Characteristic Function of a Compound Poisson Process

AI Thread Summary
The discussion revolves around finding the characteristic function (CF) of a Compound Poisson Process (CPP) defined as X(t) = ΣYj, where Yj are independent normally distributed variables. The user derives the CF using expectations and moment generating functions, reaching a point where they need assistance in combining the normal distribution with the Poisson process. A suggestion is made to evaluate expressions of the form E[z^N] using the Poisson distribution formula for N. The conversation highlights the complexity of integrating the normal distribution with the characteristics of a Poisson process. The thread concludes with a reference to a more comprehensive answer on the topic.
mikhairu
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Hello,

I am trying to find a characteristic function (CF) of a Compound Poisson Process (CPP) and I am stuck :(.

I have a CPP defined as X(t) = SIGMA[from j=1 to Nt]{Yj}. Yj's are independent and are Normally distributed.

So, in trying to find the CF of X I do the following:
(Notation: CFy = Characteristic Function of y (y is subscript))

CF(X) = E[exp{i*u*X}]
= E[ (E[exp{i*u*Y}])^N ]
= E[ (CFy(u))^N ]
= E[ (exp{ ln[CFy(u)] })^N ]
which is really just a moment generating function Phi_N (Phi subscript N):
Phi_N( ln[CFy(u)] ).

I don't know how to go from here... Y's are Normally distributed but the entire process is Poisson.. so I'm not sure how to combine these. Please help!

Thank you.
 
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mikhairu said:
Hello,

I am trying to find a characteristic function (CF) of a Compound Poisson Process (CPP) and I am stuck :(.

I have a CPP defined as X(t) = SIGMA[from j=1 to Nt]{Yj}. Yj's are independent and are Normally distributed.

So, in trying to find the CF of X I do the following:
(Notation: CFy = Characteristic Function of y (y is subscript))

CF(X) = E[exp{i*u*X}]
= E[ (E[exp{i*u*Y}])^N ]
= E[ (CFy(u))^N ]
= E[ (exp{ ln[CFy(u)] })^N ]
which is really just a moment generating function Phi_N (Phi subscript N):
Phi_N( ln[CFy(u)] ).

I don't know how to go from here... Y's are Normally distributed but the entire process is Poisson.. so I'm not sure how to combine these. Please help!

Thank you.

After line 3 it should be possible to evaluate expressions of the form E[z^N] using P[N=n]=exp(-L*t)*(L*t)^n/n! where L is the rate of the Poisson process.
 
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