We reveal that in the estimation of univariate GARCH or multivariate generalized orthogonal GARCH (GO-GARCH) models, maximizing the likelihood is equivalent to making the standardized residuals as independent as possible. Based on that, we propose three factor GARCH models in the framework of GO-GARCH: independent-factor GARCH exploits factors that are statistically as independent as possible; ...

It is well-known that the estimated GARCH dynamics exhibit common patterns. Starting from this fact we extend the Dynamic Conditional Correlation (DCC) model by allowing for a clustering structure of the univariate GARCH parameters. The model can be estimated in two steps, the first devoted to the clustering structure, and the second focusing on correlation parameters. Differently from the trad...

The Covid-19 pandemic increased uncertainty in the Indonesian stock market. This paper aims to investigate foreign and domestic investors' behavior market, especially during pandemic. method used was E-GARCH DCC-GARCH. results showed that impact of shocks from bad good news greater on return volatility had a positive increase spillover effect NFI's short long term before pandemic, but this did ...

To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive heteroscedasticity (GARCH) models on daily stock return emerging markets. A log-returns three leading indices such as KSE100, KSE30, KSE-ALL from Pakistan Stock Exchange SSE180, SSE50 SSE-Composite Shanghai ...

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...