Learn more about Econometrics Toolbox

Forecasting

Use the model from Estimation to compute forecasts for the nasdaq return series 30 days into the future.

1. Set the forecast horizon to 30 days (one month):

   horizon = 30;  % Define the forecast horizon

2. Call the forecasting engine, garchpred, with the estimated model parameters, coeff, the nasdaq returns, and the forecast horizon:

   [sigmaForecast,meanForecast,sigmaTotal,meanRMSE] = ...
         garchpred(coeff,nasdaq,horizon);

   This call to garchpred returns the following parameters:

   • Forecasts of conditional standard deviations of the residuals (sigmaForecast)

   • Forecasts of the nasdaq returns (meanForecast)

   • Forecasts of the standard deviations of the cumulative holding period nasdaq returns (sigmaTotal)

   • Standard errors associated with forecasts of nasdaq returns (meanRMSE)

Because the return series nasdaq is a vector, all garchpred outputs are vectors. Because garchpred uses iterated conditional expectations to successively update forecasts, all of its outputs have 30 rows. The first row stores the 1-period-ahead forecasts, the second row stores the 2-period-ahead forecasts, and so on. Thus, the last row stores the forecasts at the 30-day horizon.

Estimation

Simulation

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