Maple für Professional
Maple für Akademiker
Maple für Studenten
Maple Personal Edition
Maple Player
Maple Player für iPad
MapleSim für Professional
MapleSim für Akademiker
Maple T.A. - Testen & beurteilen
Maple T.A. MAA Placement Test Suite
Möbius - Online-Courseware
Machine Design / Industrial Automation
Luft- und Raumfahrt
Fahrzeugtechnik
Robotics
Energiebranche
System Simulation and Analysis
Model development for HIL
Anlagenmodelle für den Regelungsentwurf
Robotics/Motion Control/Mechatronics
Other Application Areas
Mathematikausbildung
Technik
Allgemein- und berufsbildende Schulen
Testen und beurteilen
Studierende
Finanzmodelle
Betriebsforschung
Hochleistungsrechnen
Physik
Live-Webinare
Aufgezeichnete Webinare
Geplante Veranstaltungen
MaplePrimes
Maplesoft-Blog
Maplesoft-Mitgliedschaft
Maple Ambassador Program
MapleCloud
Technische Whitepapers
E-Mail Newsletters
Maple-Bücher
Math Matters
Anwendungs-Center
MapleSim Modell-Galerie
Anwenderberichte
Exploring Engineering Fundamentals
Lehrkonzepte mit Maple
Maplesoft Welcome-Center
Resource-Center für Lehrer
Help-Center für Studierende
Finance[GeometricBrownianMotion] - create new Brownian motion process
Calling Sequence
GeometricBrownianMotion(, mu, sigma, opts)
GeometricBrownianMotion(, mu, sigma, t, opts)
Parameters
-
real constant; initial value
mu
algebraic expression, operator or procedure; drift parameter
sigma
algebraic expression, operator, procedure or a one-dimensional stochastic process; volatility parameter
t
name; time parameter
opts
(optional) equation(s) of the form option = value where option is scheme; specify options for the GeometricBrownianMotion command
Description
The GeometricBrownianMotion command creates a new geometric Brownian motion process. This is a stochastic process , which is governed by the stochastic differential equation (SDE)
where
is the drift parameter
is the volatility parameter
and
is the standard Wiener process.
The parameter defines the initial value of the underlying stochastic process. It must be a real constant.
The parameter mu is the drift. In the simplest case of a constant drift mu is a real number (that is, any expression of type ). Time-dependent drift can be given either as an algebraic expression or as a Maple procedure. If mu is given as an algebraic expression, then the parameter t must be passed to specify which variable in mu should be used as a time variable. A Maple procedure defining a time-dependent drift must accept one parameter (the time) and return the corresponding value for the drift.
The parameter sigma is the volatility. Similar to the drift parameter, the volatility can be constant or time-dependent. Unlike drift, volatility can involve other (one-dimensional) stochastic variables.
The scheme option specifies the discretization scheme used for simulation of this process. By default the standard Euler scheme is used. When scheme is set to unbiased the transition density will be used to simulate a value given . This scheme is appropriate in the case of a time-dependent drift and/or volatility.
In the multi-dimensional case the drift and the volatility parameter must be constant. The drift parameter must be specified as a Vector and the volatility parameter must be a symmetric matrix that defines the covariance between the individual components.
Options
scheme = unbiased or Euler -- This option specifies which discretization scheme should be used for simulating this process.
Compatibility
The Finance[GeometricBrownianMotion] command was introduced in Maple 15.
For more information on Maple 15 changes, see Updates in Maple 15.
Examples
First consider the case of a one-dimensional Brownian motion with constant drift and volatility.
Compute the drift and diffusion for functions of .
Here is an example of a one-dimensional Brownian motion with time-dependent parameters given in algebraic form.
Here is the same example but with drift and volatility given in the form of Maple procedures.
You can simulate values for any path function given as a Maple procedure.
Here are examples involving stochastic volatility.
Here is the same using different discretization schemes. For presentation purposes consider a geometric Brownian motion with very low volatility and time-dependent drift. Compare the simulated results with the corresponding solution of an ordinary (non-stochastic) differential equation.
See Also
Finance[BlackScholesProcess], Finance[BrownianMotion], Finance[CEVProcess], Finance[Diffusion], Finance[Drift], Finance[ExpectedValue], Finance[GeometricBrownianMotion], Finance[ItoProcess], Finance[SamplePath], Finance[SampleValues], Finance[StochasticProcesses], Finance[WienerProcess]
References
Glasserman, P., Monte Carlo Methods in Financial Engineering. New York: Springer-Verlag, 2004.
Hull, J., Options, Futures, and Other Derivatives, 5th. edition. Upper Saddle River, New Jersey: Prentice Hall, 2003.
Download Help Document