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Alternative field curve modelling approach : regional models
Šopov, Boril ; Seidler, Jakub (advisor) ; Baruník, Jozef (referee)
In this thesis, we focus on thorough yield curve modelling. We build on extended classical Nelson-Siegel model, which we further develop to accommodate unobserved regional common factors and principal components. We centre our discussion on central European currencies' yield curves: CZK, HUF, PLN and SKK. We propose two novel models to capture regional dynamics; one based purely on state space formulation and the other relying also on principal components of the regional yield curves. Moreover, we supplement the models with two application examples in risk management and structural break detection. The main contribution of this thesis is a creation of a complete framework that enables us to analyse yield curves, to design risk scenarios and to detect structural breaks of various types.
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Alternative yield curve modelling approach : regional models
Šopov, Boril ; Seidler, Jakub (advisor) ; Baruník, Jozef (referee)
In this thesis, we focus on thorough yield curve modelling. We build on extended classical Nelson-Siegel model, which we further develop to accommodate unobserved regional common factors and principal components. We centre our discussion on central European currencies' yield curves: CZK, HUF, PLN and SKK. We propose two novel models to capture regional dynamics; one based purely on state space formulation and the other relying also on principal components of the regional yield curves. Moreover, we supplement the models with two application examples in risk management and structural break detection. The main contribution of this thesis is a creation of a complete framework that enables us to analyse yield curves, to design risk scenarios and to detect structural breaks of various types.
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Predictive accuracy of competing Value-at Risk specifications during crisis : an application to CEE financial markets
Kroutil, Tomáš ; Baruník, Jozef (advisor) ; Seidler, Jakub (referee)
The recent worldwide Financial Crisis has increased the need for reliable financial risk measurement and management. In this thesis we evaluate and compare the accuracy of one-day-ahead out-of-sample forecasts of various Value-at-Risk models through a comprehensive assessment framework using crisis data of three CEE stock market indices (PX, WIG20 and BUX) and two benchmark stock indices (S&P 500, DAX). For building the VaR specifications we employ several GARCH extensions allowing either for asymmetry in volatility such as EGARCH, TGARCH and APARCH or long memory like FIGARCH and HYGARCH. Apart from conditional heteroscedasticity models, we also utilize realized volatility estimated by long memory ARFIMA and HAR. Individual volatility models are combined with full parametric approach, filtered historical simulation or filtered extreme value theory. This thesis shows that while VaR specifications based on logarithmic realized volatility, TGARCH and APARCH perform best overall, the benchmark - RiskMetrics model - is not significantly outperformed. The best performing model proves to be the TGARCH-t FHS, which is a combination of asymmetric and heavy-tailed GARCH filter with a historical simulation based approach.
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Value-at-risk based extreme value theory method and copulas : empirical evidence from Central Europe
Avdulaj, Krenar ; Baruník, Jozef (advisor) ; Seidler, Jakub (referee)
Assessing the extreme events is crucial in financial risk management. All risk managers and and financial institutions want to know the risk of their portfolio under rare events scenarios. We illustrate a multivariate Monte Carlo and semi-parametric method to estimate Value-at-Risk (VaR) for a portfolio of stock exchange indexes in Central Europe. It is a method that uses the non-parametric empirical distribution to capture the small risks and the parametric Extreme Value theory to capture large risks. We compare this method with historical simulation and variance-covariance method under low and high volatility samples of data. In general historical simulation method over estimates the VaR for extreme events, while variance-covariance underestimates it. The method that we illustrate gives a result in between because it considers historical performance of the stocks and also corrects for the heavy tails of the distribution. We conclude that the estimate method that we illustrate here is useful in estimating VaR for extreme events, especially for high volatility times.
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Mean-variance & mean-VaR portfolio selection : a simulation based comparison in Czech crisis environment
Parrák, Radovan ; Seidler, Jakub (advisor) ; Hájek, Filip (referee)
This thesis focuses on two methods for optimum portfolio selection. We compare Mean-Variance method with Mean-VaR method by the means of investment simulation, based on Czech financial market data from turbulent market periods of the year 2007 and the year 2008. We theoretically describe various approaches of optimum portfolio selection within a frame of Mean-Variance as well as Mean-VaR method. Furthermore, we highlight the similarities and differences of both strategies. Finally, we compare both strategies, basing on measurements of relative and absolute profitability of both strategies in crisis periods.
