in this study, the relationship between inflation and economic growth of iran is conciliated with a perspective on uncertainty of inflation. we use a generalized autoregressive conditional heteroskedasticity (garch) model that make possible this advantage that conditional variance of error term changes along the time, also, vector error correction model (vecm) is stable. for surveying the co-in...

We derive a framework for asymptotically valid inference in stable vector autoregressive (VAR) models with conditional heteroskedasticity of unknown form. We prove a joint central limit theorem for the VAR slope parameter and innovation covariance parameter estimators and address bootstrap inference as well. Our results are important for correct inference on VAR statistics that depend both on t...

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...

This paper reexamines the effects of inflation uncertainty on real economic activity by utilizing a flexible, dynamic, multivariate framework that accommodates possible interaction between the conditional means and variances. The empirical model is based on the identified vector autoregressive regression of Bernanke and Gertler (1995), modified to accommodate multivariate generalized autoregres...

There is a compelling need to accurately and efficiently compute option values. Existing literature shows that models based on constant stock volatilities have been widely used in option valuation. However, stock volatilities change constantly in real life situations. The introduction of the Auto Regressive Conditional Heteroskedasticity (ARCH) model and subsequently, the Generalized Auto Regre...

This study focuses on the relation between fluctuation of international oil prices and China’s energy stock market during COVID-19 pandemic, using a dynamic conditional correlation generalized autoregressive heteroskedasticity model. We confirm spillover effect volatility price returns determine that leadership has been heavily influenced pandemic.

Based on a vector autoregressive model and dynamic conditional correlation generalized heteroskedasticity model, this study explores the relation between international crude oil market Chinese energy stock market. The findings suggest positive one-way spillover effect of returns China’s returns. Furthermore, two markets is time varying.

We propose a new class of financial volatility models, which we call the REcurrent Conditional Heteroskedastic (RECH) to improve both in-sample analysis and out-of-sample forecast performance traditional conditional heteroskedastic models. In particular, incorporate auxiliary deterministic processes, governed by recurrent neural networks, into variance e.g. GARCH-type flexibly capture dynamics ...

improved the exposition, but they are in no way responsible for remaining flaws. Engle's footsteps range widely. His major contributions include early work on band-spectral regression, development and unification of the theory of model specification tests (particularly Lagrange multiplier tests), clarification of the meaning of econometric exogeneity and its relationship to causality, and his l...