lgarch
(Package: lgarch) :
Estimate a log-GARCH model
Fit a log-GARCH model by either (nonlinear) Least Squares (LS) or Quasi Maximum Likelihood (QML) via the ARMA representation. For QML either the Gaussian or centred exponential chi-squared distribution can be used as instrumental density, see Sucarrat, Gronneberg and Escribano (2013), and Francq and Sucarrat (2013). Zero-values on the dependent variable y are treated as missing values, as suggested in Sucarrat and Escribano (2013). Estimation is via the nlminb function, whereas a numerical estimate of the Hessian is obtained with optimHess for the computation of the variance-covariance matrix
● Data Source:
CranContrib
● Keywords: Financial Econometrics, Statistical Models, Time Series
● Alias: lgarch
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lgarchObjective and lgarchRecursion1 are auxiliary functions called by lgarch. The functions are not intended for the average user.
● Data Source:
CranContrib
● Keywords: Financial Econometrics, Statistical Models, Time Series
● Alias: lgarchObjective, lgarchRecursion1
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lgarchSim
(Package: lgarch) :
Simulate from a univariate log-GARCH model
Simulate the y series (typically a financial return or the error in a regression) from a log-GARCH model. Optionally, the conditional standard deviation, the standardised error (z) and their logarithmic transformations are also returned.
● Data Source:
CranContrib
● Keywords: Financial Econometrics, Statistical Models, Time Series
● Alias: lgarchSim
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coef.mlgarch
(Package: lgarch) :
Extraction methods for 'mlgarch' objects
Extraction methods for objects of class 'mlgarch' (i.e. the result of estimating a multivariate CCC-log-GARCH model)
● Data Source:
CranContrib
● Keywords: Financial Econometrics, Statistical Models, Time Series
● Alias: coef.mlgarch, fitted.mlgarch, logLik.mlgarch, print.mlgarch, residuals.mlgarch, summary.mlgarch, vcov.mlgarch
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mlgarch
(Package: lgarch) :
Estimate a multivariate CCC-log-GARCH(1,1) model
Fit a multivariate Constant Conditional Correlation (CCC) log-GARCH(1,1) model with multivariate Gaussian Quasi Maximum Likelihood (QML) via the VARMA representation, see Sucarrat, Gronneberg and Escribano (2013). Zero-values on y are treated as missing values, as suggested in Sucarrat and Escribano (2013). Estimation is via the nlminb function, whereas a numerical estimate of the Hessian is obtained with optimHess for the computation of the variance-covariance matrix
● Data Source:
CranContrib
● Keywords: Financial Econometrics, Statistical Models, Time Series
● Alias: mlgarch
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glag
(Package: lgarch) :
Lag a vector or a matrix, with special treatment of zoo objects
Similar to the lag function from the stats package, but glag enables padding (e.g. NAs or 0s) of the lost entries. Contrary to the lag function in the stats package, however, the default in glag is to pad (with NAs). The glag is particularly suited for zoo objects, since their indexing is retained
● Data Source:
CranContrib
● Keywords: Financial Econometrics, Statistical Models, Time Series
● Alias: glag
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lgarch-package
(Package: lgarch) :
Simulation and estimation of log-GARCH models
This package provides facilities for the simulation and estimation of univariate log-GARCH models, and for the multivariate CCC-log-GARCH(1,1) model, see Sucarrat, Gronneberg and Escribano (2013), Sucarrat and Escribano (2013), and Francq and Sucarrat (2013).
mlgarchSim
(Package: lgarch) :
Simulate from a multivariate log-GARCH(1,1) model
Simulate the y series (typically a collection of financial returns or regression errors) from a log-GARCH model. Optionally, the conditional standard deviation and the standardised error, together with their logarithmic transformations, are also returned.
● Data Source:
CranContrib
● Keywords: Financial Econometrics, Statistical Models, Time Series
● Alias: mlgarchSim
●
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