| dc.description.abstract | The study aims to provide a comprehensive description of dependence pattern of a stock by
studying a range of betas derived as quantiles
of conditional return distribution using quantile
regression based on moving window regression.
We investigate predictability of various parts of
the conditional return distribution in a linear, autoregressive framework. We also aim to capture
a state of dependence at different quantiles of
the conditional return distribution. A good (bad)
state is associated with upper (lower) quantiles,
thus the impact of lagged returns is different across
quantiles. Our empirical findings are based on
daily returns of major European stocks-sample
data. Lower quantiles exhibit positive dependence
with past returns while upper quantiles are marked
by negative dependence. Central quantiles exhibit
weak dependence. Keeping the sign of returns, we
discover that positive previous day’s return leads
to strong positive returns with today’s positive
return and marked negative with today’s negative
return. The opposite pattern is visible for past
negative returns | en_US |
