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Sim.DiffProc (version 2.2)

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 for example modelling and simulation of dispersion in shallow water using the attractive center (K.BOUKHETALA, 1996). Approximated the evolution of conditional law a diffusion process with three methods Euler, Kessler and Shoji-Ozaki. Simulation and statistical analysis of the first passage time (FPT) and M-samples of the random variable X(v) given by a simulated diffusion process.

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Version

Install

install.packages('Sim.DiffProc')

Monthly Downloads

596

Version

2.2

License

GPL (>= 2)

Homepage

http://www.r-project.org

Maintainer

BOUKHETALA Kamal

Last Published

February 13th, 2012

Functions in Sim.DiffProc (2.2)

Creating Flow of The Arithmetic Brownian Motion Model

Two-Dimensional Attractive Model Model(S >= 2,Sigma)

Creating The Hyperbolic Process (by Milstein Scheme)

Adjustment By Exponential Distribution

Creating Brownian Motion Model (by a Random Walk)

Evolution a Telegraphic Process in Time

Adjustment By Gamma Distribution

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

Empirical Covariance for Brownian Motion

Adjustment By Log Normal Distribution

Properties of the stochastic integral and Ito Process [3]

Adjustment By Chi-Squared Distribution

Brownian Motion Property (trajectories brownian between function (+/-)2*sqrt(C*t))

Adjustment By Student t Distribution

Simulation Two-Dimensional Brownian Motion (by the Normal Distribution)

Stratonovitch Integral [1]

Creating Bessel process (by Milstein Scheme)

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

Adjustment By F Distribution

Stratonovitch Integral [4]

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

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

Stratonovitch Integral [3]

Creating Radial Ornstein-Uhlenbeck Process (by Milstein Scheme)

Observation of Arithmetic Brownian Motion

Approximated Conditional Law a Diffusion Process

Simulation Two-Dimensional Brownian Motion (by a Random Walk)

Creating Flow of Brownian Bridge Model

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

Predictor-Corrector Method For Three-Dimensional SDE

Parametric Estimation of Arithmetic Brownian Motion(Exact likelihood inference)

Three-Dimensional Attractive Model Model(S >= 2,Sigma)

Three-Dimensional Attractive Model Model(S = 1,Sigma)

Three-Dimensional Attractive Model for Two-Diffusion Processes V(1) and V(2)

Adjustment the Density of Random Variable by Kernel Methods

Creating Flow of Geometric Brownian Motion Models

Kolmogorov-Smirnov Tests (Student t Distribution)

Radial Process Model(S >= 2,Sigma) Or Attractive Model

Brownian Motion Property (Invariance by reversal of time)

Predictor-Corrector Method For Two-Dimensional SDE

Creating Arithmetic Brownian Motion Model

Kolmogorov-Smirnov Tests (Gamma Distribution)

Creating The Exponential Martingales Process

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

Adjustment By Weibull Distribution

Creating Brownian Motion Model (by the Normal Distribution)

Creating Pearson Diffusions Process (by Milstein Scheme)

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

Creating The General Hyperbolic Diffusion (by Milstein Scheme)

Two-Dimensional Attractive Model in Polar Coordinates Model(S = 1,Sigma)

Parametric Estimation of Ornstein-Uhlenbeck Model (Explicit Estimators)

Creating Flow of Brownian Motion (by the Normal Distribution)

Creating Stochastic Process The Gamma Distribution

Two-Dimensional Attractive Model in Polar Coordinates Model(S >= 2,Sigma)

Two-Dimensional Attractive Model Model(S = 1,Sigma)

Kolmogorov-Smirnov Tests (F Distribution)

Properties of the stochastic integral and Ito Process [5]

Numerical Solution of One-Dimensional SDE

Creating Flow of Brownian Motion (by a Random Walk)

Simulation The First Passage Time FPT For Attractive Model for Two-Diffusion Processes V(1) and V(2)

Adjustment the Empirical Distribution of Random Variable X

Creating Geometric Brownian Motion (GBM) Models

Kolmogorov-Smirnov Tests (Normal Distribution)

Two-Dimensional Attractive Model for Two-Diffusion Processes V(1) and V(2)

Calculating the Empirical Distribution of Random Variable X

Simulation M-Samples of Random Variable X(v[t]) For A Simulated Diffusion Process

Properties of the stochastic integral and Ito Process [4]

Brownian Motion Property

Simulation Three-Dimensional Brownian Motion (by a Random Walk)

Histograms of Random Variable X

Numerical Solution of Three-Dimensional SDE

Simulation Three-Dimensional Brownian Motion (by the Normal Distribution)

Display a Data Frame in a Tk Text Widget

Adjustment the Density of Random Variable X by Histograms Methods

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

Simulation The First Passage Time FPT For Attractive Model(S >= 2,Sigma)

Kolmogorov-Smirnov Tests (Beta Distribution)

Adjustment By Normal Distribution

Creating Flow of Ornstein-Uhlenbeck Process

Creating The Jacobi Diffusion Process (by Milstein Scheme)

Observation of Ornstein-Uhlenbeck Process

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

test_ks_dweibull

Kolmogorov-Smirnov Tests (Weibull Distribution)

Sim.DiffProc-package

Simulation of Diffusion Processes.

Creating White Noise Gaussian

Simulation The First Passage Time FPT For Attractive Model(S = 1,Sigma)

Properties of the stochastic integral and Ito Process [1]

Creating Ornstein-Uhlenbeck Process

Stratonovitch Integral [2]

Creating Random Walk

Kernel Density of Random Variable X

Realization a Telegraphic Process

Radial Process Model(S = 1,Sigma) Or Attractive Model

test_ks_dlognorm

Kolmogorov-Smirnov Tests (Log Normal Distribution)

Creating Diffusion Bridge Models (by Euler Scheme)

Numerical Solution of Two-Dimensional SDE

Kolmogorov-Smirnov Tests (Chi-Squared Distribution)

Brownian Motion Property (Invariance by scaling)

Creating Double-Well Potential Model (by Milstein Scheme)

Kolmogorov-Smirnov Tests (Exponential Distribution)

Observation of Geometric Brownian Motion Model

Creating Stochastic Process The Student Distribution

Simulation The First Passage Time FPT For A Simulated Diffusion Process

Creating Brownian Bridge Model

Adjustment By Beta Distribution

Predictor-Corrector Method For One-Dimensional SDE

Properties of the stochastic integral and Ito Process [2]

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