# Parametric Estimation of 1-D Stochastic Differential Equation

#### A.C. Guidoum1 and K. Boukhetala2

#### 2020-05-04

# The `fitsde()` function

The Sim.DiffProc package implements pseudo-maximum likelihood via the `fitsde()` function. The interface and the output of the `fitsde()` function are made as similar as possible to those of the standard `mle` function in the `stats4` package of the basic R system. The main arguments to `fitsde` consist:

- `data` a univariate time series (`ts` object).
- `drift` and `diffusion` indicate drift and diffusion coefficient of the SDE, is an `expression` of two variables `t`, `x` and `theta` names of the parameters, and must be nominated by a vector of `theta = (theta[1], theta[2],..., theta[p])` for reasons of symbolic derived in approximation methods.
- `start` must be specified as a named list, where the names of the elements of the list correspond to the names of the parameters as they appear in the `drift` and `diffusion` coefficient.
- The `pmle` argument must be a `character` string specifying the method to use, can be either: `"euler"` Euler pseudo-likelihood, `"ozaki"` Ozaki pseudo-likelihood, `"shoji"` Shoji pseudo-likelihood and `"kessler"` Kessler pseudo-likelihood.
- `optim.method` select the optimization method (`"L-BFGS-B"` is used by default), and further arguments to pass to `optim` function.
- `lower` and `upper` bounds on the variables for the `Brent` or `L-BFGS-B` method.

The functions of type `S3 method` (as similar of the standard `mle` function in the `stats4` package of the basic R system for the class `fitsde` are the following:

- `coef`: which extracts model coefficients from objects returned by `fitsde`.
- `vcov`: returns the variance-covariance matrix of the parameters of a fitted model objects.
- `logLik`: extract log-likelihood.
- `AIC`: calculating Akaike’s Information Criterion for fitted model objects.
- `BIC`: calculating Schwarz’s Bayesian Criterion for fitted model objects.
- `confint`: computes confidence intervals for one or more parameters in a fitted model objects.

The following we explain how to use this function to estimate a SDE with different approximation methods.

## Euler method

Consider a process solution of the general stochastic differential equation: \[\begin{equation}\label{eq02} dX_{t}= f(X_{t},\underline{\theta}) dt + g(X_{t},\underline{\theta}) dW_{t}, \quad \quad t \geq 0 \, , X_{0} = x_{0}, \end{equation}\] The Euler scheme produces the discretization (\(\Delta t \rightarrow 0\)): \[\begin{equation*} X_{t+\Delta t} - X_{t} = f(X_{t},\theta) \Delta t+ g(X_{t},\theta) (W_{t+\Delta t} -W_{t}), \end{equation*}\] The increments \(X_{t+\Delta t} - X_{t}\) are then independent Gaussian random variables with mean: \(\text{E}_{x} = f(X_{t},\theta)\Delta t\), and variance: \(\text{V}_{x} = g^{2}(X_{t},\theta) \Delta t\). Therefore the transition density of the process can be written as: \[\begin{equation*} p_{\theta}(t,y|x)=\frac{1}{\sqrt{2\pi t g^{2}(x,\theta)}} \exp\left(-\frac{\left(y-x-f(x,\theta)t\right)^2}{2tg^{2}(x,\theta)}\right) \end{equation*}\] Then the log-likelihood is: \[\begin{equation}\label{eq08} h_{n}(\theta|X_{1},X_{2},\dots,X_{n})=-\frac{1}{2}\left(\sum_{i=1}^{n} \frac{(X_{i}-X_{i-1}-f(X_{i-1},\theta)\Delta)^2}{\sigma^2 \Delta t} + n \log(2\pi \sigma^2 \Delta t)\right) \end{equation}\] As an example, we consider the Chan-Karolyi-Longstaff-Sanders (CKLS) model: \[\begin{equation}\label{eq09} dX_{t} = (\theta_{1}+\theta_{2} X_{t}) dt + \theta_{3} X_{t}^{\theta_{4}} dW_{t},\qquad X_{0}=2 \end{equation}\]

with \(\theta_{1}=1\), \(\theta_{2}=2\), \(\theta_{3}=0.5\) and \(\theta_{4}=0.3\). Before calling `fitsde`, we generate sampled data \(X_{t_{i}}\), with \(\Delta t =10^{-4}\), as following:

    R> f <- expression( (1+2*x) ) ; g <- expression( 0.5*x^0.3 )
    R> sim    <- snssde1d(drift=f,diffusion=g,x0=2,N=10^4,Dt=10^-4)
    R> mydata <- sim$X

we set the initial values for the optimizer as \(\theta_{1}=\theta_{2}=\theta_{3}=\theta_{4}=1\), and we specify the coefficients drift and diffusion as expressions. you can use the `upper` and `lower` limits of the search region used by the optimizer (here using the default method `"L-BFGS-B"`), and specifying the method to use with `pmle="euler"`.

