hide long namesshow long names
hide short namesshow short names
Integer type:  int32  int64  nag_int  show int32  show int32  show int64  show int64  show nag_int  show nag_int
PDF version (NAG web site, 64-bit version, 64-bit version)
Chapter Contents
Chapter Introduction
NAG Toolbox

NAG Toolbox: nag_tsa_uni_garch_asym1_forecast (g13fb)

▸▿ Contents

    1  Purpose
    2  Syntax
    3  Description
    4  References
▸▿ 5  Parameters
    6  Error Indicators and Warnings
    7  Accuracy
    8  Further Comments
    9  Example

Purpose

nag_tsa_uni_garch_asym1_forecast (g13fb) forecasts the conditional variances ht, for t=T+1,…,T+ξ, from a type I AGARCHp,q sequence, where ξ is the forecast horizon and T is the current time (see Engle and Ng (1993)).

Syntax

[fht, ifail] = g13fb(nt, ip, iq, theta, gamma, ht, et, 'num', num)
[fht, ifail] = nag_tsa_uni_garch_asym1_forecast(nt, ip, iq, theta, gamma, ht, et, 'num', num)

Description

Assume the GARCHp,q process can be represented by:
ht=α0+∑i=1qαi εt-i+γ 2+∑i=1pβiht-i,  t=1,2,…,T  
where εt∣ψt-1=N0,ht or εt∣ψt-1=Stdf,ht, has been modelled by nag_tsa_uni_garch_asym1_estim (g13fa) and the estimated conditional variances and residuals are contained in the arrays ht and et respectively.
nag_tsa_uni_garch_asym1_forecast (g13fb) will then use the last maxp,q elements of the arrays ht and et to estimate the conditional variance forecasts, ht∣ψT, where t=T+1,…,T+ξ and ξ is the forecast horizon.

References

Bollerslev T (1986) Generalised autoregressive conditional heteroskedasticity Journal of Econometrics 31 307–327
Engle R (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation Econometrica 50 987–1008
Engle R and Ng V (1993) Measuring and testing the impact of news on volatility Journal of Finance 48 1749–1777
Hamilton J (1994) Time Series Analysis Princeton University Press

Parameters

Compulsory Input Parameters

1:     nt – int64int32nag_int scalar
ξ, the forecast horizon.
Constraint: nt>0.
2:     ip – int64int32nag_int scalar
The number of coefficients, βi, for i=1,2,…,p.
Constraints:
  • maxip,iq≤20;
  • ip≥0.
3:     iq – int64int32nag_int scalar
The number of coefficients, αi, for i=1,2,…,q.
Constraints:
  • maxip,iq≤20;
  • iq≥1.
4:     thetaiq+ip+1 – double array
The first element must contain the coefficient αo and the next iq elements must contain the coefficients αi, for i=1,2,…,q. The remaining ip elements must contain the coefficients βj, for j=1,2,…,p.
5:     gamma – double scalar
The asymmetry parameter γ for the GARCHp,q sequence.
6:     htnum – double array
The sequence of past conditional variances for the GARCHp,q process, ht, for t=1,2,…,T.
7:     etnum – double array
The sequence of past residuals for the GARCHp,q process, εt, for t=1,2,…,T.

Optional Input Parameters

1:     num – int64int32nag_int scalar
Default: the dimension of the arrays ht, et. (An error is raised if these dimensions are not equal.)
The number of terms in the arrays ht and et from the modelled sequence.
Constraint: maxip,iq≤num.

Output Parameters

1:     fhtnt – double array
The forecast values of the conditional variance, ht, for t=T+1,…,T+ξ.
2:     ifail – int64int32nag_int scalar
ifail=0 unless the function detects an error (see Error Indicators and Warnings).

Error Indicators and Warnings

Errors or warnings detected by the function:
   ifail=1
On entry,num<maxip,iq,
oriq<1,
orip<0,
ormaxip,iq>20,
ornt≤0.
   ifail=-99
An unexpected error has been triggered by this routine. Please contact NAG.
   ifail=-399
Your licence key may have expired or may not have been installed correctly.
   ifail=-999
Dynamic memory allocation failed.

Accuracy

Not applicable.

Further Comments

None.

Example

See Example in nag_tsa_uni_garch_asym1_estim (g13fa).

Open in the MATLAB editor: g13fb_example

function g13fb_example


fprintf('g13fb example results\n\n');

num  = 100;
n1   = int64(1);
mn   = n1;
nreg = 2*n1;

% Series
yt = [ 9.04;  9.49;  9.12;  9.23;  9.35;
       9.09;  9.75;  9.23;  8.76;  9.17;
       9.20;  9.64;  8.74;  9.23;  9.42;
       9.70;  9.55; 10.00;  9.18;  9.77;
       9.80;  9.56;  9.28;  9.68;  9.51;
       9.51;  8.97;  9.30;  9.52;  9.41;
       9.53;  9.75;  9.72;  9.38;  9.28;
       9.42;  9.74;  9.75;  9.60;  9.90;
       9.06;  9.92;  9.21;  9.57;  9.42;
       8.65;  8.85;  9.61; 10.77; 10.19;
      10.47; 10.10; 10.21;  9.96;  9.66;
       9.79; 10.30;  9.68; 10.08; 10.38;
       9.69;  9.02;  9.89; 10.46; 10.47;
       9.99;  9.76;  9.78;  9.62; 10.43;
      10.42;  9.95;  9.95;  9.70; 10.24;
       9.78;  9.98;  8.73; 10.23;  9.10;
      10.27;  9.85; 10.44; 10.30; 10.08;
      10.20; 10.14;  9.89;  9.90; 11.33;
       9.71;  9.40;  9.97; 10.92;  9.76;
      10.16; 10.43;  9.60; 10.29; 10.03];

