Finance
WienerProcess
create new Wiener process
Calling Sequence
Parameters
Description
Examples
Compatibility
WienerProcess(J)
WienerProcess(Sigma)
J
-
(optional) stochastic process with non-negative increments, or a deterministic function of time; subordinator
Sigma
Matrix; covariance matrix
The WienerProcess command creates a new Wiener process. If called with no arguments, the WienerProcess command creates a new standard Wiener process, W⁡t, that is a Gaussian process with independent increments such that W⁡0=0 with probability 1, E⁡W⁡t=0 and Var⁡W⁡t−W⁡s=t−s for all 0≤s≤t.
The WienerProcess(Sigma) calling sequence creates a Wiener process with covariance matrix Sigma. The matrix Sigma must be a positive semi-definite square matrix. The dimension of the generated process will be equal to the dimension of the matrix Sigma.
If an optional parameter J is passed, the WienerProcess command creates a process of the form W⁡J⁡t, where W⁡t is the standard Wiener process. Note that the subordinator J⁡t must be an increasing process with non-negative, homogeneous, and independent increments. This can be either another stochastic process such as a Poisson process or a Gamma process, a procedure, or an algebraic expression.
with⁡Finance:
First create a standard Wiener process and generate 50 replications of the sample path and plot the result.
W≔WienerProcess⁡:
P≔PathPlot⁡W⁡t,t=0..3,timesteps=50,replications=20,thickness=3,color=red,axes=BOXED,gridlines=true:P
Define another stochastic variable as an expression involving W1. You can compute the expected value of X⁡3 using Monte Carlo simulation with the specified number of replications of the sample path.
T≔3
ExpectedValue⁡exp⁡0.05⁢T+0.3⁢W⁡T,replications=105
value=1.331151006,standarderror=0.002335223363
Define another stochastic variable Y, which also depends on W1 but uses symbolic coefficients. Note that Y is an Ito process, so it is governed by the stochastic differential equation (SDE) dY⁡t=μ⁡Y⁡t,t⁢dt+σ⁡Y⁡t,t⁢dW⁡t. You can use the Drift and Diffusion commands to compute μ and σ.
Y≔t↦exp⁡μ⋅t+σ⋅W⁡t:Y⁡t
ⅇμ⁢t+σ⁢_W⁡t
subs⁡Y⁡t=Y⁡t,Drift⁡Y⁡t
μ⁢Y⁡t+σ2⁢Y⁡t2
subs⁡Y⁡t=Y⁡t,Diffusion⁡Y⁡t
σ⁢Y⁡t
Create a subordinated Wiener process that uses a Poisson process with intensity parameter λ=0.3 as subordinator.
J≔PoissonProcess⁡0.3
J≔_P
W2≔WienerProcess⁡J
W2≔_W0
P2≔PathPlot⁡W2⁡t,t=0..3,timesteps=50,replications=20,thickness=3,color=blue,axes=BOXED,gridlines=true:P2
plotsdisplay⁡P,P2
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.
κ≔0.1:σ≔0.3:θ≔0.5:R0≔0.02:
τ≔exp⁡2⁢κ⁢t−12⁢κ
τ≔5.000000000⁢ⅇ0.2⁢t−5.000000000
W3≔WienerProcess⁡τ
W3≔_W1
R≔t↦R0⋅exp⁡−κ⋅t+θ⋅1−exp⁡−κ⋅t+σ⋅exp⁡−κ⋅t⋅W3⁡t:R⁡t
−0.48⁢ⅇ−0.1⁢t+0.5+0.3⁢ⅇ−0.1⁢t⁢_W1⁡t
P3≔PathPlot⁡R⁡t,t=0..3,timesteps=50,replications=20,thickness=3,color=blue,axes=BOXED,gridlines=true:P3
ExpectedValue⁡R⁡3,timesteps=100,replications=105
value=0.1517355448,standarderror=0.009505937484
The Finance[WienerProcess] command was introduced in Maple 15.
For more information on Maple 15 changes, see Updates in Maple 15.
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]
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