|
|
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
|
|
|
A Meta-Analysis of the Estimates of the Armington Elasticity
Bajzík, Josef ; Havránek, Tomáš (advisor) ; Polák, Petr (referee)
Josef Bajzík Abstract We examine determinants of Armington elasticities throughout history and nations employing 3,524 observations from 42 studies. We conduct meta-analysis using Bayesian model averaging approach to test the most influential factors. We explore more than 30 variables and compare our results with previous summarizing articles. In this thesis is, for instance, the first comparison of employment of different type of models in this area. Finally, we find out that the level of aggregation of the data used for estimation matters as well as the power of the currency. On the other hand, we discover that there is no significant distinction between long-run and short-run estimates. Moreover, we test for publication bias and we find evidence for it in this field.
|
|
|
Income Elasticity of Water Demand: A Meta-Analysis
Vlach, Tomáš ; Havránek, Tomáš (advisor) ; Červinka, Michal (referee)
If policymakers address water scarcity with the demand-oriented approach, the income elasticity of water demand is of pivotal importance. Its estimates, however, differ considerably. We collect 307 estimates of the income elasticity of water demand reported in 62 studies, codify 31 variables describing the estimation design, and employ Bayesian model averaging to address model uncertainty inherent to any meta-analysis. The studies were published between 1972 and 2015, which means that this meta-analysis covers a longer period of time than two previous meta-analyses on this topic combined. Our results suggest that income elasticity estimates for developed countries do not significantly differ from income elasticity estimates for developing countries and that different estimation techniques do not systematically produce different values of the income elasticity of water demand. We find evidence of publication selection bias in the literature on the income elasticity of water demand with the use of both graphical and regression analysis. We correct the estimates for publication selection bias and estimate the true effect beyond bias, which reaches approximately 0.2. 1
|
|
|
What Drives the Grades of Bachelor Theses?
Kurka, Josef ; Schwarz, Jiří (advisor) ; Korbel, Václav (referee)
This thesis examines factors influencing grades of bachelor theses. We are not aware of any literature dealing directly with factors affecting grades, hence we review literature investigating determinants of citations in the scientific articles, which is a topic very similar to ours. The literature shows numerous factors with potential to be determinants of grades. They vary from general characteristics to content, cited literature or academic degree of advisor and opponent. Most of those variables are correlated with inner abilities of each student, which could make our model suffer from endogeneity. A proxy in the form of average grade from mathematic courses is included to deal with this problem. To account for model uncertainty resulting from high number of explanatory variables, Bayesian Model Averaging is employed on our dataset comprised of 100 bachelor theses of IES students. Academic degree of advisor and opponent, as well as clarity of writing were found to influence grades significantly. In some specifications, mathematics grade and selection of literature turn out to be efficient predictors of grades as well.
|
|
|
Comparison of the inflation prediction approaches: Monetary growth vs. Output gap analysis
Kuliková, Veronika ; Horváth, Roman (advisor) ; Hlaváček, Michal (referee)
Inflation is one of the often used monetary indicators in conducting monetary policy. Even though money supply is an essential determinant of inflation, it is not used in inflation modeling. Currently, output gap is considered as most predicative variable. This thesis brings the empirical evidence on the hypothesis of money supply carrying more information on estimating inflation than the output gap. It is provided on the case of 16 developed European economies using Bayesian Model Averaging (BMA). BMA is a comprehensive approach that deals with the model uncertainty and thus solves the variable selection problem. The results of analysis confirmed that money supply includes more information of inflation than the output gap and thus should be used in inflation modeling. These outcomes are robust towards prior selection and high correlation of some variables. Powered by TCPDF (www.tcpdf.org)
|
|
|
Chinese Stock Markets: Underperformance and its Determinants
Kováč, Roman ; Báťa, Karel (advisor) ; Dědek, Oldřich (referee)
Performance of stock markets is determined by three classes of variables: macroeconomic indicators, industry & firm heterogeneity and third country effects. When assessing performance of a stock market index, impact of industry & firm heterogeneity is marginal as it is already embedded in the index through its constituent companies. This paper will therefore focus on the other two. Chinese stock market was selected as an application as their performance compared to other domestic indicators (mainly GDP growth) is considered inferior by many researchers. Using econometric framework for panel data and a Bayesian extension, the paper estimates multiple models of Chinese stock market performance examining individual determinants of it. Subsequently, it predicts development of theoretical prices of two main Chinese stock indices on two time samples until 2013. The paper then demonstrates underperformance of Chinese stock market by comparing the modeled prices to actual prices realized on the market. JEL Classification C23, C51, C53, G15, G17 Keywords underperformance, panel data, fixed effects model, Bayesian Model Averaging Author's e-mail roman_kovac@ymail.com Supervisor's e-mail karel.bata@seznam.cz
|
|
|
Comparison of the inflation prediction approaches: Monetary growth vs. Output gap analysis
Kuliková, Veronika ; Horváth, Roman (advisor) ; Babin, Adrian (referee)
Inflation is one of the often used monetary indicators in conducting monetary policy. Even though money supply is an essential determinant of inflation, it is not used in inflation modeling. Currently, output gap is considered as most predicative variable. This thesis brings the empirical evidence on the hypothesis of money supply carrying more information on estimating inflation than the output gap. It is provided on the case of 16 developed European economies using Bayesian Model Averaging (BMA). BMA is a comprehensive approach that deals with the model uncertainty and thus solves the variable selection problem. The results of analysis confirmed that money supply includes more information of inflation than the output gap and thus should be used in inflation modeling. These outcomes are robust towards prior selection and high correlation of some variables.