% Exogenous variables
x = zeros(num, nreg+mn);
x(:, 1:nreg) = [0.12, 2.40; 0.12, 2.40; 0.13, 2.40; 0.14, 2.40; 0.14, 2.40;
                0.15, 2.40; 0.16, 2.40; 0.16, 2.40; 0.17, 2.40; 0.18, 2.41;
                0.19, 2.41; 0.19, 2.41; 0.20, 2.41; 0.21, 2.41; 0.21, 2.41;
                0.22, 2.41; 0.23, 2.41; 0.23, 2.41; 0.24, 2.41; 0.25, 2.42;
                0.25, 2.42; 0.26, 2.42; 0.26, 2.42; 0.27, 2.42; 0.28, 2.42;
                0.28, 2.42; 0.29, 2.42; 0.30, 2.42; 0.30, 2.42; 0.31, 2.43;
                0.32, 2.43; 0.32, 2.43; 0.33, 2.43; 0.33, 2.43; 0.34, 2.43;
                0.35, 2.43; 0.35, 2.43; 0.36, 2.43; 0.37, 2.43; 0.37, 2.44;
                0.38, 2.44; 0.38, 2.44; 0.39, 2.44; 0.39, 2.44; 0.40, 2.44;
                0.41, 2.44; 0.41, 2.44; 0.42, 2.44; 0.42, 2.44; 0.43, 2.45;
                0.43, 2.45; 0.44, 2.45; 0.45, 2.45; 0.45, 2.45; 0.46, 2.45;
                0.46, 2.45; 0.47, 2.45; 0.47, 2.45; 0.48, 2.45; 0.48, 2.46;
                0.49, 2.46; 0.49, 2.46; 0.50, 2.46; 0.50, 2.46; 0.51, 2.46;
                0.51, 2.46; 0.52, 2.46; 0.52, 2.46; 0.53, 2.46; 0.53, 2.47;
                0.54, 2.47; 0.54, 2.47; 0.54, 2.47; 0.55, 2.47; 0.55, 2.47;
                0.56, 2.47; 0.56, 2.47; 0.57, 2.47; 0.57, 2.47; 0.57, 2.48;
                0.58, 2.48; 0.58, 2.48; 0.59, 2.48; 0.59, 2.48; 0.59, 2.48;
                0.60, 2.48; 0.60, 2.48; 0.61, 2.48; 0.61, 2.48; 0.61, 2.49;
                0.62, 2.49; 0.62, 2.49; 0.62, 2.49; 0.63, 2.49; 0.63, 2.49;
                0.63, 2.49; 0.64, 2.49; 0.64, 2.49; 0.64, 2.49; 0.64, 2.50];

% Details of model to fit
dist = 't';
ip = n1;
iq = n1;
isym = n1;

% Control parameters
copts = [true; true];
maxit = int64(200);
tol = 0.00001;

% Initial values
gammaval = -0.1;
theta = [0.05; 0.1; 0.15; gammaval; 2.6; 1.5; 0; 0];

% Forecast horizon
nt = 4*n1;

% Fit the GARCH model
[theta, se, sc, covar, hp, et, ht, lgf, ifail] = ...
  g13fa( ...
         dist, yt, x, ip, iq, mn, isym, theta, 0, copts, maxit, tol, ...
         'nreg',nreg);

% Calculate the volatility forecast
[fht, ifail] = g13fb( ...
                      nt, ip, iq, theta, gammaval, ht, et);

% Output the results
fprintf('\n               Parameter        Standard\n');
fprintf('               estimates         errors\n');

% Output the coefficient alpha_0
fprintf('Alpha0 %16.2f%16.2f\n', theta(1), se(1));
l = 2;

% Output the coefficients alpha_i
for i = l:l+iq-1
  fprintf('Alpha%d %16.2f%16.2f\n', i-1, theta(i), se(i));
end
l = l+iq;

% Output the coefficients beta_j
fprintf('\n');
for i = l:l+ip-1
  fprintf(' Beta%d %16.2f%16.2f\n', i-l+1, theta(i), se(i));
end
l = l+ip;

% Output the estimated asymmetry parameter, gamma
if (isym == 1)
  fprintf('\n Gamma %16.2f%16.2f\n', theta(l), se(l));
  l = l+1;
end
% Output the estimated degrees of freedom, df
if (dist == 't')
  fprintf('\n    DF %16.2f%16.2f\n', theta(l), se(l));
  l = l + 1;
end

% Output the estimated mean term, b_0
if (mn == 1)
  fprintf('\n    B0 %16.2f%16.2f\n', theta(l), se(l));
  l = l + 1;
end

% Output the estimated linear regression coefficients, b_i
for i = l:l+nreg-1
  fprintf('    B%d %16.2f%16.2f\n', i-l+1, theta(i), se(i));
end

% Display the volatility forecast
fprintf('\nVolatility forecast = %12.4f\n', fht(nt));



g13fb example results


               Parameter        Standard
               estimates         errors
Alpha0             0.00            0.06
Alpha1             0.11            0.13

 Beta1             0.66            0.23

 Gamma            -0.62            0.62

    DF             6.25            4.70

    B0             3.85           24.11
    B1             1.48            1.82
    B2             2.15           10.16

Volatility forecast =       0.0626
PDF version (NAG web site, 64-bit version, 64-bit version)
Chapter Contents
Chapter Introduction
NAG Toolbox
© The Numerical Algorithms Group Ltd, Oxford, UK. 2009–2015