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Modeling Conditional Quantiles of Central European Stock Market Returns
Burdová, Diana ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
Most of the literature on Value at Risk concentrates on the unconditional nonparametric or parametric approach to VaR estimation and much less on the direct modeling of conditional quantiles. This thesis focuses on the direct conditional VaR modeling, using the flexible quantile regression and hence imposing no restrictions on the return distribution. We apply semiparamet- ric Conditional Autoregressive Value at Risk (CAViaR) models that allow time-variation of the conditional distribution of returns and also different time-variation for different quantiles on four stock price indices: Czech PX, Hungarian BUX, German DAX and U.S. S&P 500. The objective is to inves- tigate how the introduction of dynamics impacts VaR accuracy. The main contribution lies firstly in the primary application of this approach on Cen- tral European stock market and secondly in the fact that we investigate the impact on VaR accuracy during the pre-crisis period and also the period covering the global financial crisis. Our results show that CAViaR models perform very well in describing the evolution of the quantiles, both in abso- lute terms and relative to the benchmark parametric models. Not only do they provide generally a better fit, they are also able to produce accurate forecasts. CAViaR models may be therefore used as a...
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Modeling Conditional Quantiles of Central European Stock Market Returns
Burdová, Diana ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
Most of the literature on Value at Risk concentrates on the unconditional nonparametric or parametric approach to VaR estimation and much less on the direct modeling of conditional quantiles. This thesis focuses on the direct conditional VaR modeling, using the flexible quantile regression and hence imposing no restrictions on the return distribution. We apply semiparamet- ric Conditional Autoregressive Value at Risk (CAViaR) models that allow time-variation of the conditional distribution of returns and also different time-variation for different quantiles on four stock price indices: Czech PX, Hungarian BUX, German DAX and U.S. S&P 500. The objective is to inves- tigate how the introduction of dynamics impacts VaR accuracy. The main contribution lies firstly in the primary application of this approach on Cen- tral European stock market and secondly in the fact that we investigate the impact on VaR accuracy during the pre-crisis period and also the period covering the global financial crisis. Our results show that CAViaR models perform very well in describing the evolution of the quantiles, both in abso- lute terms and relative to the benchmark parametric models. Not only do they provide generally a better fit, they are also able to produce accurate forecasts. CAViaR models may be therefore used as a...
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Modeling Conditional Quantiles of Central European Stock Market Returns
Burdová, Diana ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
Most of the literature on Value at Risk concentrates on the unconditional nonparametric or parametric approach to VaR estimation and much less on the direct modeling of conditional quantiles. This thesis focuses on the direct conditional VaR modeling, using the flexible quantile regression and hence imposing no restrictions on the return distribution. We apply semiparamet- ric Conditional Autoregressive Value at Risk (CAViaR) models that allow time-variation of the conditional distribution of returns and also different time-variation for different quantiles on four stock price indices: Czech PX, Hungarian BUX, German DAX and U.S. S&P 500. The objective is to inves- tigate how the introduction of dynamics impacts VaR accuracy. The main contribution lies firstly in the primary application of this approach on Cen- tral European stock market and secondly in the fact that we investigate the impact on VaR accuracy during the pre-crisis period and also the period covering the global financial crisis. Our results show that CAViaR models perform very well in describing the evolution of the quantiles, both in abso- lute terms and relative to the benchmark parametric models. Not only do they provide generally a better fit, they are also able to produce accurate forecasts. CAViaR models may be therefore used as a...
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Měření finanční nákazy pomocí CAViaR metody: Aplikace na Evropu
Tomanová, Petra ; Zouhar, Jan (advisor) ; Formánek, Tomáš (referee)
The aim of this thesis is to measure changes in dependencies among returns on equity indices for European countries in tranquil periods against crisis periods and to investigate their asymmetries in the lower and upper tail of their distributions. The approach is based on a conditional probability that a random variable is lower than a given quantile while other random variables are also lower than their corresponding quantiles. Time-varying conditional quantiles are modeled by the Conditional Autoregressive Value at Risk via Regression Quantiles (CAViaR) method. In addition to the univariate conditional autoregressive models, the vector autoregressive extension is considered. In the second step, the conditional probability is estimated through the OLS regression. Moreover, the model which allows the distribution of returns in one country to lead or to lag the distribution of returns in another country, is defined and applied on European equity returns. Finally, the model measuring dependencies among more than two return series is derived and the relating dimensionality problems are discussed. The results document a significant increase in European equity return comovements in bear markets during the crisis in 1990s and 2000s. The explicit controlling for the high volatility days does not appear to have an impact on the main findings. For the comparison purposes, the results for Latin American countries are reported as well.
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