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Forecasting realized volatility: Do jumps in prices matter?
Lipták, Štefan ; Baruník, Jozef (advisor) ; Šopov, Boril (referee)
This thesis uses Heterogeneous Autoregressive models of Realized Volatility on five-minute data of three of the most liquid financial assets - S&P 500 Futures index, Euro FX and Light Crude NYMEX. The main contribution lies in the length of the datasets which span the time period of 25 years (13 years in case of Euro FX). Our aim is to show that decomposing realized variance into continuous and jump components improves the predicatability of RV also on extremely long high frequency datasets. The main goal is to investigate the dynamics of the HAR model parameters in time. Also, we examine whether volatilities of various assets behave differently. Results reveal that decomposing RV into its components indeed improves the modeling and forecasting of volatility on all datasets. However, we found that forecasts are best when based on short, 1-2 years, pre-forecast periods due to high dynamics of HAR model's parameters in time. This dynamics is revealed also in a year-by-year estimation on all datasets. Consequently, we consider HAR models to be inappropriate for modeling RV on such long datasets as they are not able to capture the dynamics of RV. This was indi- cated on all three datasets, thus, we conclude that volatility behaves similarly for different types of assets with similar liquidity. 1
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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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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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Financial Risk Measures: Review and Empirical Applications
Říha, Jan ; Šopov, Boril (advisor) ; Krištoufek, Ladislav (referee)
This thesis focuses on several classes of risk measures, related axioms and properties. We have introduced and compared monetary, coherent, convex and deviation classes of risk measures and subsequently their properties have been discussed and in selected cases demonstrated on data. Furthermore the relatively promising and advanced class of risk measures, the spectral risk measures, has been introduced. In addition to that we have outlined selected topics from portfolio theory that are relevant for applications of selected risk measures and then derived theoretical solution of portfolio selection using chosen risk measures. In the end we have highlighted the potential consequences of improper employment of certain risk measures in portfolio optimization.
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Portfolio diversification with cryptoassets
Chládek, Matúš ; Krištoufek, Ladislav (advisor) ; Šopov, Boril (referee)
This thesis investigates diversification benefits of Bitcoin and Ethereum. Technological innovation that made them possible, is interesting but for investors hard to grasp. The more important question is whether they should buy the digital currency or avoid it. We analyze Bitcoin and Ethereum from point of view of an investor within compatible with mean-variance (and non-mean-variance respectively) framework. Both cryptoassets are alternately added to base portfolio consisting of global indices representing American, European and Asian markets. Statistically rigorous tests suggest that Bitcoin yields ad- ded value to investors with utility function consistent with mean-variance setting. Same holds for for investors with preferences described by exponential and power utility func- tion. Ethereum shows similar results with exception of exponential utility. Performance benefits of both assets are preserved in the out-of-sample setting as size of test window reaches 28 weeks and increases. In the case of shorter test window, base assets show similar or slightly superior performance. Optimal allocation in out-of-sample framework is found by direct utility maximization with gradient based method. Keywords Bitcoin, Ethereum, digital currency, investment portfolio, diversification
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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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