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Sim.DiffProc (version 1.0)

Simulation of Diffusion Processes

Description

Simulation of diffusion processes and numerical solution of stochastic differential equations. Analysis of discrete-time approximations for stochastic differential equations (SDE) driven by Wiener processes,in financial and actuarial modeling and other areas of application.

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Version

Install

install.packages('Sim.DiffProc')

Monthly Downloads

1,107

Version

1.0

License

GPL

Maintainer

Arsalane Guidoum

Last Published

January 4th, 2011

Functions in Sim.DiffProc (1.0)

Parametric Estimation of Arithmetic Brownian Motion(Exact likelihood inference)

Creating Flow of Brownian Bridge Model

Creating Geometric Brownian Motion (GBM) Models

Properties of the stochastic integral and Ito Process [1]

Creating Arithmetic Brownian Motion Model

Creating Flow of Brownian Motion (by the Normal Distribution)

Stratonovitch Integral [3]

Creating The modified CIR and hyperbolic Process (by Milstein Scheme)

Creating The Exponential Martingales Process

Creating The General Hyperbolic Diffusion (by Milstein Scheme)

Brownian Motion Property (trajectories brownian lies between the two curves (+/-)2*sqrt(C*t))

Brownian Motion Property (Invariance by reversal of time)

Empirical Covariance for Brownian Motion

Properties of the stochastic integral and Ito Process [5]

Sim.DiffProc-package

Simulation of Diffusion Processes.

Creating Radial Ornstein-Uhlenbeck Process (by Milstein Scheme)

Parametric Estimation of Hull-White/Vasicek (HWV) Gaussian Diffusion Models(Exact likelihood inference)

Creating Flow of Hull-White/Vasicek (HWV) Gaussian Diffusion Models

Creating Pearson Diffusions Process (by Milstein Scheme)

Observation of Ornstein-Uhlenbeck Process

Stratonovitch Integral [2]

Creating The Chan-Karolyi-Longstaff-Sanders (CKLS) family of models (by Milstein Scheme)

Properties of the stochastic integral and Ito Process [4]

Properties of the stochastic integral and Ito Process [2]

Creating Flow of The Arithmetic Brownian Motion Model

Evolution a Telegraphic Process in Time

Creating Bessel process (by Milstein Scheme)

Parametric Estimation of Model Black-Scholes (Exact likelihood inference)

Parametric Estimation of Ornstein-Uhlenbeck Model (Explicit Estimators)

Simulation Numerical Solution of Stochastic Differential Equation

Creating Double-Well Potential Model (by Milstein Scheme)

Brownian Motion Property (Invariance by scaling)

Properties of the stochastic integral and Ito Process [3]

Observation of Geometric Brownian Motion Model

Observation of Arithmetic Brownian Motion

Creating Brownian Bridge Model

Realization a Telegraphic Process

Creating Flow of Ornstein-Uhlenbeck Process

Stratonovitch Integral [1]

Display a Data Frame in a Tk Text Widget

Creating Hull-White/Vasicek (HWV) Gaussian Diffusion Models

Creating White Noise Gaussian

Creating Random Walk

Creating Ornstein-Uhlenbeck Process

Stratonovitch Integral [4]

Creating The Hyperbolic Process (by Milstein Scheme)

Creating Stochastic Process The Gamma Distribution

Creating Brownian Motion Model (by a Random Walk)

Creating The Jacobi Diffusion Process (by Milstein Scheme)

Creating Flow of Brownian Motion (by a Random Walk)

Creating Cox-Ingersoll-Ross (CIR) Square Root Diffusion Models (by Milstein Scheme)

Creating Stochastic Process The Student Distribution

Creating Brownian Motion Model (by the Normal Distribution)

Creating Flow of Geometric Brownian Motion Models

Parametric Estimation of Ornstein-Uhlenbeck Model (Exact likelihood inference)

Brownian Motion Property

Creating Constant Elasticity of Variance (CEV) Models (by Milstein Scheme)

Predictor-Corrector Method

Creating Diffusion Bridge Models (by Euler Scheme)

Creating Ahn and Gao model or Inverse of Feller Square Root Models (by Milstein Scheme)