New dynamic models for realized covariance matrices are proposed. The expected value of the realized covariance matrix is specified in two steps: a model for each realized variance, and a model for the realized correlation matrix. The realized correlation model is a dynamic conditional correlation model. Estimation can be done in two steps as well, and a QML interpretation is given to each step...

One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC) specification. However, the underlying stochastic process to derive DCC has not yet been established, which has made problematic the derivation of asymptotic properties of the Quasi-Maximum Likelihood Estimators (QMLE). To date, the statistical properties of the QMLE of the DCC...

This paper examines the hedging effectiveness of gold futures for the stock market in minimizing variance and downside risks, including value at risk and expected shortfall using data from the Iran emerging capital market during four different sub-periods from December 2008 to August 2018. We employ dynamic conditional correlation models including VARMA-BGARCH (DCC, ADCC, BEKK, and ABEKK) and c...

In this paper we put forward a generalization of the Dynamic Conditional Correlation (DCC) Model of Engle (2002). Our model allows for asset-specific correlation sensitivities, which is useful in particular if one aims to summarize a large number of asset returns. The resultant GDCC model is considered for daily data on 18 German stock returns, which are all included in the DAX, and for 25 UK s...

This paper estimates the dynamic conditional correlations in the returns on Tapis oil spot and onemonth forward prices for the period 2 June 1992 to 16 January 2004, using recently developed multivariate conditional volatility models, namely the Constant Conditional Correlation Multivariate GARCH (CCCMGARCH) model of Bollerslev [1990], Vector Autoregressive Moving Average – GARCH (VARMAGARCH) m...

The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does n...

A signal processing technique is presented to improve the angular rate accuracy of Micro-Electro-Mechanical System (MEMS) gyroscope by combining numerous gyroscopes. Based on the conditional correlation between gyroscopes, a dynamic data fusion model is established. Firstly, the gyroscope error model is built through Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process to i...