Least absolute deviations (LAD) estimation of linear time series models is considered
under conditional heteroskedasticity and serial correlation. The limit theory of
the LAD estimator is obtained without assuming the finite density condition for the
errors that is required in standard LAD asymptotics. The results are particularly useful
in application of LAD estimation to financial time series data.