Results 191 to 200 of about 1,495 (237)

Reprint of: Generalized Autoregressive Conditional Heteroskedasticity

Journal of Econometrics, 2023
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Maximum entropy autoregressive conditional heteroskedasticity model

Journal of Econometrics, 2009
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Sung Y. Park, Anil K. Bera
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Stable Randomized Generalized Autoregressive Conditional Heteroskedastic Models

Econometrics and Statistics, 2020
Abstract The class of Randomized Generalized Autoregressive Conditional Heteroskedastic (R-GARCH) models represents a generalization of the GARCH models, adding a random term to the volatility with the purpose to better accommodate the heaviness of the tails expected for returns in the financial field. In fact, it is assumed that this term has stable
Jhames M. Sampaio, Pedro A. Morettin
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Autoregressive Conditional Heteroskedasticity

2007
All models discussed so far use the conditional expectation to describe the mean development of one or more time series. The optimal forecast, in the sense that the variance of the forecast errors will be minimised, is given by the conditional mean of the underlying model.
Gebhard Kirchgässner, Jürgen Wolters
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Fractionally integrated generalized autoregressive conditional heteroskedasticity

Journal of Econometrics, 1996
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Baillie, Richard T., Bollerslev, Tim, Mikkelsen, Hans Ole   +2 more
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Mixture periodic autoregressive conditional heteroskedastic models

Computational Statistics & Data Analysis, 2008
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Bentarzi, M., Hamdi, F.
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Autoregressive conditional heteroskedasticity and changes in regime

Journal of Econometrics, 1994
ARCH models often impute a lot of persistence to stock volatility and yet give relatively poor forecasts. One explanation is that extremely large shocks, such as the October 1987 crash, arise from quite different causes and have different consequences for subsequent volatility than do small shocks. We explore this possibility with U.S.
Hamilton, James D., Susmel, Raul
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Autoregressive Conditional Parameter Model with Heteroskedastic Regressors

SSRN Electronic Journal, 2016
To do with the ARCH effects in explanatory variables, a new time-varying parameter regression is developed. The autoregressive conditional parameter (ACP) model with heteroskedastic regressors extends the ACP model of Lu and Wang (2016) by allowing explanatory variables to follow a multivariate GARCH process.
Fengbin Lu, Shouyang Wang
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On mixture autoregressive conditional heteroskedasticity

Journal of Statistical Planning and Inference, 2018
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Random rounded integer-valued autoregressive conditional heteroskedastic process

Statistical Papers, 2012
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Liu, Tianqing, Yuan, Xiaohui
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