Results 171 to 180 of about 51,403 (193)
Some of the next articles are maybe not open access.
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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EMPIRICAL LIKELIHOOD FOR GARCH MODELS
Econometric Theory, 2006Summary: This paper develops an empirical likelihood approach for regular generalized autoregressive conditional heteroskedasticity (GARCH) models and GARCH models with unit roots. For regular GARCH models, it is shown that the log empirical likelihood ratio statistic asymptotically follows a \(\chi^2\) distribution.
Chan, NH, Ling, SQ
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Dynamic Factor Multivariate GARCH Model
SSRN Electronic Journal, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Santos, André A. P. +1 more
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2003
Previous chapters have concentrated on modeling and predicting the conditional mean, or the first order moment, of a univariate time series, and are rarely concerned with the conditional variance, or the second order moment, of a time series. However, it is well known that in financial markets large changes tend to be followed by large changes, and ...
Eric Zivot, Jiahui Wang
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Previous chapters have concentrated on modeling and predicting the conditional mean, or the first order moment, of a univariate time series, and are rarely concerned with the conditional variance, or the second order moment, of a time series. However, it is well known that in financial markets large changes tend to be followed by large changes, and ...
Eric Zivot, Jiahui Wang
openaire +1 more source
2011
Time series data exhibits complex behavior including path-dependency and non-linearity. It is important to employ flexible methods, such as a mixture of GARCH models, to allow for possible changes in the nature of this complex behavior. This work proposes a novel flexible fuzzy GARCH model that attempts to capture complex data behavior.
Almeida, R.J. +3 more
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Time series data exhibits complex behavior including path-dependency and non-linearity. It is important to employ flexible methods, such as a mixture of GARCH models, to allow for possible changes in the nature of this complex behavior. This work proposes a novel flexible fuzzy GARCH model that attempts to capture complex data behavior.
Almeida, R.J. +3 more
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2015
In this study, wavelet based GARCH-Extreme Value Theory (EVT) is proposed to model financial return series to forecast daily value-at-risk. Wavelets based GARCH-EVT is hybrid model combining the wavelet analysis and EVT. Proposed model contains three stages.
ALTUN, Emrah, TATLİDİL, Hüseyin
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In this study, wavelet based GARCH-Extreme Value Theory (EVT) is proposed to model financial return series to forecast daily value-at-risk. Wavelets based GARCH-EVT is hybrid model combining the wavelet analysis and EVT. Proposed model contains three stages.
ALTUN, Emrah, TATLİDİL, Hüseyin
openaire +1 more source