After declaring COVID-19 pneumonia as a pandemic, researchers promptly advanced to seek solutions for patients fighting this fatal disease. Computed tomography (CT) scans offer valuable insight into how infection affects the lungs. Analysis of CT is very significant, especially when physicians are striving quick solutions. This study successfully segmented lung due and provided physician with q...

A standard model of (conditional) heteroscedasticity, i.e., the phenomenon that the variance of a process changes over time, is the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is especially important for economics and finance. GARCH models are typically estimated by the Quasi-Maximum Likelihood (QML) method, which works under mild statistical assumptions. Here...

It is shown empirically that mixed autoregressive moving average regression models with generalized autoregressive conditional heteroskedasticity (Reg-ARMA-GARCH models) can have multimodality in the likelihood that is caused by a dummy variable in the conditional mean. Maximum likelihood estimates at the local and global modes are investigated and turn out to be qualitatively different, leadin...

This paper develops a new econometric framework for investigating how the sensitivity of the financial market volatility to shocks varies with the volatility level. For this purpose, the paper first introduces the square-root (SQ) GARCH model for financial time series. It is an ARCH analogue of the continuous-time square-root stochastic volatility model popularly used in derivatives pricing and...

The paper investigates the relationship between inflation and inflation uncertainty using the Iranian data over the period 1959:03 – 2008:02. GARCH models are used to examine this relationship. Granger methods are employed to provide statistical evidence for the relationship between average inflation and inflation uncertainty. Threshold GARCH (TGARCH) models are considered to investigate asymme...

We investigate a model in which we connect slowly time varying unconditional long-run volatility with short-run conditional volatility whose representation is given as a semi-strong GARCH (1,1) process with heavy tailed errors. We focus on robust estimation of both long-run and short-run volatilities. Our estimation is semiparametric since the long-run volatility is totally unspecified whereas ...

In this paper we study a new class of nonlinear GARCH models. Special interest is devoted to models that are similar to previously introduced smooth transition GARCH models except for the novel feature that a lagged value of conditional variance is used as the transition variable. This choice of the transition variable is mainly motivated by the desire to find useful models for highly persisten...

This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs sampler is usually based on the analytical knowledge of the full conditional posterior densities, such knowledge is not available in regression models with GARCH errors. We show that the Gibbs sampler can be combined with a unidimensional deterministic integration rule applied...

This paper adopts a new approach to estimating the conditional probability distribution of asset returns. It is evident that the exact conditional mean or variance is inherently unobservable for time series. In practice, the popular way is to derive from different models such as GARCH by assuming distributions such as normal, student t, or skewed t. Thus the accuracy of forecast strongly depend...

Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation models is proposed. These have the  exibility of univariate GARCH models coupled with parsimonious parametric models for the c...