Dynamic Conditional Correlation

  title={Dynamic Conditional Correlation},
  author={Robert F. Engle},
  journal={Journal of Business \& Economic Statistics},
  pages={339 - 350}
  • R. Engle
  • Published 1 July 2002
  • Mathematics
  • Journal of Business & Economic Statistics
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 flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two-step… 

Average Conditional Correlation and Tree Structures for Multivariate GARCH Models

We propose a simple class of multivariate GARCH models, allowing for time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex

A General Multivariate Threshold GARCH Model With Dynamic Conditional Correlations

We introduce a new multivariate GARCH model with multivariate thresholds in conditional correlations and develop a two-step estimation procedure that is feasible in large dimensional applications.

Dynamic Conditional Correlation GARCH: A Multivariate Time Series Novel using a Bayesian Approach

The Dynamic Conditional Correlation GARCH (DCC-GARCH) mutation model is considered using a Monte Carlo approach via Markov chains in the estimation of parameters, time-dependence variation is

Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations

In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The approach adopted here is based on the decomposition of the covariances into

The variance implied conditional correlation

ABSTRACT We apply univariate GARCH models to construct a computationally simple filter for estimating the conditional correlation matrix of asset returns. The proposed Variance Implied Conditional

A local dynamic conditional correlation model

This paper introduces the idea that the variances or correlations in financial returns may all change conditionally and slowly over time. A multi-step local dynamic conditional correlation model is

Cointegration models with non Gaussian GARCH innovations

This paper presents the estimation procedures for a bivariate cointegration model when the errors are generated by a constant conditional correlation model. In particular, the method of maximum

Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison

In this paper we show that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called

A Geometric GARCH Framework for Covariance Dynamics

This paper develops new multivariate GARCH models that respect intrinsic geometric properties of covariance matrix, and are physically meaningful. These models can be specified using either asset

Title Stata.com Mgarch — Multivariate Garch Models

  • Economics
Description Syntax Remarks and examples References Also see Description mgarch estimates the parameters of multivariate generalized autoregressive conditional-heteroskedasticity (MGARCH) models.



Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model

A multivariate time series model with time varying conditional variances and covariances, but constant conditional correlations is proposed. In a multivariate regression framework, the model is

Chapter 49 Arch models

Modeling Asymmetric Comovements of Asset Returns

Existing time-varying covariance models usually impose strong restrictions on how past shocks affect the forecasted covariance matrix. In this article we compare the restrictions imposed by the four

Asset Pricing with a Factor Arch Covariance Structure: Empirical Estimates for Treasury Bills

Asset pricing relations are developed for a vector of assets with a time varying covariance structure. Assuming that the eigenvectors are constant but the eigenvalues changing, both the Capital Asset

Large Scale Conditional Covariance Matrix Modeling, Estimation and Testing

A new representation of the diagonal Vech model is given using the Hadamard product. Sufficient conditions on parameter matrices are provided to ensure the positive definiteness of covariance

A Capital Asset Pricing Model with Time-Varying Covariances

The capital asset pricing model provides a theoretical structure for the pricing of assets with uncertain returns. The premium to induce risk-averse investors to bear risk is proportional to the

Multivariate Simultaneous Generalized ARCH

This paper presents theoretical results on the formulation and estimation of multivariate generalized ARCH models within simultaneous equations systems. A new parameterization of the multivariate

Large sample estimation and hypothesis testing