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Yield curve dynamics: Co-movements of latent global and Czech yield curves
Šimáně, Jaromír ; Šopov, Boril (advisor) ; Novák, Jiří (referee)
This thesis focus on a yield curve modelling. It estimates unobserved "global" yield curve factors which drives changes in individual real yield curves. Yield curves of USD, GBP, JPY and EUR are considered and global factors are able to explain substantial part of their variances. The method is built on the Nelson-Siegel model which is implemented in a state-space form to be able to extract the unobserved yield factors. The estimated global yield factors are further used for explaining the evolution of the Czech yield curve. Their impact to the Czech yield curve is estimated in a time-varying regression which results show that the impact of the global factors is stronger during the years of the interventions of the Czech National Bank and thus suggests that the interventions help to transmit the global low interest rates to the Czech economy.
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Price Determinants of Art Photography at Auctions
Habalová, Veronika ; Šopov, Boril (advisor) ; Bauer, Michal (referee)
In the recent years, prices of art have repeatedly broken records, and the interest in investing in fine art photography has been growing. Although there is plenty of research dedicated to studying prices of paintings, fine art photography has been largely overlooked. This thesis aims to shed light on identifying price determinants for this particular medium. A new data set is collected from sold lot archives of Sotheby's and Phillips auction houses, which also provide images of some of the sold items. These images are then used to create new variables describing visual attributes of the artworks. In order to inspect the effect of color-related predictors on price, four different methods are discussed. Color is found to be significant in OLS model, but the effect diminishes when model averaging is applied. Machine learning al- gorithms - regression trees and random forests - suggest that the importance of color is relatively low. The thesis also shows that expert estimates can improved by incorporating available information and using random forests for prediction. The fact that the expert estimates are not very accurate sug- gest that they either do not use all the available information or they do not process it efficiently. 1
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Optimal portfolio selection under Expected Shortfall optimisation with Random Matrix Theory denoising
Šíla, Jan ; Šopov, Boril (advisor) ; Baruník, Jozef (referee)
This thesis challenges several concepts in finance. Firstly, it is the Markowitz's solution to the portfolio problem. It introduces a new method which de- noises the covariance matrix - the cornerstone of the portfolio management. Random Matrix Theory originates in particle physics and was recently intro- duced to finance as the intersection between economics and natural sciences has widened over the past couple of years. Often discussed Efficient Market Hypothesis is opposed by adopting the assumption, that financial returns are driven by Paretian distributions, in- stead of Gaussian ones, as conjured by Mandelbrot some 50 years ago. The portfolio selection is set in a framework, where Expected Shortfall replaces the standard deviation as the risk measure. Therefore, direct optimi- sation of the portfolio is implemented to be compared with the performance of the classical solution and its denoised counterpart. The results are evalu- ated in a controlled environment of Monte Carlo simulation as well as using empirical data from S&P 500 constituents. 1
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News Feed Classifications to Improve Volatility Predictions
Pogodina, Ksenia ; Šopov, Boril (advisor) ; Červinka, Michal (referee)
This thesis analyzes various text classification techniques in order to assess whether the knowledge of published news articles about selected companies can improve its' stock return volatility modelling and forecasting. We examine the content of the textual news releases and derive the news sentiment (po larity and strength) employing three different approaches: supervised machine learning Naive Bayes algorithm, lexicon-based as a representative of linguistic approach and hybrid Naive Bayes. In hybrid Naive Bayes we consider only the words contained in the specific lexicon rather than whole set of words from the article. For the lexicon-based approach we used independently two lexicons one with binary another with multiclass labels. The training set for the Naive Bayes was labeled by the author. When comparing the classifiers from the machine learning approach we can conclude that all of them performed similarly with a slight advantage of the hybrid Naive Bayes combined with multiclass lexicon. The resulting quantitative data in form of sentiment scores will be then incorpo rated into GARCH volatility modelling. The findings suggest that information contained in news feeds does bring an additional explanatory power to tradi tional GARCH model and is able to improve it's forecast. On the...
