Work detail

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

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