Results 211 to 220 of about 291,653 (252)

Maximum entropy autoregressive conditional heteroskedasticity model

Journal of Econometrics, 2009
Abstract In many applications, it has been found that the autoregressive conditional heteroskedasticity (ARCH) model under the conditional normal or Student’s t distributions are not general enough to account for the excess kurtosis in the data. Moreover, asymmetry in the financial data is rarely modeled in a systematic way.
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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On mixture autoregressive conditional heteroskedasticity

Journal of Statistical Planning and Inference, 2018
Abstract We consider mixture univariate autoregressive conditional heteroskedastic models, both with Gaussian or Student t -distributions, which were proposed in the literature for modeling nonlinear time series. We derive sufficient conditions for second order stationarity of these processes.
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Mixture periodic autoregressive conditional heteroskedastic models

Computational Statistics & Data Analysis, 2008
Mixture Periodically Correlated Autoregressive Conditionally Heteroskedastic (MPARCH) model, which extends the ARCH model, is proposed. The primary motivation behind this extension is to make the model consistent with high kurtosis, outliers and extreme events, and at the same time, able to capture the periodicity feature exhibited by the ...
M. Bentarzi, Fayçal Hamdi
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Adaptive Test for Periodicity in Autoregressive Conditional Heteroskedastic Processes

Communications in Statistics - Simulation and Computation, 2010
This article is concerned with the periodicity testing problem in Autoregressive Conditional Heteroskedastic (ARCH) process. Adaptive locally asymptotically optimal test is derived, when the innovation density is unspecified but symmetric satisfying only some general technical assumptions, for the null hypothesis of classical ARCH process against an ...
Mohamed Bentarzi, Mouna Merzougui
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Modelling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model [PDF]

SSRN Electronic Journal, 2008
In this paper, we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new double smooth transition conditional correlation (DSTCC) GARCH model extends the smooth transition conditional correlation (STCC) GARCH model of Silvennoinen and Terasvirta (2005) by including another variable according to which the ...
Silvennoinen, Annastiina, Terasvirta, Timo   +1 more
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The probabilistic properties of the nonlinear autoregressive model with conditional heteroskedasticity

Acta Mathematicae Applicatae Sinica, 1999
In this paper we examine the geometric ergodicities under fairly wide conditions for the following nonlinear autoregressive model $$x_t = \phi (x_{t - 1} , \cdots ,x_{t - p} ) + \varepsilon _t h(x_{t - 1} , \cdots ,x_{t - p} )$$ .
An Hongzhi, Chen Min
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Limit Theory for Explosive Autoregression Under Conditional Heteroskedasticity

SSRN Electronic Journal, 2017
This paper studies an explosive autoregression with conditionally heteroskedastic innovations. The asymptotic distributions of LS, GLS, t-statitiscs, heteroskedasticity-consistent t-statistics and GLS t-statistics are derived for nonstationary local-to-unity and mildly explosive roots, in which ρ_{n} satisfies n(1-ρ_{n})→[-∞,0).
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Generalized Autoregressive Conditional Heteroskedasticity in Credit Risk Measurement

2009 International Conference on Management and Service Science, 2009
This paper presents a modified model for Chinese credit risk management. The model is based on KMV model with consideration of Generalized Autoregressive Conditional Heteroskedasticit (GARCH). Data used in this research are from the balance sheet and the Chinese stock market. T-tests and ROC curves are employed to analyze the data, examining the model.
Jun Xu, Charlene Xie, ChengQi Ou, YunLiang Hu   +3 more
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Testing for multivariate autoregressive conditional heteroskedasticity using wavelets

Computational Statistics & Data Analysis, 2006
Test statistics for autoregressive conditional heteroskedasticity (ARCH) in the residuals from a possibly nonlinear and dynamic multivariate regression model are considered. The new approach is based on estimation of the multivariate spectral density of squared and cross-residuals.
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