/* nag_tsa_uni_garch_gjr_estim (g13fec) Example Program.
*
* Copyright 2024 Numerical Algorithms Group.
*
* NAG C Library
*
* Mark 30.1, 2024.
*
*/
#include <ctype.h>
#include <math.h>
#include <nag.h>
#include <stdio.h>
#define X(I, J) x[(I)*tdx + (J)]
int main(void) {
/* Integer scalar and array declarations */
Integer exit_status = 0;
Integer i, j, k, npar, tdc, tdx, lr, lstate;
Integer *state = 0;
/* NAG structures and data types */
NagError fail;
Nag_Boolean fcall;
/* Double scalar and array declarations */
double fac1, pht, lgf, xterm;
double *covar = 0, *cvar = 0, *etm = 0, *ht = 0;
double *htm = 0, *r = 0, *sc = 0, *se = 0, *theta = 0;
double *x = 0, *yt = 0;
/* Choose the base generator */
Nag_BaseRNG genid = Nag_Basic;
Integer subid = 0;
/* Set the seed */
Integer seed[] = {1762543};
Integer lseed = 1;
/* Set parameters for the (randomly generated) time series ... */
/* Generate data assuming normally distributed errors */
Nag_ErrorDistn dist = Nag_NormalDistn;
double df = 0;
/* Size of the time series */
Integer num = 1000;
/* MA and AR parameters */
Integer ip = 1;
Integer iq = 1;
double param[] = {0.4, 0.1, 0.7};
/* Asymmetry parameter */
double gamma = 0.1;
/* Regression parameters */
Integer nreg = 2;
double mean = 4.0;
double bx[] = {1.5, 2.5};
/* ... end of parameters for (randomly generated) time series */
/* When fitting a model to the time series ... */
/* Include mean in the model */
Integer mn = 1;
/* Use the following maaximum number of iterations and tolerance */
Integer maxit = 50;
double tol = 1e-12;
/* Enforce stationary conditions */
Nag_Garch_Stationary_Type stat_opt = Nag_Garch_Stationary_True;
/* Estimate initial values for regression parameters */
Nag_Garch_Est_Initial_Type est_opt = Nag_Garch_Est_Initial_True;
/* Set the number of values to forecast from the fitted model */
Integer nt = 6;
/* ... end of model fitting options */
/* Initialize the error structure */
INIT_FAIL(fail);
printf("nag_tsa_uni_garch_gjr_estim (g13fec) Example Program Results \n\n");
/* Get the length of the state array */
lstate = -1;
nag_rand_init_repeat(genid, subid, seed, lseed, state, &lstate, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_rand_init_repeat (g05kfc).\n%s\n", fail.message);
exit_status = 1;
goto END;
}
/* Derive various amounts */
npar = iq + ip + 1;
tdx = nreg;
tdc = npar + mn + nreg + 1;
/* Calculate the size of the reference vector */
lr = 2 * (iq + ip + 2);
if (!(covar = NAG_ALLOC((npar + mn + nreg + 1) * tdc, double)) ||
!(etm = NAG_ALLOC(num, double)) || !(ht = NAG_ALLOC(num, double)) ||
!(htm = NAG_ALLOC(num, double)) || !(r = NAG_ALLOC(lr, double)) ||
!(state = NAG_ALLOC(lstate, Integer)) ||
!(sc = NAG_ALLOC(npar + mn + nreg + 1, double)) ||
!(se = NAG_ALLOC(npar + mn + nreg + 1, double)) ||
!(theta = NAG_ALLOC(npar + mn + nreg + 1, double)) ||
!(cvar = NAG_ALLOC(nt, double)) || !(x = NAG_ALLOC(num * tdx, double)) ||
!(yt = NAG_ALLOC(num, double))) {
printf("Allocation failure\n");
exit_status = -1;
goto END;
}
