Quadratic Variation of a Poisson Process?

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Discussion Overview

The discussion revolves around the concept of quadratic variation in the context of a Poisson process. Participants explore the mathematical formulation and implications of calculating the quadratic variation, addressing both theoretical and practical aspects of the problem.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant expresses confusion about finding the quadratic variation of a Poisson process and mentions difficulties in applying the general formula.
  • Another participant suggests that the quadratic variation is the sum of squared differences of the Poisson process increments and discusses the limit as the partition size approaches zero.
  • A question is raised regarding whether the limit should be taken over the maximum time interval or the increments of the process, indicating potential misunderstanding of the definitions involved.
  • One participant asserts that the quadratic variation of a Poisson process equals the process itself, citing that it is a pure jump process with jumps of size one.
  • Another participant adds that the distribution of the process is irrelevant to the calculation of quadratic variation, emphasizing the piecewise constant nature of the process.

Areas of Agreement / Disagreement

There is no consensus on the correct approach to calculating the quadratic variation, with multiple competing views and interpretations of the definitions and limits involved. Some participants agree on the nature of the Poisson process as a pure jump process, while others question the application of the quadratic variation formula.

Contextual Notes

Participants express uncertainty regarding the correct interpretation of limits and the implications of the Poisson process's properties on the calculation of quadratic variation. There are unresolved mathematical steps and definitions that contribute to the ongoing debate.

RedZone2k2
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Hey guys,

This is my first post on PhysicsForums; my friend said that this was the best place to ask questions about math.

Anyways, I have to find the Quadratic Variation of a Poisson Process.

My professor doesn't have a class textbook (just some notes that he's found online), and although I can find a general formula for quadratic variation, I can't seem to plug it in, and get a non-zero answer. My professor said that this question should be pretty easy, but I'm totally lost.

If you guys could offer any help, that would be great. If you guys have any more questions, let me know!

Thanks!
 
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So here is what I have so far:

The quadratic variation is the sum of all [N_t_(i+1)-N_t_i]^2, with the max |N_t_(i+1)-N_t_i| --> 0.

Since Poisson distributions are independent, and we want to find N(t), partition the interval [0,t] to n subintervals. Let h = max |N_t_(i+1)-N_t_i|. So, since I'm taking the limit as h becomes arbitrarily tiny, I can rewrite this sum as n*(E(N_h))^2 right? But then, I get n*(lam*(1\n))^2, which goes to zero. This can't be right, so I must have made at least one error here right?
 
First, are you trying to find the expected value of the quadratic variation? Second, are you sure it's not max ti+1-ti --> 0. In other words, the largest subinterval of time goes to zero? If it is N(ti+1) - N(ti) --> 0, then yeah, it looks like the answer would have to be zero.

I am not familiar with the term quadratic variation, but I would guess that it is supposed to result in a Riemann sum, or integral.
 
A Poisson process has quadratic variation equal to itself.

This is because it is a pure jump process with jump sizes equal to 1. If Nt is the Poisson process then the only contribution to the quadratic variation [N] comes from the jumps,

<br /> [N]_t=\sum_{s\le t} \Delta N_s^2 = \sum_{s\le t,\Delta N_s\not=0}1 = \sum_{s\le t}\Delta N_s=N_t.<br />
 
Just to add - the distribution of the process doesn't matter. Once you know that it is piecewise constant then you can conclude that the quadratic variation is just the sum of the squares of the jumps, which is easily calculated as I did above.
 
That makes a lot of sense. Thank you!
 

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