org)
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Application of technical analysis on algorithmic trading
Šíla, Jan ; Krištoufek, Ladislav (advisor) ; Křehlík, Tomáš (referee)
The thesis takes on the question of profitability of algorithmic trading based on trend and momentum indicators and examines whether or not it is possible to acquire systematic profits. It reviews the development of relevant literature over the last 100 years to determine whether the inner workings of the market can be quantified and plausibly modelled. On three major U.S. stock indices are then tested several different strategies to determine whether in the long- term, passive investment can be outperformed by active trading. Merit of the work lies in backtesting several strategies and interpreting the results according to unique characteristics of the indices.
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Debt Contracts and Stochastic Default Barrier
Dózsa, Martin ; Janda, Karel (advisor) ; Krištoufek, Ladislav (referee)
This thesis focuses on the theory of asset pricing models and their usage in the design of credit contracts. We describe the evolution of structural models start- ing from the basic Mertonian framework through the introduction of a default barrier, and ending with stochastic interest rate environment. Further, with the use of game theory analysis, the parameters of an optimal capital struc- ture and safety covenants are examined. To the author's best knowledge, the first EBIT-based structural model is built up that considers stochastic default barrier. This set-up is able to catch the different optimal capital structures in various business cycle periods, as well as bankruptcy decisions dependent on the state of the economy. The effects of an exogenous change in the risk-free interest rate on the asset value, probability of default, and optimal debt ratio are also explained. JEL Classification C73, G12, G32, G33 Keywords credit contracts, stochastic default barrier, asset pricing, EBIT-based models, struc- tural models Author's e-mail martin@dozsa.cz Supervisor's e-mail Karel-Janda@seznam.cz Abstrakt Tato práce se zabývá teoretickými modely pro oceňování finančních aktiv a je- jich použitím při návrhu optimálních úvěrových smluv mezi dlužníky a věřiteli. V první části je popsán...
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Portfolio selection based on hierarchical structure of its components
Ševinský, Robert ; Krištoufek, Ladislav (advisor) ; Rusnák, Marek (referee)
This thesis investigate empirical performance of three portfolio selection and covariance matrix models. The goal is to find a strategy that outperform equally weighted portfolio in the long run and survives even in times of finan- cial distress. Two models based on Markowitz approach absolutely failed in this context, however the last approach based on network analysis indeed out- perform the market even after risk adjustment of returns. Moreover this model have sparse transaction matrix throughout time, therefore exhibit excellent properties even in the presence of transaction costs. Results for network based portfolio were obtained from running a back test on 160 member companies of S&P 500 index for 6'000 trading days. JEL Classification G11, G32, C10 Keywords Portfolio selection, Minimum spanning tree, Transaction costs, Covariance matrix Author's e-mail r.sevinsky@gmail.com Supervisor's e-mail kristoufek@ies-prague.org
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