    R> fx <- expression( theta[1]+theta[2]*x ) ## drift coefficient of model
    R> gx <- expression( theta[3]*x^theta[4] ) ## diffusion coefficient of model
    R> fitmod <- fitsde(data = mydata, drift = fx, diffusion = gx, start = list(theta1=1, theta2=1,
    +                 theta3=1,theta4=1),pmle="euler")
    R> fitmod

    Call:
    fitsde(data = mydata, drift = fx, diffusion = gx, start = list(theta1 = 1,
        theta2 = 1, theta3 = 1, theta4 = 1), pmle = "euler")

    Coefficients:
     theta1  theta2  theta3  theta4
    1.11201 1.95717 0.50243 0.30419

The estimated coefficients are extracted from the output object `fitmod` as follows:

    R> coef(fitmod)

     theta1  theta2  theta3  theta4
    1.11201 1.95717 0.50243 0.30419

We can use the `summary` function to produce result summaries of output object:

    R> summary(fitmod)

    Pseudo maximum likelihood estimation

    Method:  Euler
    Call:
    fitsde(data = mydata, drift = fx, diffusion = gx, start = list(theta1 = 1,
        theta2 = 1, theta3 = 1, theta4 = 1), pmle = "euler")

    Coefficients:
           Estimate Std. Error
    theta1  1.11201   1.495082
    theta2  1.95717   0.196385
    theta3  0.50243   0.010715
    theta4  0.30419   0.010697

    -2 log L: -66048

`vcov` for variance-covariance matrice, and extract log-likelihood by `logLik`:

    R> vcov(fitmod)

                 theta1        theta2        theta3       theta4
    theta1  2.235269019 -0.2409196808 -0.0000809562  0.000010246
    theta2 -0.240919681  0.0385669931 -0.0000048572  0.000013319
    theta3 -0.000080956 -0.0000048572  0.0001148041 -0.000108128
    theta4  0.000010246  0.0000133186 -0.0001081279  0.000114421

    R> logLik(fitmod)

    [1] 33024

    R> AIC(fitmod)

    [1] -66040

    R> BIC(fitmod)

    [1] -66030

Computes confidence intervals for one or more parameters in a fitted SDE:

    R> confint(fitmod, level=0.95)

              2.5 %  97.5 %
    theta1 -1.81829 4.04232
    theta2  1.57226 2.34208
    theta3  0.48142 0.52343
    theta4  0.28322 0.32515

## Ozaki method

The second approach we present is the Ozaki method, and it works for homogeneous stochastic differential equations. Consider the stochastic differential equation: \[\begin{equation}\label{eq10} dX_{t}= f(X_{t},\underline{\theta}) dt + \sigma dW_{t}, \quad \quad t \geq 0 \, , X_{0} = x_{0}, \end{equation}\] where \(\sigma\) is supposed to be constant. We just recall that the transition density for the Ozaki method is Gaussian, we have that: \(X_{t+\Delta t}|X_{t} = x \sim \mathcal{N}(\text{E}_{x},\text{V}_{x})\), where: \[\begin{align}\label{eq11} \text{E}_{x} &= x + \frac{f(x)}{\partial_{x} f(x)} \left( e^{\partial_{x} f(x) \Delta t} - 1 \right), \quad\text{and}\quad \text{V}_{x} &= \sigma^{2} \frac{e^{2K_{x} \Delta t} -1}{2K_{x}}, \end{align}\] with: \[\begin{equation*} K_{x} = \frac{1}{\Delta t} \log \left(1+\frac{f(x)}{x\partial_{x}f(x)}\left(e^{\partial_{x}f(x) \Delta t}-1\right) \right) \end{equation*}\]

It is always possible to transform process \(X_t\) with a constant diffusion coefficient using the Lamperti transform.

Now we consider the Vasicek model, with \(f(x,\theta) = \theta_{1} (\theta_{2}- x)\) and constant volatility \(g(x,\theta) = \theta_{3}\), \[\begin{equation}\label{eq12} dX_{t} = \theta_{1} (\theta_{2}- X_{t}) dt + \theta_{3} dW_{t},\qquad X_{0}=5 \end{equation}\]

with \(\theta_{1}=3\), \(\theta_{2}=2\) and \(\theta_{3}=0.5\), we generate sampled data \(X_{t_{i}}\), with \(\Delta t =10^{-2}\), as following:

    R> f <- expression( 3*(2-x) ) ; g <- expression( 0.5 )
    R> sim <- snssde1d(drift=f,diffusion=g,x0=5,Dt=0.01)
    R> HWV <- sim$X

we set the initial values for the optimizer as \(\theta_{1}=\theta_{2}=\theta_{3}=1\), and we specify the coefficients drift and diffusion as expressions. Specifying the method to use with `pmle="ozaki"`, which can easily be implemented in R as follows:

