FcGARCH: Forecasting with Model for Generalized Autoregressive...

In Mthrun/TSAT: Time Series Analysis Tools (TSAT)

Forecasting with Model for Generalized Autoregressive Conditional Heteroscedasticity

Description

Autoregressive conditional heteroskedasticity (ARCH) model is a statistical model for time series data that describes the variance of the current error term or innovation as a function of the actual sizes of the previous time periods error terms often the variance is related to the squares of the previous innovations - Wikipedia

Usage

FcGARCH(DataVec, VarianceModel = list(model = "sGARCH", garchOrder = c(1, 1)),

 ForecastHorizon, DistributionModel = 'norm', 

PlotIt = FALSE, Summary = FALSE, ...)

Arguments

DataVec	[1:n] Datavector
VarianceModel	see ugarchspec, The The ARCH (q) and GARCH (p) orders. be chosen manually accordingly to the problem at hand, possible model variants are “sGARCH”, “fGARCH”, “eGARCH”, “gjrGARCH”, “apARCH” and “iGARCH” and “csGARCH”.
ForecastHorizon	Number of Forecast units of time
DistributionModel	Optional, see ugarchspec
PlotIt	Optional, If TRUE, plots the forecast
Summary	Optional, If TRUE, plots the summary of the model
...	Optional, Further argument passed on to ugarchspec

Details

The ARCH model is appropriate when the error variance in a time series follows an autoregressive (AR) model; if an autoregressive moving average model (ARMA) model is assumed for the error variance, the model is a generalized autoregressive conditional heteroskedasticity (GARCH) model - Wikiepdia

Value

List of

Forecast	[1:ForecastHorizon] of Data
Model	Model, the output of ugarchspec

in mode invisible

Note

wrapper for ugarchspec

Author(s)

Michael Thrun

References

R. F. Engle: Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK. Inflation. In: Econometrica, Vol.: 50, pp. 987 - 1008, 1982.

T. Bollerslev: Generalized Autoregressive Conditional Heteroskedasticity. In: Journal of Econometrics, Vol.: 31 No.: 3, pp. 307 - 327, 1986.

Franke, J., Härdle, W., Hafner, C. M.: Statistics of Financial Markets: An Introduction. Springer, Berlin, Heidelberg, New York, 2. Auflage, 2008.

See Also

ugarchspec

Examples

# Plot with forecast
FcGARCH(TempMelbourneAustralia$Temp,ForecastHorizon=10, PlotIt = TRUE)#
# Plot with summary
FcGARCH(TempMelbourneAustralia$Temp,ForecastHorizon=10, Summary = TRUE)

Mthrun/TSAT documentation built on Feb. 5, 2024, 11:15 p.m.