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Sim.DiffProc (version 2.1)

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

1,107

Version

2.1

License

GPL (>= 2)

Homepage

http://www.r-project.org

Maintainer

BOUKHETALA Kamal

Last Published

November 18th, 2011

Functions in Sim.DiffProc (2.1)

Creating Flow of Brownian Motion (by a Random Walk)

Adjustment By Beta Distribution

Empirical Covariance for Brownian Motion

Sim.DiffProc-package

Simulation of Diffusion Processes.

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

Histograms of Random Variable X

Creating Brownian Bridge Model

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

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

Properties of the stochastic integral and Ito Process [2]

Adjustment By Log Normal Distribution

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

Creating Flow of Brownian Bridge Model

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

Observation of Ornstein-Uhlenbeck Process

Kernel Density of Random Variable X

Properties of the stochastic integral and Ito Process [1]

Simulation The First Passage Time FPT For A Simulated Diffusion Process

Creating Flow of Brownian Motion (by the Normal Distribution)

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

Kolmogorov-Smirnov Tests (F Distribution)

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

Creating Flow of Ornstein-Uhlenbeck Process

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

Adjustment the Empirical Distribution of Random Variable X

Adjustment By Normal Distribution

Creating The General Hyperbolic Diffusion (by Milstein Scheme)

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

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

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

Creating Stochastic Process The Student Distribution

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

Creating Brownian Motion Model (by the Normal Distribution)

Display a Data Frame in a Tk Text Widget

Creating Geometric Brownian Motion (GBM) Models

Creating Pearson Diffusions Process (by Milstein Scheme)

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

Adjustment By Gamma Distribution

Stratonovitch Integral [4]

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

Brownian Motion Property (Invariance by reversal of time)

Creating Random Walk

Creating Radial Ornstein-Uhlenbeck Process (by Milstein Scheme)

Kolmogorov-Smirnov Tests (Exponential Distribution)

Calculating the Empirical Distribution of Random Variable X

Parametric Estimation of Ornstein-Uhlenbeck Model (Explicit Estimators)

Stratonovitch Integral [3]

test_ks_dweibull

Kolmogorov-Smirnov Tests (Weibull Distribution)

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

Creating Double-Well Potential Model (by Milstein Scheme)

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

Approximated Conditional Law a Diffusion Process

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

Properties of the stochastic integral and Ito Process [4]

Kolmogorov-Smirnov Tests (Chi-Squared Distribution)

Adjustment By Exponential Distribution

Parametric Estimation of Arithmetic Brownian Motion(Exact likelihood inference)

Kolmogorov-Smirnov Tests (Student t Distribution)

Evolution a Telegraphic Process in Time

Creating Brownian Motion Model (by a Random Walk)

Kolmogorov-Smirnov Tests (Gamma Distribution)

Adjustment By Weibull Distribution

Creating Bessel process (by Milstein Scheme)

Brownian Motion Property

Properties of the stochastic integral and Ito Process [5]

Creating Ornstein-Uhlenbeck Process

Stratonovitch Integral [2]

test_ks_dlognorm

Kolmogorov-Smirnov Tests (Log Normal Distribution)

Creating Arithmetic Brownian Motion Model

Adjustment the Density of Random Variable by Kernel Methods

Creating The Jacobi Diffusion Process (by Milstein Scheme)

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

Creating Diffusion Bridge Models (by Euler Scheme)

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

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

Numerical Solution of Two-Dimensional SDE

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

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

Predictor-Corrector Method For Three-Dimensional SDE

Creating Stochastic Process The Gamma Distribution

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

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

Adjustment By Student t Distribution

Numerical Solution of Three-Dimensional SDE

Stratonovitch Integral [1]

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

Kolmogorov-Smirnov Tests (Beta Distribution)

Observation of Arithmetic Brownian Motion

Observation of Geometric Brownian Motion Model

Creating The Hyperbolic Process (by Milstein Scheme)

Predictor-Corrector Method For One-Dimensional SDE

Kolmogorov-Smirnov Tests (Normal Distribution)

Numerical Solution of One-Dimensional SDE

Realization a Telegraphic Process

Creating White Noise Gaussian

Predictor-Corrector Method For Two-Dimensional SDE

Creating Flow of The Arithmetic Brownian Motion Model

Adjustment By Chi-Squared Distribution

Creating Flow of Geometric Brownian Motion Models

Adjustment By F Distribution

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

Adjustment the Density of Random Variable X by Histograms Methods

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

Properties of the stochastic integral and Ito Process [3]

Creating The Exponential Martingales Process

Brownian Motion Property (Invariance by scaling)

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

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