Results 161 to 165 of about 21,854 (165)

Multivariate GARCH Models with Correlation Clustering

Journal of Forecasting, 2009
ABSTRACTA new clustered correlation multivariate generalized autoregressive conditional heteroskedasticity (CC‐MGARCH) model that allows conditional correlations to form clusters is proposed. This model generalizes the time‐varying correlation structure of Tse and Tsui (2002, Journal of Business and Economic Statistics 20: 351–361) by classifying the ...
So, Mike K.P., Yip, Iris W.H.
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Testing multivariate distributions in GARCH models

Journal of Econometrics, 2008
In this paper, we consider testing distributional assumptions in multivariate GARCH models based on empirical processes. Using the fact that joint distribution carries the same amount of information as the marginal together with conditional distributions, we first transform the multivariate data into univariate independent data based on the marginal ...
Jushan Bai, Jushan Bai, Zhihong Chen
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Digital Currencies: A Multivariate GARCH Approach

2020
In this paper we will present quantifiable linkages between five different cryptocurrencies, those being Bitcoin, Ethereum, Ripple, Dash and Monero. Initially, we conduct a review of the existing related work. As the concept of cryptocurrencies is fairly new, the relevant literature is very restricted.
Sofia Papadaki, Stamatis Papangelou
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A full-factor multivariate GARCH model

Econometrics Journal, 2003
Summary A new multivariate time series model with time varying conditional variances and covariances is presented and analysed. A complete analysis of the proposed model is presented consisting of parameter estimation, model selection and volatility prediction. Classical and Bayesian techniques are used for the estimation of the model parameters.
Ioannis D. Vrontos, Petros Dellaportas, Dimitris N. Politis   +2 more
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Multivariate ARCH and GARCH Models

2005
In the previous chapters, we have discussed modelling the conditional mean of the data generation process of a multiple time series, conditional on the past at each particular time point. In that context, the variance or covariance matrix of the conditional distribution was assumed to be time invariant. In fact, in much of the discussion, the residuals
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