advanced-algorithmic-trading

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Let us try a MA(2) model:
> amznrt.ma <- arima(amznrt, order=c(0, 0, 2))
> amznrt.ma
Call:
arima(x = amznrt, order = c(0, 0, 2))
Coefficients:
ma1 ma2 intercept
-0.0254 -0.0689 0.0012
s.e. 0.0215 0.0217 0.0005
sigma^2 estimated as 0.0007011: log likelihood = 4801.02, aic = -9594.05
Both of the estimates for the β coefficients are negative. Let us plot the residuals once again,
given in Figure 10.14.
> acf(amznrt.ma$res[-1])
Figure 10.14: Residuals of MA(2) Model Fitted to AMZN Daily Log Prices
We can see that there is almost zero autocorrelation in the first few lags. However, we have
five marginally significant peaks at lags k = 12, k = 16, k = 19, k = 25 and k = 27. This is
suggestive that the MA(2) model is capturing a lot of the autocorrelation, but not all of the
long-memory effects. How about a MA(3) model?
> amznrt.ma <- arima(amznrt, order=c(0, 0, 3))