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Finance[PoissonProcess] - create new Poisson process
Calling Sequence
PoissonProcess(lambda)
PoissonProcess(lambda, X)
Parameters
lambda
-
algebraic expression; intensity parameter
X
algebraic expression; jump size distribution
Description
A Poisson process with intensity parameter , where is a deterministic function of time, is a stochastic process with independent increments such that and
for all . If the intensity parameter itself is stochastic, the corresponding process is called a doubly stochastic Poisson process or Cox process.
A compound Poisson process is a stochastic process of the form , where is a standard Poisson process and are independent and identically distributed random variables. A compound Cox process is defined in a similar way.
The parameter lambda is the intensity. It can be constant or time-dependent. It can also be a function of other stochastic variables, in which case the so-called doubly stochastic Poisson process (or Cox process) will be created.
The parameter X is the jump size distribution. The value of X can be a distribution, a random variable or any algebraic expression involving random variables.
If called with one parameter, the PoissonProcess command creates a standard Poisson or Cox process with the specified intensity parameter.
Compatibility
The Finance[PoissonProcess] command was introduced in Maple 15.
For more information on Maple 15 changes, see Updates in Maple 15.
Examples
Create a subordinated Wiener process with as a subordinator.
Next define a compound Poisson process.
Compute the expected value of for and verify that this is approximately equal to times the expected value of .
Here is an example of a doubly stochastic Poisson process for which the intensity parameter evolves as a square-root diffusion.
See Also
Finance[BlackScholesProcess], Finance[CEVProcess], Finance[Diffusion], Finance[Drift], Finance[ExpectedValue], Finance[GeometricBrownianMotion], Finance[ItoProcess], Finance[PathPlot], Finance[SamplePath], Finance[SampleValues], Finance[StochasticProcesses], Finance[WienerProcess]
References
Glasserman, P., Monte Carlo Methods in Financial Engineering. New York: Springer-Verlag, 2004.
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