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 …

Neural network (NN) based prediction or detection systems often perform excellently with
easy problems without considering 1-5% difficult problems. This work proposes an …

We propose a new Conditional BEKK matrix-F (CBF) model for timevarying realized
covariance (RCOV) matrices. This CBF model is capable of capturing a heavy-tailed RCOV …

This paper proposes a sign-based portmanteau test for diagnostic checking of ARCH-type
models estimated by the least absolute deviation approach. Under the strict stationarity …

The daily returns from financial market variables, such as stock indices, exhibit empirical
distributions that are often heavy or semi-heavy or more Gaussian-like tailed. Estimating …

The exponentially weighting scheme is a simple and pragmatic approach to compute the
value at risk (VaR). However, the existing exponentially weighting methods lack a sound …

This paper establishes consistency and asymptotic normality of the generalized quasi-
maximum likelihood estimate (GQMLE) for a general class of periodic conditionally …

A covariate-driven random coefficient generalized conditional heteroscedasticity (GARCH)
time series model with the form of the buffered autoregression (BRC-GARCH-X) for …

Many financial and economic time series exhibit nonlinear patterns or relationships.
However, most statistical methods for time series analysis are developed for mean …

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