advanced-algorithmic-trading

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> plot(x)
The output of our ARMA(2,2) model is given in Figure 10.21.
Figure 10.21: Realisation of an ARMA(2,2) Model, with α 1 = 0.5, α 2 = −0.25, β 1 = 0.5 and
β 2 = −0.3
And the corresponding autocorelation, as given in Figure 10.22.
> acf(x)
We can now try fitting an ARMA(2,2) model to the data:
> arima(x, order=c(2, 0, 2))
Call:
arima(x = x, order = c(2, 0, 2))
Coefficients:
ar1 ar2 ma1 ma2 intercept
0.6529 -0.2291 0.3191 -0.5522 -0.0290
s.e. 0.0802 0.0346 0.0792 0.0771 0.0434
sigma^2 estimated as 1.06: log likelihood = -1449.16, aic = 2910.32
We can also calculate the confidence intervals for each parameter:
> 0.653 + c(-1.96, 1.96)*0.0802
[1] 0.495808 0.810192
> -0.229 + c(-1.96, 1.96)*0.0346