Hi all,

I would like to thank everybody that gave an answer to my question last
week. It helped really a lot and I found the book "Financial modelling with
jump processes" by Cont & Taknov that you all recommended just excellent!

Now, I would like to bother you once agian with the simultion of the
compound Poisson process that I already mentioned. I am simulating such a
process as described by Cont & Tankov with certain parameter values - let's
say I have the intensity parameter lambda = 0.001 and the jump magnitudes
are Beta distributed with parameters (133.16, 1) . Since I am simulating log
returns, the data set looks like this:  0, 0, 0, 0, 0, 0.011836677, 0, 0, 0,
0 . . . , 0.002458188, 0, 0, . . . , 0. I use this data set to let a maximum
likelihood algorithm (I use the mle2 function of the bbmle package) to
estimate parameter values best describing the data set and compare the
estimated values with the true parameter values. I got the following results
in this case:

Coefficients:
lambda_u      r_u
0.001000     8.469006

It means that the estimated intensity parameter is lambda = 0.001 and the
Beta distribution has parameters (8.469006, 1).

I let the algorithm run a lot of times and with different values of the
parameters of the Beta distribution. The results that I get are all just
like that. I think, it is too great a difference between the true parameter
values and the estimated ones.

What do you think might be the problem?

Thank you very much in advance!

Cheers,
Zoe

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