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Unformatted text preview: oefficient is significant.
Q=5.09 and Q*=5.26
Compared with a tabulated χ2(5)=11.1 at the 5% level, so the 5 coefficients
are jointly insignificant. 6 Moving Average Processes
• Let ut (t=1,2,3,...) be a sequence of independently and identically
distributed (iid) random variables with E(ut)=0 and Var(ut)= , then
yt = μ + ut + θ1ut1 + θ2ut2 + ... + θqutq
is a qth order moving average model MA(q). • Its properties are
E(yt)=μ; Var(yt) = γ0 = (1+
Covariances )σ2 7 Example of an MA Problem 1. Consider the following MA(2) process:
where ut is a zero mean white noise process with variance .
(i) Calculate the mean and variance of Xt
(ii) Derive the autocorrelation function for this process (i.e. express the
autocorrelations, τ1, τ2, ... as functions of parameters θ1 and θ2).
(iii) If θ1 = 0.5 and θ2 = 0.25, sketch the ACF of Xt. 8 Solution
(i) If E(ut)=0, then E(uti)=0 ∀ i.
So
E(Xt) = E(ut + θ1ut1+ θ2ut2)= E(ut)+ θ1E(ut1)+ θ2E(ut2)=0
Var(Xt)
but E(Xt)
Var(Xt) = E[XtE(Xt)][XtE(Xt)]
= 0, so
= E[(Xt)(Xt)]
= E[(ut + θ1ut1+ θ2ut2)(ut + θ1ut1+ θ2ut2)]
= E[
+crossproducts] But E[crossproducts]=0 since Cov(ut,uts)=0 for s≠0.
9 Solution (cont’d)
So Var(Xt) = γ0= E [
=
= (ii) The ACF of Xt.
γ1
= E[XtE(Xt)][Xt1E(Xt1)]
= E[Xt][Xt1]
= E[(ut +θ1ut1+ θ2ut2)(ut1 + θ1ut2+ θ2ut3)]
= E[(
)]
=
= 10 Solution (cont’d)
γ2 = E[XtE(Xt)][Xt2E(Xt2)]
= E[Xt][Xt2]
= E[(ut + θ1ut1+θ2ut2)(ut2 +θ1ut3+θ2ut4)]
= E[(
)]
= γ3 = E[XtE(Xt)][Xt3E(Xt3)]
= E[Xt][Xt3]
= E[(ut +θ1ut1+θ2ut2)(ut3 +θ1ut4+θ2ut5)]
=0 So γs = 0 for s > 2.
11 Solution (cont’d)
We have the autocovariances, now calculate the autocorrelations: (iii) For θ1 = 0.5 and θ2 = 0.25, substituting these into the formulae above
gives τ1 = 0.476, τ2 = 0.190.
12 ACF Plot
Thus the ACF plot will appear as follows: 13 Autoregressive Processes
• An autoregressive model of order p, an AR(p) can be expressed as • Or using the lag operator notation:
Lyt = yt1
Liyt = yti • or
or where . 14 The Stationary Condition for an AR Model •
•
• • The condition for stationarity of a general AR(p) model is that the
roots of
all lie outside the unit circle.
A stationary AR(p) model is required for it to have an MA(∞)
representation.
Example 1: Is yt = yt1 + ut stationary?
The characteristic root is 1, so it is a unit root process (so nonstationary)
Example 2: Is yt = 3yt1  0.25yt2 + 0.75yt3 +ut stationary?
The characteristic roots are 1, 2/3, and 2. Since only one of these lies
outside the unit circle, the process is nonstationary. 15 Wold’s Decomposition Theorem • States that any stationary series can be decomposed into the sum of two
unrelated processes, a purely deterministic part and a purely stochastic
part, which will be an MA(∞). • For the AR(p) model,
decomposition is , ignoring the intercept, the Wold where, 16 The Moments of an Autoregressive Process • The moments of an autoregressive process are as follo...
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This note was uploaded on 09/20/2013 for the course FINA 5170 taught by Professor Janebargers during the Summer '13 term at Greenwich School of Management.
 Summer '13
 JaneBargers
 Financial Markets

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