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Oligopoly theory : mobile phone providers in the Czech Republic
Šopovová, Andrea ; Koubek, Ivo (advisor) ; Netuka, Martin (referee)
This paper studies an Oligopoly theory, which is applied on a mobile phone market in the Czech republic in order to measure and analyze the market performance throughout the years (1995-2008). We present several approaches of measures of market performance. The chosen econometric model then gives the results. A central question we ask is whether and how much the behaviour of operators changed with an entry of new provider. We get rather expected results. Moreover, we include a critical evaluation of our model and analysis.
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Can Bayesian econometric methods outperform traditional econometrics in inflation forecasting?
Stráský, Josef ; Baxa, Jaromír (advisor) ; Netuka, Martin (referee)
Forecasting of inflation has become crucial for both policy makers and private agents who try to understand and react to Central Bank decisions because many Central Banks implemented inflation targeting rules instead of control of monetary aggregates. Inflation forecasting is considered to be very complicated issue because univariate regression models and structural macroeconomic models are usually outperformed by naive random walk model. This work is intended for forecasting inflation in the Czech Republic by employing Bayesian econometric method (namely Bayesian vector autoregression - BVAR). Bayesian methods proved to be useful in inflation forecasting in developed countries (Fabio Canova: G-7 Inflation Forecasts: Random Walk, Phillips Curve or What Else?, 2007). Bayesian econometrics is one of the fast developing fields of econometrics for past two decades. In the centre of the approach is Bayesian probabilistic theory based on conditional probabilities. This probabilistic approach is, however, computationally demanding. Fast computer evolution enables wide applications of Bayesian models. Model estimations are based on combining information from some prior beliefs and from the data. Many different sorts of models have their Bayesian variants (e.g. OLS) but the emphasis in this work is on Bayesian...
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The Stock Market Volatility in the Czech Republic: Rises and Falls
Princ, Michael ; Netuka, Martin (advisor) ; Seidler, Jakub (referee)
A stock market came through a significant development in the Czech Republic; from its artificial beginning, through a fierce decline in listed companies, to a gradual rise in the market capitalization, which was suddenly turned off by a global financial crisis in 2008. The diploma thesis concentrate on a volatility analysis of a stock market in the Czech Republic in years 1994- 2009 including a comparison with a data available from world developed stock markets - namely European region, USA and Japan. The most important and influential events concerning world markets and also a development of Prague Stock Exchange are included in the analysis. Econometric tools includes GARCH model and its most popular derivatives and generalisations i.e. IGARCH, EGARCH and APARCH processes. The thesis is split into two main parts. The first part is devoted to a PSE volatility analysis based only on domestic data series involving GARCH class models estimations, a forecasting abilities comparison and also a structural-break analysis based on the ICSS algorithm including the Inclan-Tiao test and its successors. Next part involves a dynamic analysis based on the DCC MVGARCH model, which describes a change in a volatility spillover effect during the time. It is furthermore supported by the Granger causality...
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Estimate of the Capital Assets Pricing Models by means of Kalman filter
Pařenicová, Petra ; Netuka, Martin (advisor) ; Vošvrda, Miloslav (referee)
The Capital Assets Pricing Model (CAPM), which was published by W. F. Sharpe and J. Linter in the middle of the sixties, has since that time grown to one of the piers of foundation of the financial economics. During the time it used to be empirically tested for several times, but these tests in most of the cases contradicted its validity - especially (since as early as the seventies) rose the doubt about the time stability of the coefficient. Hence many economists have tried hard to find a new model, which could concisely express the progress of this coefficient. In have focused on three basic models in my thesis - they are the Model with Random Coefficients, the Random Walk and the Mean Reverting Model. I estimated these models for selected share issues from Prague Stock Exchange and New York Stock Exchange by Kalman filter and, finally, I tried to make a confrontation of all those models mentioned above. It is quite clear, that as for the sequel from empirical estimation, there always exists at the least one model with variable parameters, which better (in quite a concise way) describes to behaviour of the coefficient than the standard model CAPM with constant parameters.
