Modelling Conditional Quantiles of CEE Stock Market Returns
|Author:||Bc. Daniel Tóth|
|Year:||2015 - summer|
|Leaders:|| doc. PhDr. Jozef Baruník Ph.D.
|Work type:|| Finance, Financial Markets and Banking
|Awards and prizes:|
|Abstract:||Correctly specified models to forecast returns of indices are important for investors
to minimize risk on financial markets. This thesis focuses on conditional
Value at Risk modeling, employing flexible quantile regression framework and
hence avoiding the assumption on the return distribution. We apply semiparametric
linear quantile regression (LQR) models with realized variance and
also models with positive and negative semivariance which allows for direct
modelling of the quantiles. Four European stock price indices are taken into
account: Czech PX, Hungarian BUX, German DAX and London FTSE 100.
The objective is to investigate how the use of realized variance influence the
VaR accuracy and the correlation between the Central & Eastern and Western
European indices. The main contribution is application of the LQR models for
modelling of conditional quantiles and comparison of the correlation between
European indices with use of the realized measures. Our results show that
linear quantile regression models on one-step-ahead forecast provide better fit
and more accurate modelling than classical VaR model with assumption of normally
distributed returns. Therefore LQR models with realized variance can
be used as accurate tool for investors. Moreover we show that diversification
benefits are decreasing over time.