GARCH_FORE - Forecasting for GARCH Model

Calculates the out-of-sample conditional mean and volatility forecast.

 

Syntax

GARCH_FORE(X, Sigmas, Order, mean, alphas, betas, innovation, Nu, T, Type, alpha)

X is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)).

Sigmas is the univariate time series data (a one dimensional array of cells (e.g. rows or columns)) of the last q realized volatilities.

Order is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).

Order Description
1 ascending (the first data point corresponds to the earliest date) (default)
0 descending (the first data point corresponds to the latest date)

mean is the GARCH model mean (i.e. mu). if missing, a default of 0 is assumed.

alphas are the parameters of the ARCH(p) component model (starting with the lowest lag).

betas are the parameters of the GARCH(q) component model (starting with the lowest lag).

innovation is the probability distribution function of the innovations/residuals (1=Gaussian (default), 2=t-Distribution, 3=GED).

value Description
1 Gaussian or Normal Distribution (default)
2 Student's t-Distribution
3 Generalized Error Distribution (GED)

Nu is the shape parameter (or degrees of freedom) of the innovations/residuals probability distribution function.

T is the forecast time/horizon (expressed in terms of steps beyond end of the time series). If missing, a default of 1 is assumed.

Type is an integer switch to select the forecast output type: (1=mean (default), 2=Std. Error, 3=Term Struct, 4=LL, 5=UL)

Order Description
1 Mean forecast value (default)
2 Forecast standard error (aka local volatility)
3 Volatility term structure
4 Lower limit of the forecast confidence interval.
5 Upper limit of the forecast confidence interval.

alpha is the statistical significance level. If missing, a default of 5% is assumed.

 

Remarks

  1. The underlying model is described here.
  2. The time series is homogeneous or equally spaced.
  3. The time series may include missing values (e.g. #N/A) at either end.
  4. The number of parameters in the input argument - alpha - determines the order of the ARCH component model.
  5. The number of parameters in the input argument - beta - determines the order of the GARCH component model.
  6. By definition, the GARCH_FORE function returns a constant value equal to the model mean (i.e. $\mu$) for all horizons.

Examples

Example 1:

 
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A B C D
Date Data    
January 10, 2008 -2.827 GARCH(1,1)  
January 11, 2008 -0.947 Mean -0.160
January 12, 2008 -0.877 Alpha_0 0.608
January 14, 2008 1.209 Alpha_1 0.00
January 13, 2008 -1.669 Beta_1 0.391
January 15, 2008 0.835    
January 16, 2008 -0.266    
January 17, 2008 1.361    
January 18, 2008 -0.343    
January 19, 2008 0.475    
January 20, 2008 -1.153    
January 21, 2008 1.144    
January 22, 2008 -1.070    
January 23, 2008 -1.491    
January 24, 2008 0.686    
January 25, 2008 0.975    
January 26, 2008 -1.316    
January 27, 2008 0.125    
January 28, 2008 0.712    
January 29, 2008 -1.530    
January 30, 2008 0.918    
January 31, 2008 0.365    
February 1, 2008 -0.997    
February 2, 2008 -0.360    
February 3, 2008 1.347    
February 4, 2008 -1.339    
February 5, 2008 0.481    
February 6, 2008 -1.270    
February 7, 2008 1.710    
February 8, 2008 -0.125    
February 9, 2008 -0.940    


  Formula Description (Result)
  =GARCH_FORE($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,1) Forecasted conditional mean at T+1 (-0.160)
  =GARCH_FORE($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,2) Forecasted conditional mean at T+2 (-0.160)
  =GARCH_FORE($B$2:$B$32,1,$D$3,$D$4:$D$5,$D$6,3) Forecasted conditional mean at T+3 (-0.160)

Files Examples

References

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