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Sim.DiffProc (version 2.0)

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 for example modelling and simulation of dispersion in shallow water using the attractive center (K.BOUKHETALA,1996). 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

2.0

License

GPL (>= 2)

Maintainer

BOUKHETALA Kamal

Last Published

February 13th, 2011

Functions in Sim.DiffProc (2.0)

Properties of the stochastic integral and Ito Process [5]

Adjustment By Log Normal Distribution

Simulation The First Passage Time FPT For A Simulated Diffusion Process

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

Properties of the stochastic integral and Ito Process [2]

Adjustment By Student t Distribution

Properties of the stochastic integral and Ito Process [4]

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

Creating Flow of Brownian Motion (by the Normal Distribution)

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

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

Evolution a Telegraphic Process in Time

Adjustment the Empirical Distribution of Random Variable X

Sim.DiffProc-package

Simulation of Diffusion Processes.

Creating Flow of Ornstein-Uhlenbeck Process

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

Creating Stochastic Process The Gamma Distribution

Calculating the Empirical Distribution of Random Variable X

Creating Flow of Brownian Motion (by a Random Walk)

Adjustment By Exponential Distribution

Predictor-Corrector Method For Two-Dimensional SDE

Kolmogorov-Smirnov Tests (Student t Distribution)

Creating Brownian Bridge Model

Creating Flow of The Arithmetic Brownian Motion Model

Adjustment By Gamma Distribution

Creating Pearson Diffusions Process (by Milstein Scheme)

Stratonovitch Integral [1]

Empirical Covariance for Brownian Motion

test_ks_dlognorm

Kolmogorov-Smirnov Tests (Log Normal Distribution)

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

Kolmogorov-Smirnov Tests (Chi-Squared Distribution)

Kolmogorov-Smirnov Tests (Gamma Distribution)

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

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

Observation of Ornstein-Uhlenbeck Process

Observation of Arithmetic Brownian Motion

Kernel Density of Random Variable X

test_ks_dweibull

Kolmogorov-Smirnov Tests (Weibull Distribution)

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

Stratonovitch Integral [3]

Creating Geometric Brownian Motion (GBM) Models

Kolmogorov-Smirnov Tests (Exponential Distribution)

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

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

Creating Brownian Motion Model (by the Normal Distribution)

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

Creating Stochastic Process The Student Distribution

Adjustment the Density of Random Variable by Kernel Methods

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

Creating Arithmetic Brownian Motion Model

Creating Flow of Geometric Brownian Motion Models

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

Histograms of Random Variable X

Brownian Motion Property (Invariance by reversal of time)

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

Creating The General Hyperbolic Diffusion (by Milstein Scheme)

Observation of Geometric Brownian Motion Model

Creating Flow of Brownian Bridge Model

Realization a Telegraphic Process

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

Adjustment By Weibull Distribution

Display a Data Frame in a Tk Text Widget

Brownian Motion Property (Invariance by scaling)

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

Stratonovitch Integral [2]

Adjustment By Normal Distribution

Stratonovitch Integral [4]

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

Creating Bessel process (by Milstein Scheme)

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

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

Adjustment By Beta Distribution

Adjustment By F Distribution

Creating Double-Well Potential Model (by Milstein Scheme)

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

Creating White Noise Gaussian

Creating Diffusion Bridge Models (by Euler Scheme)

Creating The Jacobi Diffusion Process (by Milstein Scheme)

Creating The Exponential Martingales Process

Creating Random Walk

Kolmogorov-Smirnov Tests (Normal Distribution)

Properties of the stochastic integral and Ito Process [1]

Creating Brownian Motion Model (by a Random Walk)

Creating Ornstein-Uhlenbeck Process

Kolmogorov-Smirnov Tests (Beta Distribution)

Creating Radial Ornstein-Uhlenbeck Process (by Milstein Scheme)

Kolmogorov-Smirnov Tests (F Distribution)

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

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

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

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

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

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

Properties of the stochastic integral and Ito Process [3]

Adjustment the Density of Random Variable X by Histograms Methods

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

Numerical Solution of Two-Dimensional SDE

Numerical Solution of One-Dimensional SDE

Creating The Hyperbolic Process (by Milstein Scheme)

Adjustment By Chi-Squared Distribution

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

Parametric Estimation of Arithmetic Brownian Motion(Exact likelihood inference)

Parametric Estimation of Ornstein-Uhlenbeck Model (Explicit Estimators)

Predictor-Corrector Method For One-Dimensional SDE

Brownian Motion Property