Characteristic Function of a Compound Poisson Process

In summary, the conversation discusses the process of finding the characteristic function of a Compound Poisson Process, which involves using the moment generating function and combining the Normal and Poisson distributions. The final step is evaluating expressions using the rate of the Poisson process.
  • #1
mikhairu
2
0
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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  • #2
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.
 

Related to Characteristic Function of a Compound Poisson Process

1. What is a Compound Poisson Process?

A Compound Poisson Process is a type of stochastic process where the arrival of events follows a Poisson distribution, and each event has a random magnitude or size associated with it. This process is commonly used to model the behavior of a system where the occurrence of events is random and the magnitude of each event varies, such as in financial markets or insurance claims.

2. What is the Characteristic Function of a Compound Poisson Process?

The Characteristic Function of a Compound Poisson Process is a mathematical function that describes the probability distribution of the process. It is defined as the expected value of the complex exponential of the random variable associated with the process. This function is useful for analyzing the behavior of the process and making predictions about future events.

3. How is the Characteristic Function of a Compound Poisson Process calculated?

The Characteristic Function of a Compound Poisson Process can be calculated using the properties of the Poisson distribution and the law of total probability. It involves summing the probabilities of all possible outcomes of the process, each multiplied by their respective complex exponential value. This calculation can be done analytically or numerically using software or programming languages.

4. What are the main properties of the Characteristic Function of a Compound Poisson Process?

The main properties of the Characteristic Function of a Compound Poisson Process include its ability to fully describe the probability distribution of the process, its dependence on the parameters of the Poisson distribution and the random variable associated with the process, and its usefulness in making predictions about the behavior of the process.

5. How is the Characteristic Function of a Compound Poisson Process used in practice?

The Characteristic Function of a Compound Poisson Process is used in various fields, including finance, insurance, and engineering, to model and analyze random events and make predictions about their behavior. It is also used for risk management and decision-making in these fields, as well as for developing mathematical models for real-world systems with random events and varying magnitudes.

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