|
|
|
Systemic Risks Assessment and Systemic Events Prediction: Early Warning System Design for the Czech Republic
Žigraiová, Diana ; Jakubík, Petr (advisor) ; Doležel, Pavel (referee)
This thesis develops an early warning system framework for assessing systemic risks and for predicting systemic events, i.e. periods of extreme financial instability with potential real costs, over the short horizon of six quarters and the long horizon of twelve quarters on the panel of 14 countries both advanced and developing. Firstly, Financial Stress Index is built aggregating indicators from equity, foreign exchange, security and money markets in order to identify starting dates of systemic financial crises for each country in the panel. Secondly, the selection of early warning indicators for assessment and prediction of systemic risks is undertaken in a two- step approach; relevant prediction horizons for each indicator are found by means of a univariate logit model followed by the application of Bayesian model averaging method to identify the most useful indicators. Next, logit models containing useful indicators only are estimated on the panel while their in-sample and out-of-sample performance is assessed by a variety of measures. Finally, having applied the constructed EWS for both horizons to the Czech Republic it was found that even though models for both horizons perform very well in-sample, i.e. both predict 100% of crises, only the long model attains the maximum utility of 0,5 as...
|
|
|
Determinants of Economic Growth: A Bayesian Model Averaging
Kudashvili, Nikoloz ; Horváth, Roman (advisor) ; Havránek, Tomáš (referee)
MASTER THESIS Determinants of Economic Growth: A Bayesian Model Averaging Author: Bc. Nikoloz Kudashvili Abstract The paper estimates the economic growth determinants across 72 countries using a Bayesian Model Averaging. Unlike the other studies we include debt to GDP ratio as an explanatory variable among 29 growth determinants. For given values of the other variables debt to GDP ratio up to the threshold level is positively related with the growth rate. The coefficient on the ratio has nearly 0.8 posterior inclusion probability suggesting that debt to GDP ratio is an important long term growth determinant. We find that the initial level of GDP, life expectancy and equipment investments have a strong effect on the GDP per capita growth rate together with the debt to GDP ratio.
|
|
|
Spread Determinants and Model Uncertainty: A Bayesian Model Averaging Analysis
Seman, Vojtěch ; Rusnák, Marek (advisor) ; Hlaváček, Michal (referee)
The spread between interest rate and sovereign bond rate is commonly used in- dicator for country's probability to default. Existing literature proposes many different potential spread determinants but fails to agree on which of them are important. As a result, there is a considerable uncertainty about the cor- rect model explaining the spread. We address this uncertainty by employing Bayesian Model Averaging method (BMA). The BMA technique attempts to consider all the possible combinations of variables and averages them using a model fit measure as weights. For this empirical exercise, we consider 20 different explanatory variables for a panel of 47 countries for the 1980-2010 period. Most of the previously suggested determinants were attributed high inclusion probabilities. Only the "foreign exchange reserves growth" and the "exports growth" scored low by their inclusion probabilities. We also find a role of variables previously not included in the literature's spread determinants - "openness" and "unemployment" which rank high by the inclusion probability. These results are robust to a wide range of both parameter and model priors. JEL Classification C6, C8, C11, C51, E43 Keywords Sovereign Spread Determinants, Model Uncer- tainty, Bayesian Model Averaging Author's e-mail semanv()gmail()com...
|
guest ::
