How to specify a state space model with cycle in this case? 
I am trying to specify a state space model for the dependent variable from this graph. As you can see, there clearly seems to be cyclical behaviour. Therefore, I tried to specify the following state space model: 
However, I am not sure what I should use for lambda. Does anyone know what this value should approximately be for the data in the plot (the data is monthly)? 
For those who are interested, I am using the following code in EViews 
@SIGNAL SP500EP = mu +  c1 + beta1*x1(-1)^2+ [var = exp(C(1))]
@state mu = mu(-1) + [var = exp(C(2))]
@STATE c1 = c(3)*(@cos(0.00001)*c1(-1) + @sin(0.00001)*c2(-1)) + [var = exp( c(4)*(1 - c(3)^2) ) ]
@STATE c2 = c(3)*(-@sin(0.00001)*c1(-1)+ @sin(0.00001)*c2(-1)) + [var = exp( c(4)*(1 - c(3)^2) ) ]
@state beta1 = beta1(-1)
@param c(1) 3 c(2) 3 c(3) 3 c(4) 3 c(5) 3 c(6) 3 c(7) 3

As you can see I am currently using the value 0.00001 for lambda as this seems to give the best results but I doubt if its logical. If anyone has any other suggestions for state space models that can capture the cycle from the plot, that would also be helpful!
Thank you in advance!
Edit:
Following up on F. Tusell's suggestion, here the periodogram of the data, can anyone tell me what exactly this tells me about what I should pick for lambda?

 A: Instead of guessing its value, you should include $\lambda_c$ in the set of parameters to be estimated by means of some method or rule. For example, you can estimate the parameters by maximum likelihood. Upon the state-space representation of the model, the likelihood function can be evaluated by means of the Kalman filter. The likelihood function can be maximized using a numerical optimization algorithm, the L-BFGS-B optimization algorithm is a good option in this case since it allows setting box-constraints to ensure non-negative variance parameters, $0 \leq \rho \leq 1$ and $0 \leq \lambda_c \leq \pi$.
In this answer you can find some useful references on alternative methods to fit this kind of models.
A: It is not clear to me from your graph neither that you have a prominent cycle nor that there is only one. In addition to what has been already answered, you might want to try a spectral analysis first, to detect hidden periodicities in case there are any. 
At the very least you would gain some insight on suitable initial values if you decide later to estimate $\lambda_c$.
