Sentence examples for variance process from inspiring English sources

This paper introduces a new long memory volatility process, denoted by adaptive FIGARCH, or A-FIGARCH, which is designed to account for both long memory and structural change in the conditional variance process.

Accordingly, Eq. (12) is a hybrid model consisting of two regime LSTAR process in the conditional mean with residuals following a nonlinear neural network model for the conditional variance with multi-layer perceptrons in each regime of the conditional variance process.

The measured standard deviations for the outlier points were considered to be valid sample measurements of the variance process and were not excluded from the calculation of the pooled standard deviations for spots with similar intensities.

The variance process of the mean ODE approach is not rigorously derived and can be specified by the modeler in an attempt to capture anything known about the residual process and measurement error.

The measured standard deviations for these outlier points are valid sample measurements of the variance process and should be used to calculate the pooled standard deviations for spots with similar intensities.

Each model possesses LSTAR type nonlinear structure in the conditional mean and conditional variance processes.

Lundberg and Terasvirta (1998) developed the STAR-ST-GARCH model that allows nonlinearity in both the conditional mean and the conditional variance processes of a time series.

First, similar to the MLP-based models given in this paper, the proposed LSTAR-LST-GARCH-RBF models utilize neural network architectures in the conditional variance processes.

The null hypothesis of no ARCH effects is rejected at a significance level of 1%, suggesting that the GARCH parameterization might well fit for the conditional variance processes.

According to the empirical findings, the oil prices and their daily returns could be classified as possessing nonlinearity in the conditional mean and conditional variance processes.

Hence, the LSTAR type, nonlinearity is introduced in the conditional mean and conditional variance processes of neural networks models to obtain the LSTAR-LST-GARCH-MLP and LSTAR-LST-GARCH-RBF models.