First create a standard Wiener process and generate replications of the sample path and plot the result.
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Define another stochastic variable as an expression involving . You can compute the expected value of using Monte Carlo simulation with the specified number of replications of the sample path.
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Define another stochastic variable , which also depends on but uses symbolic coefficients. Note that is an Ito process, so it is governed by the stochastic differential equation (SDE) . You can use the Drift and Diffusion commands to compute and .
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Create a subordinated Wiener process that uses a Poisson process with intensity parameter as subordinator.
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Here is a representation of the Ornstein-Uhlenbeck process in terms of or a subordinated Wiener process. In this case the subordinator is a deterministic process that can be specified as a Maple procedure or an algebraic expression.
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