LAD Asymptotics under Conditional Heteroskedasticity with Possibly Infinite Error Densities
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.
Cho, Jin Seo; Han, Chirok; and Phillips, Peter C.B., "LAD Asymptotics under Conditional Heteroskedasticity with Possibly Infinite Error Densities" (2009). Cowles Foundation Discussion Papers. 2021.