Whether to include an asymmetry term to the DCC model (thus Reweighted estimator (“delta”), the number of subsets (“ns”) and Including the proportion to trim (“gamma”), the critical value for The tuning parameters to the robust regression The criterion to use for choosing the best lag whenĪllows for a matrix of common pre-lagged external The maximum VAR lag to search for best fit. Whether to use the robust version of VAR. Whether to fit a VAR model for the conditional mean. Multispec on a list of univariate GARCH specifications. radical: The Robust Accurate, Direct ICA aLgorithm (RADICAL).Ĭgarchspec ( uspec, VAR = FALSE, robust = FALSE, lag = 1, lag.max = NULL, lag.criterion = c ( "AIC", "HQ", "SC", "FPE" ), external.regressors = NULL, ntrol = list ( gamma = 0.25, delta = 0.01, nc = 10, ns = 500 ), dccOrder = c ( 1, 1 ), asymmetric = FALSE, distribution.model = list ( copula = c ( "mvnorm", "mvt" ), method = c ( "Kendall", "ML" ), time.varying = FALSE, transformation = c ( "parametric", "empirical", "spd" )), start.pars = list (), fixed.pars = list ())Ī uGARCHmultispec object created by calling.mGARCHspec-class: Class: Multivariate GARCH Specification.mGARCHsim-class: Class: Multivariate GARCH Simulation Class.mGARCHroll-class: Class: Multivariate GARCH Roll Class.mGARCHforecast-class: Class: Multivariate GARCH Forecast Class.mGARCHfit-class: Class: Multivariate GARCH Fit Class.mGARCHfilter-class: Class: Multivariate GARCH Filter Class.last-methods: First and Last methods for accessing objects.goload-methods: Load Scenario from File.gogarchspec-methods: function: GO-GARCH Specification.goGARCHspec-class: class: GO-GARCH Specification Class.gogarchsim-methods: function: GO-GARCH Simulation.goGARCHsim-class: class: GO-GARCH Simultion Class.gogarchroll-methods: function: GO-GARCH Rolling Estimation.goGARCHroll-class: class: GO-GARCH Roll Class.gogarchforecast-methods: function: GO-GARCH Forecast.goGARCHforecast-class: class: GO-GARCH Forecast Class.gogarchfit-methods: function: GO-GARCH Filter.goGARCHfit-class: class: GO-GARCH Fit Class.gogarchfilter-methods: function: GO-GARCH Filter.goGARCHfilter-class: class: GO-GARCH Filter Class.goGARCHfft-class: Class: GO-GARCH portfolio density.fmoments-methods: Moment Based Forecast Generation.dji30retw: data: Dow Jones 30 Constituents Closing Value log Weekly. DCCtest: Engle and Sheppard Test of Dynamic Correlation.dccspec-methods: function: DCC-GARCH Specification.DCCspec-class: class: DCC Specification Class.dccsim-methods: function: DCC-GARCH Simulation.DCCsim-class: class: DCC Forecast Class.dccroll-methods: function: DCC-GARCH Rolling Forecast.dccforecast-methods: function: DCC-GARCH Forecast.DCCforecast-class: class: DCC Forecast Class.dccfit-methods: function: DCC-GARCH Fit.dccfilter-methods: function: DCC-GARCH Filter.DCCfilter-class: class: DCC Filter Class.cordist: A Correlation Distance Measure.cgarchspec-methods: function: Copula-GARCH Specification.cGARCHspec-class: class: Copula Specification Class.cgarchsim-methods: function: Copula-GARCH Simulation.cGARCHsim-class: class: Copula Simulation Class.cgarchfit-methods: function: Copula-GARCH Fit.cGARCHfit-class: class: Copula Fit Class.cgarchfilter-methods: function: Copula-GARCH Filter.cGARCHfilter-class: class: Copula Filter Class.
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