R-estimators in GARCH models: asymptotics and applications
提出一类基于残差秩次的R估计量用于GARCH模型参数估计，在误差仅需有限2+δ矩条件下具有渐近正态性和高效率，且对异常值稳健，模拟和实证表现优于传统QMLE。
Summary The quasi-maximum likelihood estimation is a commonly-used method for estimating the generalized autoregressive conditional heteroscedastic parameters. However, such estimators are sensitive to outliers and their asymptotic normality is proved under the finite fourth moment assumption on the underlying error distribution. In this paper, we propose a novel class of estimators of the generalized autoregressive conditional heteroscedastic parameters based on ranks of the residuals, called R-estimators, with the property that they are asymptotically normal under the existence of a finite $2+\delta$ moment of the errors and are highly efficient. We propose a fast algorithm for computing the R-estimators. Both real data analysis and simulations show the superior performance of the proposed estimators under the heavy-tailed and asymmetric distributions.