RT Generic
T1 Unbiased QML Estimation of Log-GARCH Models in the Presence of Zero Returns
A1 Sucarrat, Genaro
A1 Escribano, Álvaro
A2 Universidad Carlos III de Madrid. Departamento de Economía, 
AB A critique that has been directed towards the log-GARCH model is that its logvolatility specification does not exist in the presence of zero returns. A common "remedy" is to replace the zeros with a small (in the absolute sense) non-zero value. However, this renders Quasi Maximum Likelihood (QML) estimation asymptotically biased. Here, we propose a solution to the case where actual returns are equal to zero with probability zero, but zeros nevertheless are observed because of measurement error (due to missing values, discreteness approximisation error, etc.). The solution treats zeros as missing values and handles these by combining QML estimation via the ARMA representation with the Expectation-maximisation (EM) algorithm. Monte Carlo simulations confirm that the solution corrects the bias, and several empirical applications illustrate that the biascorrecting estimator can make a substantial difference.
SN 2340-5031
YR 2013
FD 2013-09-01
LK https://hdl.handle.net/10016/17909
UL https://hdl.handle.net/10016/17909
LA eng
DS e-Archivo
RD 1 sept. 2024