Garch
Represents a "Generalized Autoregressive Conditional Heteroskedasticity" (GARCH) model.
Definition
Namespace: Extreme.Statistics.TimeSeriesAnalysis
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
C#
Assembly: Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.23
public class GarchModel : TimeSeriesModel<double>- Inheritance
- Object → Model → TimeSeriesModel<Double> → GarchModel
Remarks
Use the GarchModel class to model a time series where the expected value of the error terms may vary over time. GARCH models
Constructors
| Garch | Constructs a new ARCH model of the specified order. |
| Garch | Constructs a new GARCH model of the specified order. |
| Garch | Constructs a new ARCH model of the specified order. |
| Garch | Constructs a new GARCH model of the specified order. |
Properties
| Arch | Gets the parameters corresponding to the lagged squared innovations in the model. |
| Base |
Gets an index containing the keys of the columns
that are required inputs to the model.
(Inherited from Model) |
| Computed |
Gets whether the model has been computed.
(Inherited from Model) Obsolete. |
| Conditional | Gets a vector containing the estimated conditional variances. |
| Constant | Gets the parameter for the constant term in the model. |
| Covariance |
Gets the covariance matrix of the model parameters.
(Inherited from TimeSeriesModel<T>) |
| Data |
Gets an object that contains all the data used as input to the model.
(Inherited from Model) |
| Degrees |
Gets the total degrees of freedom of the data.
(Inherited from TimeSeriesModel<T>) |
| Distribution | Gets the conditional distribution of the innovations process. If no value is specified, a standard normal distribution is assumed. |
| Fitted |
Gets whether the model has been computed.
(Inherited from Model) |
| Garch | Gets the parameters corresponding to the lagged conditional variances in the model. |
| Innovation | Gets or sets the probability distribution of the innovations of the GARCH model. |
| Input |
Gets the schema for the features used for fitting the model.
(Inherited from Model) |
| Log |
Gets the log-likelihood that the model generated the data.
(Inherited from TimeSeriesModel<T>) |
| Max |
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model) |
| Model |
Gets the collection of variables used in the model.
(Inherited from Model) |
| Number |
Gets the number of observations the model is based on.
(Inherited from Model) |
| P | Gets or sets the number of lagged squared innovations in the model. |
| Parallel |
Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model) |
| Parameters |
Gets the collection of parameters associated with this model.
(Inherited from TimeSeriesModel<T>) |
| Parameter |
Gets the collection of parameters associated with this model.
(Inherited from TimeSeriesModel<T>) |
| Predictions |
Gets a vector containing the model's predicted values for the dependent variable.
(Inherited from TimeSeriesModel<T>) |
| Q | Gets or sets the number of lagged conditional variances in the model. |
| Residuals |
Gets a vector containing the residuals of the model.
(Inherited from TimeSeriesModel<T>) |
| Residual |
Gets the sum of squares of the residuals of the model.
(Inherited from TimeSeriesModel<T>) |
| Standard |
Gets the standard error of the regression.
(Inherited from TimeSeriesModel<T>) |
| Status |
Gets the status of the model, which determines which information is available.
(Inherited from Model) |
| Student | Gets or sets the degrees of freedom when fitting a GARCH model with innovations from a student-t distribution. |
| Supports |
Indicates whether the model supports case weights.
(Inherited from Model) |
| Time |
Gets the time series that is being modeled.
(Inherited from TimeSeriesModel<T>) |
| Weights |
Gets or sets the actual weights.
(Inherited from Model) |
Methods
| Compute() |
Computes the model.
(Inherited from Model) Obsolete. |
| Compute( |
Computes the model.
(Inherited from Model) Obsolete. |
| Equals | Determines whether the specified object is equal to the current object. (Inherited from Object) |
| Finalize | Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object) |
| Fit() |
Fits the model to the data.
(Inherited from Model) |
| Fit( |
Fits the model to the data.
(Inherited from Model) |
| Fit |
Computes the model to the specified input
using the specified parallelization options.
(Overrides Model.FitCore(ModelInput, ParallelOptions)) |
| Forecast() |
Returns the one step ahead forecast.
(Inherited from TimeSeriesModel<T>) |
| Forecast( |
Returns the forecast for the specified number of steps ahead.
(Overrides TimeSeriesModel<T>.Forecast(Int32)) |
| Forecast( | Returns the forecast for the specified number of steps ahead. |
| Get |
Returns the Akaike information criterion (AIC) value for the model.
(Inherited from TimeSeriesModel<T>) |
| Get |
Returns the Bayesian information criterion (BIC) value for the model.
(Inherited from TimeSeriesModel<T>) |
| GetHashCode | Serves as the default hash function. (Inherited from Object) |
| GetType | Gets the Type of the current instance. (Inherited from Object) |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object) |
| Reset |
Clears all fitted model parameters.
(Inherited from Model) Obsolete. |
| Reset |
Clears all fitted model parameters.
(Inherited from Model) |
| Set |
Uses the specified data frame as the source for all input variables.
(Inherited from Model) |
| Summarize() |
Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model) |
| Summarize( |
Returns a string containing a human-readable summary of the object using the specified options.
(Overrides Model.Summarize(SummaryOptions)) |
| ToString | Returns a string that represents the current object. (Inherited from Model) |