Modeling Conditional Quantiles of Central European Stock Market Returns
| Author: | Mgr. Diana Burdová |
|---|---|
| Year: | 2014 - winter |
| Leaders: | doc. PhDr. Jozef Baruník Ph.D. |
| Consultants: | |
| Work type: | Economic Theory Masters |
| Language: | English |
| Pages: | 87 |
| Awards and prizes: | M.A. with distinction from the Dean of the Faculty of Social Sciences for an excellent state-final examination performance and for an extraordinarily good masters diploma thesis. |
| Link: | https://is.cuni.cz/webapps/zzp/detail/125355/ |
| Abstract: | Abstract 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 exible 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 di erent time-variation for di erent 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 rstly 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 nancial 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 t, they are also able to produce accurate forecasts. CAViaR models may be therefore used as a suitable tool for VaR estimation in practical risk managemen |