/* Initialize the generator to a repeatable sequence */
nag_rand_init_repeat(genid, subid, seed, lseed, state, &lstate, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_rand_init_repeat (g05kfc).\n%s\n", fail.message);
exit_status = 1;
goto END;
}
/* Set up the time dependent exogenous matrix x */
for (i = 0; i < num; ++i) {
fac1 = (double)(i + 1) * 0.01;
X(i, 1) = sin(fac1) * 0.7 + 0.01;
X(i, 0) = fac1 * 0.1 + 0.5;
}
/* Generate a realization of a random GARCH GJR time series and discard it */
fcall = Nag_TRUE;
nag_rand_times_garch_gjr(dist, num, ip, iq, param, gamma, df, ht, yt, fcall,
r, lr, state, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_rand_times_garch_gjr (g05pfc).\n%s\n", fail.message);
exit_status = 1;
goto END;
}
/* Generate a realization of a random GARCH GJR time series to use */
fcall = Nag_FALSE;
nag_rand_times_garch_gjr(dist, num, ip, iq, param, gamma, df, ht, yt, fcall,
r, lr, state, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_rand_times_garch_gjr (g05pfc).\n%s\n", fail.message);
exit_status = 1;
goto END;
}
/* Adjust the randomly generated time series to take into account for the
exogenous matrix x */
for (i = 0; i < num; ++i) {
xterm = 0.0;
for (k = 0; k < nreg; ++k)
xterm += X(i, k) * bx[k];
if (mn == 1)
yt[i] = mean + xterm + yt[i];
else
yt[i] = xterm + yt[i];
}
/* Set initial estimates for the parameters */
for (i = 0; i < npar; ++i)
theta[i] = param[i] * 0.5;
theta[npar] = gamma * 0.5;
if (mn == 1)
theta[npar + 1] = mean * 0.5;
for (i = 0; i < nreg; ++i)
theta[npar + 1 + mn + i] = bx[i] * 0.5;
/* nag_tsa_uni_garch_gjr_estim (g13fec).
* Univariate time series, parameter estimation for an
* asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH
* process
*/
nag_tsa_uni_garch_gjr_estim(yt, x, tdx, num, ip, iq, nreg, mn, theta, se, sc,
covar, tdc, &pht, etm, htm, &lgf, stat_opt,
est_opt, maxit, tol, &fail);
if (fail.code != NE_NOERROR) {
printf("Error from nag_tsa_uni_garch_gjr_estim (g13fec).\n%s\n",
fail.message);
exit_status = 1;
goto END;
}
/* Display the results */
printf(" Parameter estimates Standard errors "
"Correct values\n");
for (j = 0; j < npar; ++j)
printf("%20.4f (%6.4f) %20.4f\n", theta[j], se[j], param[j]);
printf("%20.4f (%6.4f) %20.4f\n", theta[npar], se[npar], gamma);
if (mn)
printf("%20.4f (%6.4f) %20.4f\n", theta[npar + 1], se[npar + 1],
mean);
for (j = 0; j < nreg; ++j)
printf("%20.4f (%6.4f) %20.4f\n", theta[npar + 1 + mn + j],
se[npar + 1 + mn + j], bx[j]);
/* Now forecast nt steps ahead */
gamma = theta[npar];
/* nag_tsa_uni_garch_gjr_forecast (g13ffc).
* Univariate time series, forecast function for an
* asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH
* process
*/
nag_tsa_uni_garch_gjr_forecast(num, nt, ip, iq, theta, gamma, cvar, htm, etm,
&fail);
printf("\n%" NAG_IFMT " step forecast = %8.4f\n", nt, cvar[nt - 1]);
END:
NAG_FREE(covar);
NAG_FREE(etm);
NAG_FREE(ht);
NAG_FREE(htm);
NAG_FREE(sc);
NAG_FREE(se);
NAG_FREE(theta);
NAG_FREE(cvar);
NAG_FREE(x);
NAG_FREE(yt);
NAG_FREE(r);
NAG_FREE(state);
return exit_status;
}