This paper proposes a first-order zero-drift GARCH (ZD-GARCH (1, 1)) model to study
conditional heteroscedasticity and heteroscedasticity together. Unlike the classical GARCH …

The paper uses a random-weighting (RW) method to bootstrap the critical values for the
Ljung–Box or Monti portmanteau tests and weighted Ljung–Box or Monti portmanteau tests …

The paper investigates the maximum likelihood estimation (MLE) for a first-order double
autoregressive model with standardized non-Gaussian symmetric α-stable innovation …

This article proposes a novel Pearson-type quasi-maximum likelihood estimator (QMLE) of
GARCH (p, q) models. Unlike the existing Gaussian QMLE, Laplacian QMLE, generalized …

The portmanteau test is an effective tool for testing the goodness of fit of models. Motivated
by the fact that high-frequency data can improve the estimation accuracy of models, a …

The main research aims to detect the linear and nonlinear variability modeling in analyzing
the variability patterns of rainfall series. For rainfall linear and nonlinear variability modeling …

We study the bootstrap inference on the goodness-of-fit test for generalized autoregressive
conditional heteroskedastic (GARCH) models. Note that the commonly-used portmanteau …

Two of the fastest growing frontiers in econometrics and quantitative finance are time series
and financial econometrics. Significant theoretical contributions to financial econometrics …

This article considers the generalized bootstrap method to approximate the least absolute
deviation estimation and portmanteau test for generalized autoregressive conditional …

The estimation of time series models with heavy-tailed innovations has been widely
discussed, but corresponding goodness-of-fit tests have attracted less attention, primarily …