Results 191 to 200 of about 1,876 (209)
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2003
When modeling multivariate economic and financial time series using vector autoregressive (VAR) models, squared residuals often exhibit significant serial correlation. For univariate time series, Chapter 7 indicates that the time series may be conditionally heteroskedastic, and GARCH models have been proved to be very successful at modeling the serial ...
Eric Zivot, Jiahui Wang
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When modeling multivariate economic and financial time series using vector autoregressive (VAR) models, squared residuals often exhibit significant serial correlation. For univariate time series, Chapter 7 indicates that the time series may be conditionally heteroskedastic, and GARCH models have been proved to be very successful at modeling the serial ...
Eric Zivot, Jiahui Wang
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GO‐GARCH: a multivariate generalized orthogonal GARCH model
Journal of Applied Econometrics, 2002AbstractMultivariate GARCH specifications are typically determined by means of practical considerations such as the ease of estimation, which often results in a serious loss of generality. A new type of multivariate GARCH model is proposed, in which potentially large covariance matrices can be parameterized with a fairly large degree of freedom while ...
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2017
4 Title: Multivariate GARCH Author: Mgr. Milan Mad'ar Department: Katedra pravděpodobnosti a matematické statistiky Abstract: This thesis will examine the regional and global linkages as evi- dence of integration of stock markets in Frankfurt, Amsterdam, Prague and the U.S. Therefore we will utilize the multivariate GARCH approach that investigates the
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4 Title: Multivariate GARCH Author: Mgr. Milan Mad'ar Department: Katedra pravděpodobnosti a matematické statistiky Abstract: This thesis will examine the regional and global linkages as evi- dence of integration of stock markets in Frankfurt, Amsterdam, Prague and the U.S. Therefore we will utilize the multivariate GARCH approach that investigates the
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Multivariate
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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Second‐Order Noncausality in Multivariate GARCH Processes
Journal of Time Series Analysis, 2000Typical multivariate economic time series may exhibit co‐behavior patterns not only in the conditional means, but also in the conditional variances. In this paper we give two new definitions of variance noncausality in a multivariate setting a Granger‐type noncausality and a linear Granger noncausality through projections on Hilbert spaces.
Comte, Fabienne, Lieberman, Offer
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Variance (Non) Causality in Multivariate GARCH
Econometric Reviews, 2007This paper extends the current literature on the variance-causality topic providing the coefficient restrictions ensuring variance noncausality within multivariate GARCH models with in-mean effects. Furthermore, this paper presents a new multivariate model, the exponential causality GARCH.
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A Student-T Full Factor Multivariate GARCH Model
SSRN Electronic Journal, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Diamantopoulos, K., Vrontos, I. D.
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REALIZED BETA GARCH: A MULTIVARIATE GARCH MODEL WITH REALIZED MEASURES OF VOLATILITY
Journal of Applied Econometrics, 2014SUMMARYWe introduce a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model that incorporates realized measures of variances and covariances. Realized measures extract information about the current levels of volatilities and correlations from high‐frequency data, which is particularly useful for modeling financial returns
HANSEN, Peter Reinhard +2 more
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A full-factor multivariate GARCH model
Econometrics Journal, 2003Summary: 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.
Vrontos, I. D. +2 more
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Testing multivariate distributions in GARCH models
Journal of Econometrics, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bai, Jushan, Chen, Zhihong
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