Results 171 to 180 of about 1,433 (209)
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A multivariate skew-garch model
2005Empirical research on European stock markets has shown that they behave differently according to the performance of the leading financial market identified as the US market. A positive sign is viewed as good news in the international financial markets, a negative sign means, conversely, bad news.
DE LUCA, GIOVANNI +2 more
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A multivariate GARCH–jump mixture model
Journal of Forecasting, 2023AbstractThis paper proposes a new parsimonious multivariate GARCH–jump (MGARCH–jump) mixture model with multivariate jumps that allows both jump sizes and jump arrivals to be correlated among assets. Dependent jumps impact the conditional moments of returns and beta dynamics of a stock. Applied to daily stock returns, the model identifies co‐jumps well
Chenxing Li, John M. Maheu
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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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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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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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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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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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Fourth Moment Structure of Multivariate GARCH Models
Journal of Financial Econometrics, 2003This article derives conditions for the existence of fourth moments of multivariate GARCH processes in the general vector specification and gives explicit results for the fourth moments and autocovariances of the squares and cross products. Results are provided for the kurtosis and cokurtosis between components.
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Multivariate ARCH and GARCH Models
2005In 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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