    R> fx <- expression( theta[1]*(theta[2]- x) ) ## drift coefficient of model
    R> gx <- expression( theta[3] )           ## diffusion coefficient of model
    R> fitmod <- fitsde(data=HWV,drift=fx,diffusion=gx,start = list(theta1=1,theta2=1,
    +                   theta3=1),pmle="ozaki")
    R> summary(fitmod)

    Pseudo maximum likelihood estimation

    Method:  Ozaki
    Call:
    fitsde(data = HWV, drift = fx, diffusion = gx, start = list(theta1 = 1,
        theta2 = 1, theta3 = 1), pmle = "ozaki")

    Coefficients:
           Estimate Std. Error
    theta1  2.79627   0.366488
    theta2  1.97629   0.060255
    theta3  0.51036   0.011414

    -2 log L: -3113.1

If you want to have confidence intervals \(\theta_{1}\) and \(\theta_{2}\) parameters only, using the command `confint` as following:

    R> confint(fitmod,parm=c("theta1","theta2"),level=0.95)

            2.5 % 97.5 %
    theta1 2.0780 3.5146
    theta2 1.8582 2.0944

## Shoji-Ozaki method

An extension of the method to Ozaki the more general case where the drift is allowed to depend on the time variable \(t\), and also the diffusion coefficient can be varied is the method Shoji and Ozaki. Consider the stochastic differential equation: \[\begin{equation}\label{eq13} dX_{t}= f(t,X_{t},\underline{\theta}) dt + g(X_{t},\underline{\theta}) dW_{t}, \quad \quad t \geq 0 \, , X_{0} = x_{0}, \end{equation}\] the transition density for the Shoji-Ozaki method is Gaussian, we have that: \(X_{t+\Delta t}|X_{t} = x \sim \mathcal{N}\left(\mathrm{A}_{(t,x)}x,\mathrm{B}^{2}_{(t,x)}\right)\), where: \[\begin{align}\label{eq14} \mathrm{A}_{(t,x)} &= 1+ \frac{f(t,x)}{x\mathrm{L}_{t}} \left(e^{\mathrm{L}_{t}\Delta t }-1\right)+\frac{\mathrm{M}_{t}}{x\mathrm{L}^{2}_{t}} \left(e^{\mathrm{L}_{t} \Delta t}-1-\mathrm{L}_{t}\Delta t\right), \\ \mathrm{B}_{(t,x)} &= g(x) \sqrt{\frac{e^{2\mathrm{L}_{t} \Delta t}-1}{2\mathrm{L}_{t}}}, \end{align}\] with: \[\begin{equation*} \mathrm{L}_{t} = \partial_{x} f(t,x) \quad\text{and}\quad \mathrm{M}_{t} = \frac{g^{2}(x)}{2} \partial_{xx} f(t,x)+ \partial_{t} f(t,x). \end{equation*}\] As an example, we consider the following model: \[\begin{equation}\label{eq15} dX_{t} = a(t)X_{t} dt + \theta_{2}X_{t} dW_{t},\qquad X_{0}=10 \end{equation}\]

with: \(a(t) = \theta_{1}t\), and we generate sampled data \(X_{t_{i}}\), with \(\theta_{1}=-2\), \(\theta_{2}=0.2\) and time step \(\Delta t =10^{-3}\), as following:

    R> f <- expression(-2*x*t) ; g <- expression(0.2*x)
    R> sim <- snssde1d(drift=f,diffusion=g,N=1000,Dt=0.001,x0=10)
    R> mydata <- sim$X

we set the initial values for the optimizer as \(\theta_{1}=\theta_{2}=1\), and we specify the method to use with `pmle="shoji"`:

    R> fx <- expression( theta[1]*x*t ) ## drift coefficient of model
    R> gx <- expression( theta[2]*x )   ## diffusion coefficient of model
    R> fitmod <- fitsde(data=mydata,drift=fx,diffusion=gx,start = list(theta1=1,
    +                  theta2=1),pmle="shoji",lower=c(-3,0),upper=c(-1,1))
    R> summary(fitmod)

    Pseudo maximum likelihood estimation

    Method:  Shoji
    Call:
    fitsde(data = mydata, drift = fx, diffusion = gx, start = list(theta1 = 1,
        theta2 = 1), pmle = "shoji", lower = c(-3, 0), upper = c(-1,
        1))

    Coefficients:
           Estimate Std. Error
    theta1 -1.58027  0.3436216
    theta2  0.19826  0.0044351

