A0180
Title: Statistical inference for power autoregressive conditional duration models with stable innovations
Authors: Yuxin Tao - Southern University of Science and Technology (China) [presenting]
Dong Li - Tsinghua University (China)
Abstract: A first-order power autoregressive conditional duration model is proposed with positive alpha-stable innovations (sPACD), and the properties of maximum likelihood estimation (MLE) are studied within a unified framework of stationary and explosive cases. The proposed model effectively addresses the excess kurtosis in the durations. Further, the power form of the model structure mitigates the issue of overpredicting short durations in the standard ACD model. The estimation for the asymptotic covariance matrix is discussed. The test statistics are established for stationarity testing and diagnostic checking in both stationary and explosive scenarios. Monte Carlo simulation studies illustrate the good performance of the MLE in finite samples. An empirical example is analyzed to illustrate the usefulness of sPACD models.
