Physical Sciences and Mathematics Commons^™

Estimation, Testing, And Monitoring Of Generalized Autoregressive Conditionally Heteroskedastic Time Series, Aonan Zhang

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

We study in this dissertation Generalized Autoregressive Conditionally Heteroskedastic (GARCH) time series. The research focuses on squared GARCH sequences. Our main results are as follows:

1. We compare three methods of constructing confidence intervals for sample autocorrelations of squared returns modeled by models from the GARCH family. We compare the residual bootstrap, block bootstrap and subsampling methods. The residual bootstrap based on the standard GARCH(l,1) model is seen to perform best. Confidence intervals for cross-correlations of a bivariate GARCH model are also studied.

2. We study a test to discriminate between long memory and volatility changes in financial returns data. …