  This paper investigates how to measure common market risk factors using newly
proposed Panel Quantile Regression Model for Returns. By exploring the fact
that volatility crosses all quantiles of the return distribution and using
penalized fixed effects estimator we are able to control for otherwise
unobserved heterogeneity among financial assets. Direct benefits of the
proposed approach are revealed in the portfolio Value-at-Risk forecasting
application, where our modeling strategy performs significantly better than
several benchmark models according to both statistical and economic comparison.
In particular Panel Quantile Regression Model for Returns consistently
outperforms all the competitors in the 5\% and 10\% quantiles. Sound
statistical performance translates directly into economic gains which is
demonstrated in the Global Minimum Value-at-Risk Portfolio and Markowitz-like
comparison. Overall results of our research are important for correct
identification of the sources of systemic risk, and are particularly attractive
for high dimensional applications.
