Sim.DiffProc (version 2.6)

bridgesde1d: Simulation of 1-Dim Diffusion Bridge

Description

The (S3) generic function bridgesde1d for simulation of 1-dim diffusion bridge.

Usage

bridgesde1d(N, ...)
## S3 method for class 'default':
bridgesde1d(N = 1000, x0 = 0, y = 0, t0 = 0, T = 1, Dt, 
   drift, diffusion, alpha = 0.5, mu = 0.5, type = c("ito", "str"), 
   method = c("euler", "milstein", "predcorr", "smilstein", "taylor", 
   "heun", "rk1", "rk2", "rk3"), ...)
							  
							  
## S3 method for class 'bridgesde1d':
summary(object, \dots)
## S3 method for class 'bridgesde1d':
time(x, \dots)
## S3 method for class 'bridgesde1d':
plot(x, \dots)
## S3 method for class 'bridgesde1d':
lines(x, \dots)
## S3 method for class 'bridgesde1d':
points(x, \dots)

Arguments

N

number of simulation steps.

x0

initial value of the process at time t0.

y

terminal value of the process at time T.

t0

initial time.

T

final time.

Dt

time step of the simulation (discretization). If it is missing a default $\Delta t = \frac{T-t_{0}}{N}$.

drift

drift coefficient: an expression of two variables t and x.

diffusion

diffusion coefficient: an expression of two variables t and x.

alpha

weight alpha of the predictor-corrector scheme; the default alpha = 0.5.

mu

weight mu of the predictor-corrector scheme; the default mu = 0.5.

type

if type="ito" simulation diffusion bridge of Ito type, else type="str" simulation diffusion bridge of Stratonovich type; the default type="ito".

method

numerical methods of simulation, the default method = "euler"; see snssde1d.

x, object

an object inheriting from class "bridgesde1d".

...

further arguments for (non-default) methods.

Value

• bridgesde1d returns an object inheriting from class "bridgesde1d".
• X
  an invisible ts object.

newcommand

\CRANpkg

href

http://CRAN.R-project.org/package=#1

pkg

#1

Details

The function bridgesde1d returns a trajectory of the diffusion bridge starting at x at time t0 and ending at y at time T. The methods of approximation are classified according to their different properties. Mainly two criteria of optimality are used in the literature: the strong and the weak (orders of) convergence. The method of simulation can be one among: Euler-Maruyama Order 0.5, Milstein Order 1, Milstein Second-Order, Predictor-Corrector method, Ito-Taylor Order 1.5, Heun Order 2 and Runge-Kutta Order 1, 2 and 3. For more details see vignette("SDEs").

References

Bladt, M. and Sorensen, M. (2007). Simple simulation of diffusion bridges with application to likelihood inference for diffusions. Working Paper, University of Copenhagen. Available at http://www.math.ku.dk/~michael/diffusionbridgepreprint.pdf Iacus, S.M. (2008). Simulation and inference for stochastic differential equations: with R examples. Springer-Verlag, New York

See Also

see DBridge in package sde. bridgesde2d and bridgesde3d for 2 and 3-dim.

Examples

## Example 1: Ito Bridge sde
## dX(t) = 2*(1-X(t)) *dt + dW(t)
## x0 = 0 at time t0=0 , and y = 0 at time T=1

f <- expression( 2*(1-x) )
g <- expression( 1 )
X <- bridgesde1d(drift=f,diffusion=g,y=0,N=1000,Dt=0.001)
X
plot(X)

## Example 2: Stratonovich Bridge sde
## dX(t) = 2*(1-X(t)) *dt + x o dW(t)
## x0 = 0 at time t0=0 , and y = 0 at time T=1

f <- expression( 2*(1-x) )
g <- expression( x )
X <- bridgesde1d(drift=f,diffusion=g,y=0,N=1000,Dt=0.001,type="str")
X
plot(X)