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The Effectiveness of the Federal Reserve's Monetary Policy under the Zero Lower Bound
Petrásek, Lukáš ; Horváth, Roman (advisor) ; Šopov, Boril (referee)
This thesis investigates the effectiveness of Federal Reserve's monetary policy under the zero lower bound. It estimates the impacts on interest rates due to surprising components of macroeconomic news. To obtain those surprise components, data on the actual and expected announced values of those news are used. The results support the findings in existing literature that the shorter- term interest rates were constrained by the zero lower bound, but the longer- term interest rates remained unconstrained. The conclusion is that to the extend that the Fed is able to affect those longer-term yields, its monetary policy effectiveness was essentially unaffected by the presence of the zero lower bound. JEL Classification E43, E52, E58 Keywords monetary policy, zero lower bound, interest rates, macroeconomic news Author's e-mail lukas.petrasek1.1@gmail.com Supervisor's e-mail roman.horvath@fsv.cuni.cz
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Risk factor modeling of Hedge Funds' strategies
Radosavčević, Aleksa ; Princ, Michael (advisor) ; Šopov, Boril (referee)
This thesis aims to identify main driving market risk factors of different strategies implemented by hedge funds by looking at correlation coefficients, implementing Principal Component Analysis and analyzing "loadings" for first three principal components, which explain the largest portion of the variation of hedge funds' returns. In the next step, a stepwise regression through iteration process includes and excludes market risk factors for each strategy, searching for the combination of risk factors which will offer a model with the best "fit", based on The Akaike Information Criterion - AIC and Bayesian Information Criterion - BIC. Lastly, to avoid counterfeit results and overcome model uncertainty issues a Bayesian Model Average - BMA approach was taken. Key words: Hedge Funds, hedge funds' strategies, market risk, principal component analysis, stepwise regression, Akaike Information Criterion, Bayesian Information Criterion, Bayesian Model Averaging Author's e-mail: aleksaradosavcevic@gmail.com Supervisor's e-mail: mp.princ@seznam.cz
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Presidential rhetoric, sentiment and their relation to stock markets
Partelová, Mária ; Šopov, Boril (advisor) ; Žigraiová, Diana (referee)
This thesis intends to uncover the linkages between the emotions contained within remarks of the president of the United States expressed on Twitter and movements of the stock market indices. The daily comments of the two consecutive presidents, Barack Obama and Donald Trump are annotated with sentiment intensity values using the lexicon-based model called VADER. Our analysis further focuses on testing for Granger causality using the bivariate vector autoregression. Overall, three major stock market indices are employed in testing, namely DJIA, S&P 500 and NASDAQ. The results yield a statistically significant Granger causal relationship in the case of the first differences of DJIA and S&P 500 logarithms with time series of Barack Obama's sentiment values.
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Impact of the Basel III Liquidity Rules on EU Banks
Klímová, Dana ; Šopov, Boril (advisor) ; Džmuráňová, Hana (referee)
New liquidity rules introduced under the Basel III framework define the Net Stable Funding Ratio (NSFR) that requires banks to possess an adequate long-term liquidity. The NSFR will enter into force on January 1, 2018 and banks are concerned that this regulation will lower their profitability. In this thesis the Basel III liquidity rules are analysed. The research seeks to define characteristics and triggers of the NSFR, using a sample of 500 EU banks. We find that smaller banks (by asset size) are more likely to fulfil the NSFR requirements, so are the banks with higher non-interest share of income and lower capital ratio, among other characteristics. Further, the NSFR's impact on the banks' performance is assessed. It is found that a higher NSFR negatively impacts the return on average equity, although it does not seem to translate into lower returns on average assets nor net interest margin. JEL Classification E58, G21, G28, G32 Keywords NSFR, Basel III, liquidity, banks, EU, profitability, capital rules, regulation Author's e-mail 45724231@fsv.cuni.cz Supervisor's e-mail boril.sopov@gmail.com
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Analysis of Price Determinants in the Art Market
Mizeráková, Elena ; Šopov, Boril (advisor) ; Moravcová, Hana (referee)
1 Abstract What qualities make the best-selling artworks worth so much? Does the in- terest of the general public influence the probability that the art will be sold in auction? The art market research focuses on various aspects that affect the potential of art as an investment. The boom of big data offers a unique op- portunity to utilize its global impact and improve the present models with a novel measure. Into the econometric analysis of auction results the thesis im- plements a change in the Internet searching volume provided by Google Trends as a reflection of the taste and the state of mind of society. The subject of the detailed discussion are not only the price determinants, but also the factors that affect the selling probability. The findings lead to a conclusion that the proposed measure based on Google Trends is significant for determining both, the odds of selling the artwork and its price. Beside that, an important effect on the price and the probability have auction houses, the personal brand of the artist or the medium of artwork. JEL Classification D44, C25, F23, Z10, Z11 Keywords art market, auctions, Google Trends, prices, price determinants, odds of selling Author's e-mail elena.mizerakova@gmail.com Supervisor's e-mail boril.sopov@gmail.com
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Effect of CNB's Monetary Intervention on Czech Exports to Germany
Krejčí, Tadeáš ; Šopov, Boril (advisor) ; Svačina, David (referee)
The primary interest of the thesis consists in impact of the Czech National Bank's monetary intervention on the volumes of Czech exports to Germany. To determine the significance of the potential casual effect, a Vector Autoregressive (VAR) model is constructed based on economic introspection and previous findings of other authors, to be subsequently estimated using the lucid method of least squares. Based on this procedure, we find evidence supporting the positive causal effect at hand. The results also confirm the presence of a structural break into periods before and after the initiation of controlled float regime. Furthermore, indications of casual effects are encountered in cases of other macroeconomic variables, such as German exports, supporting the reasoning in earlier works relating to this topic. JEL Classification E50, E51, E58, C51, C53 Keywords ForEx intervention, exports determination factors, foreign reserves, monetary policy Author's e-mail teddy.krejci@gmail.com Supervisor's e-mail boril.sopov@gmail.com
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