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Volatilita akciového trhu v ČR:Vzestupy a pády
Princ, Michael ; Netuka, Martin (advisor) ; Seidler, Jakub (referee)
A stock market came through a significant development in the Czech Republic; from its artificial beginning, through a fierce decline in listed companies, to a gradual rise in the market capitalization, which was suddenly turned off by a global financial crisis in 2008. The diploma thesis concentrate on a volatility analysis of a stock market in the Czech Republic in years 1994- 2009 including a comparison with a data available from world developed stock markets - namely European region, USA and Japan. The most important and influential events concerning world markets and also a development of Prague Stock Exchange are included in the analysis. Econometric tools includes GARCH model and its most popular derivatives and generalisations i.e. IGARCH, EGARCH and APARCH processes. The thesis is split into two main parts. The first part is devoted to a PSE volatility analysis based only on domestic data series involving GARCH class models estimations, a forecasting abilities comparison and also a structural-break analysis based on the ICSS algorithm including the Inclan-Tiao test and its successors. Next part involves a dynamic analysis based on the DCC MVGARCH model, which describes a change in a volatility spillover effect during the time. It is furthermore supported by the Granger causality...
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The Determinants of Innovation: Empirical analysis based on European country-level data
Stacho, Miroslav ; Pertold-Gebicka, Barbara (advisor) ; Netuka, Martin (referee)
The thesis summarizes current state of art for the most recent research capabilities of innovation activities analysis. Its main goal is to assess the factors influencing pace and volume of technological innovativeness throughout the European industry and services sectors considering time span 2002-2008 using country-level Community Innovation Surveys and R&D data. It also attempts to evaluate trends in innovation policy instruments targeted to close the gap between Europe and world innovation leaders such as USA. Complex literature overview, basic empirical and extended instruments' analyses lead to recommendations of optimal governments' policy approaches towards different groups of countries divided by level of innovative performance.
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Can Bayesian econometric methods outperform traditional econometrics in inflation forecasting?
Stráský, Josef ; Baxa, Jaromír (advisor) ; Netuka, Martin (referee)
Forecasting of inflation has become crucial for both policy makers and private agents who try to understand and react to Central Bank decisions because many Central Banks implemented inflation targeting rules instead of control of monetary aggregates. Inflation forecasting is considered to be very complicated issue because univariate regression models and structural macroeconomic models are usually outperformed by naive random walk model. This work is intended for forecasting inflation in the Czech Republic by employing Bayesian econometric method (namely Bayesian vector autoregression - BVAR). Bayesian methods proved to be useful in inflation forecasting in developed countries (Fabio Canova: G-7 Inflation Forecasts: Random Walk, Phillips Curve or What Else?, 2007). Bayesian econometrics is one of the fast developing fields of econometrics for past two decades. In the centre of the approach is Bayesian probabilistic theory based on conditional probabilities. This probabilistic approach is, however, computationally demanding. Fast computer evolution enables wide applications of Bayesian models. Model estimations are based on combining information from some prior beliefs and from the data. Many different sorts of models have their Bayesian variants (e.g. OLS) but the emphasis in this work is on Bayesian...
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Can Bayesian econometric methods outperform traditional econometrics in inflation forecasting?
Stráský, Josef ; Netuka, Martin (referee) ; Baxa, Jaromír (advisor)
Forecasting of inflation rates has become crucial for both policy makers and private agents who try to understand and react to Central Bank decisions since many Central Banks implemented inflation targeting rules instead of control of monetary aggregates. Inflation forecasting is considered to be very complicated issue because univariate regression models and structural macroeconomic models are usually outperformed by naive random walk model. This work is intended for forecasting inflation in the Czech Republic by employing Bayesian econometric method (namely Bayesian Vector autoregression - BVAR). Bayesian methods proved to be useful in inflation forecasting in developed countries (Fabio Canova: G-7 Inflation Forecasts: Random Walk, Phillips Curve or What Else?, 2007). Bayesian econometrics is one of the most developing fields of econometrics for past two decades. In the centre of the approach is Bayesian probabilistic theory based on conditional probabilities. This probabilistic approach is, however, computationally demanding. Fast computer evolution enables wide applications of Bayesian models. Model estimations are based on combining information from some prior beliefs and from the data. Many different sorts of models have their Bayesian variants (e.g. OLS) but the emphasis in this work is on Bayesian...
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