Load the Data

Load the NASDAQ data included with the toolbox. Convert the daily close composite index series to a percentage return series.

load Data_EquityIdx
nasdaq = DataTable.NASDAQ;
r = 100*price2ret(nasdaq);
T = length(r);

figure
plot(r)
xlim([0 T])
title('NASDAQ Daily Returns')

The returns appear to fluctuate around a constant level, but exhibit volatility clustering. Large changes in the returns tend to cluster together, and small changes tend to cluster together. That is, the series exhibits conditional heteroscedasticity.

The returns are of relatively high frequency. Therefore, the daily changes can be small. For numerical stability, it is good practice to scale such data.

Check for Autocorrelation

Plot the sample autocorrelation function (ACF) and partial autocorrelation function (PACF) for the return series.

figure
subplot(2,1,1)
autocorr(r)
subplot(2,1,2)
parcorr(r)

The autocorrelation functions suggests there is significant autocorrelation at lag one.

Test the Significance of Autocorrelations

Conduct a Ljung-Box Q-test at lag 5.

[h,p] = lbqtest(r,'Lags',5)

h = logical
   1

p = 0.0120

The null hypothesis that all autocorrelations are 0 up to lag 5 is rejected (h = 1).

Check for Conditional Heteroscedasticity.

Plot the sample ACF and PACF of the squared return series.

figure
subplot(2,1,1)
autocorr(r.^2)
subplot(2,1,2)
parcorr(r.^2)

The autocorrelation functions show significant serial dependence, which suggests that the series is conditionally heteroscedastic.

Test for Significant ARCH Effects

Conduct an Engle's ARCH test. Test the null hypothesis of no conditional heteroscedasticity against the alternative hypothesis of an ARCH model with two lags (which is locally equivalent to a GARCH(1,1) model).

[h,p] = archtest(r-mean(r),'lags',2)

h = logical
   1

p = 0

The null hypothesis is rejected in favor of the alternative hypothesis (h = 1).

Specify a Conditional Mean and Variance Model.

Specify an AR(1) model for the conditional mean of the NASDAQ returns, and a GARCH(1,1) model for the conditional variance. This is a model of the form

where $${\epsilon }_t={\sigma }_t{z}_t$$,

and $$z_t$$ is an independent and identically distributed standardized Gaussian process.

Mdl = arima('ARLags',1,'Variance',garch(1,1))

Mdl = 
  arima with properties:

     Description: "ARIMA(1,0,0) Model (Gaussian Distribution)"
    Distribution: Name = "Gaussian"
               P: 1
               D: 0
               Q: 0
        Constant: NaN
              AR: {NaN} at lag [1]
             SAR: {}
              MA: {}
             SMA: {}
     Seasonality: 0
            Beta: [1×0]
        Variance: [GARCH(1,1) Model]

The model output shows that a garch model is stored in the Variance property of the arima model, Mdl.