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Development of bank loans for private sector in Czech Republic
Doutnáčová, Jana ; Seidler, Jakub (advisor) ; Teplý, Petr (referee)
This work focuses on the development of the loan market in the czech economy. In the first portion of this work, the problems of bank loans at the infancy of the Czech Republic transformation are described. Subsequently depicted is the loan emission evolution in the private sector during the 90's, the 1997 crisis impact on the loan market and the following "credit crunch" are examined in more detail. Additionally, the influence of the privatization of large czech banks on the loan administration after the year 2000 is studied as well as the entrance of the Czech Republic into the European Union, which affected primarily banking regulation. The second portion of this work analyzes the connection between bank loan growth rate and economic measures (GNP, loan rates, and classified loans). This analysis is executed for the household and the business sectors of the Czech Republic separately.
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Credit Growth in Central and Eastern Europe
Němcová, Helena ; Seidler, Jakub (advisor) ; Hrbek, Pavel (referee)
This thesis focuses on the development of credit to the private sector in the Central and Eastern European (CEE) countries. Although the speed of credit growth in these countries has recently slowed down as the consequence of the global financial crisis, the overall increase in credit to the private sector over the past decades has been immense. As a result, the thesis examines whether this substantial increase in credit is linked to the convergence of the CEE countries towards the equilibrium or whether it represents an excessive credit growth that could threaten the macroeconomic and financial stability in these countries. We estimate the equilibrium credit levels for 11 transition countries by applying a dynamic panel data model. Since in-sample approach may bias the estimation results we perform the estimates out-of-sample using a panel of selected developed EU countries as a benchmark. The difference between the actual and estimated credit-to-GDP ratios serves as a measure of private credit excessiveness. The results indicate a slightly excessive or close to the equilibrium credit-to-GDP ratios in Bulgaria, Estonia, and Latvia prior to the financial crisis. With regard to the significant decline in GDP during the crisis this measure of credit excessiveness in these countries have further increased.
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Value-at-risk forecasting with the ARMA-GARCH family of models during the recent financial crisis
Jánský, Ivo ; Rippel, Milan (advisor) ; Seidler, Jakub (referee)
The thesis evaluates several hundred one-day-ahead VaR forecasting models in the time period between the years 2004 and 2009 on data from six world stock indices - DJI, GSPC, IXIC, FTSE, GDAXI and N225. The models model mean using the AR and MA processes with up to two lags and variance with one of GARCH, EGARCH or TARCH processes with up to two lags. The models are estimated on the data from the in-sample period and their forecasting ac- curacy is evaluated on the out-of-sample data, which are more volatile. The main aim of the thesis is to test whether a model estimated on data with lower volatility can be used in periods with higher volatility. The evaluation is based on the conditional coverage test and is performed on each stock index sepa- rately. Unlike other works in this eld of study, the thesis does not assume the log-returns to be normally distributed and does not explicitly select a partic- ular conditional volatility process. Moreover, the thesis takes advantage of a less known conditional coverage framework for the measurement of forecasting accuracy.
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What Drives the Aggregate Credit Risk: The Case of the Czech Republic
Málek, Jan ; Seidler, Jakub (advisor) ; Doležel, Pavel (referee)
There has been a long discussion about macroeconomic variables influencing the level of aggregate credit risk in the economy. While literature provides both empirical evidence and theoretical explana- tion of the influence of the business cycle on credit risk, the effect of other macroeconomic variables has not been explored sufficiently. In addition, recent literature suggests the existence of a latent risk factor behind aggregate credit risk, which is regularly interpreted as the latent default cycle. This thesis provides in its first part a discussion of potential aggregate credit risk drivers, which have been previously suggested in literature. We verify using a linear regression model whether the effect of these macroeconomic variables is also apparent in the Czech Republic. Results seem to be stable for both different model specifications and different clients segments and are in line with previous studies. The second part of this thesis explicitly models the latent factor that is assumed behind aggregate credit risk by adding an unobserved component to the already existing model constructed earlier in this thesis. The unobserved component can be estimated by applying Kalman filter. We subsequently discuss the sources of the latent component and whether it can be interpreted as the default cycle. The...
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