    -2 log L: -3313.2

## Kessler method

Kessler (1997) proposed to use a higher-order Ito-Taylor expansion to approximate the mean and variance in a conditional Gaussian density. Consider the stochastic differential equation \(dX_{t}= f(X_{t},\underline{\theta}) dt + g(X_{t},\underline{\theta}) dW_{t}\) , the transition density by Kessler method is: \(X_{t+\Delta t}|X_{t} = x \sim \mathcal{N}\left(\text{E}_{x},\text{V}_{x}\right)\), where: \[\begin{align}\label{eq16} \text{E}_{x} &= x + f(t,x) \Delta t+\left(f(t,x)\partial_{x}f(t,x) + \frac{1}{2} g^{2}(t,x) \partial_{xx}g(t,x)\right)\frac{(\Delta t)^2}{2}, \\ \text{V}_{x} &= x^2 +(2f(t,x)x+g^{2}(t,x)) \Delta t +\bigg(2f(t,x)\left(\partial_{x}f(t,x)x+f(t,x)+g(t,x)\partial_{x}g(t,x)\right) \nonumber\\ &\quad+g^{2}(t,x)\left(\partial_{xx}f(t,x)x+2\partial_{x}f(t,x)+\partial_{x}g^{2}(t,x)+g(t,x)\partial_{xx}g(t,x)\right)\bigg)\frac{(\Delta t)^2}{2}-\text{E}^{2}_{x}. \end{align}\] We consider the following Hull-White (extended Vasicek) model: \[\begin{equation}\label{eq17} dX_{t} = a(t)(b(t)-X_{t}) dt + \sigma(t) dW_{t},\qquad X_{0}=2 \end{equation}\]

with: \(a(t) = \theta_{1}t\) and \(b(t)=\theta_{2}\sqrt{t}\), the volatility depends on time: \(\sigma(t)=\theta_{3}t\). We generate sampled data of \(X_t\), with \(\theta_{1}=3\), \(\theta_{2}=1\) and \(\theta_{3}=0.3\), time step \(\Delta t =10^{-3}\), as following:

    R> f <- expression(3*t*(sqrt(t)-x)) ; g <- expression(0.3*t)
    R> sim <- snssde1d(drift=f,diffusion=g,M=1,N=1000,x0=2,Dt=0.001)
    R> mydata <- sim$X

we set the initial values for the optimizer as \(\theta_{1}=\theta_{2}=\theta_{3}=1\), and we specify the method to use with `pmle="kessler"`:

    R> fx <- expression( theta[1]*t* ( theta[2]*sqrt(t) - x ) ) ## drift coefficient of model
    R> gx <- expression( theta[3]*t ) ## diffusion coefficient of model
    R> fitmod <- fitsde(data=mydata,drift=fx,diffusion=gx,start = list(theta1=1,
    +                   theta2=1,theta3=1),pmle="kessler")
    R> summary(fitmod)

    Pseudo maximum likelihood estimation

    Method:  Kessler
    Call:
    fitsde(data = mydata, drift = fx, diffusion = gx, start = list(theta1 = 1,
        theta2 = 1, theta3 = 1), pmle = "kessler")

    Coefficients:
           Estimate Std. Error
    theta1  1.70762  0.3469224
    theta2  0.51601  0.3917016
    theta3  0.30409  0.0068078

    -2 log L: -8437.3

# The `fitsde()` in practice

## Model selection via AIC

Let the following models: \[\begin{align*} % \nonumber to remove numbering (before each equation) dX_{t} &= \theta_{1} X_{t} dt + \theta_{2} X_{t}^{\theta_{3}} dW_{t}, &\text{(true model)}\\ dX_{t} &= (\theta_{1}+\theta_{2} X_{t}) dt + \theta_{3} X_{t}^{\theta_{4}} dW_{t},&\text{(competing model 1)}\\ dX_{t} &= (\theta_{1}+\theta_{2} X_{t}) dt + \theta_{3} \sqrt{X_{t}} dW_{t}, &\text{(competing model 2)}\\ dX_{t} &= \theta_{1} dt + \theta_{2} X_{t}^{\theta_{3}} dW_{t}, &\text{(competing model 3)} \end{align*}\]

We generate data from true model with parameters \(\underline{\theta}=(2,0.3,0.5)\), initial value \(X_{0}=2\) and \(\Delta t =10^{-4}\), as following:

    R> f <- expression( 2*x )
    R> g <- expression( 0.3*x^0.5 )
    R> sim <- snssde1d(drift=f,diffusion=g,M=1,N=10000,x0=2,Dt=0.0001)
    R> mydata <- sim$X

We test the performance of the AIC statistics for the four competing models, we can proceed as follows:

    R> ## True model
    R> fx <- expression( theta[1]*x )
    R> gx <- expression( theta[2]*x^theta[3] )
    R> truemod <- fitsde(data=mydata,drift=fx,diffusion=gx,start = list(theta1=1,
    +                    theta2=1,theta3=1),pmle="euler")
    R> ## competing model 1
    R> fx1 <- expression( theta[1]+theta[2]*x )
    R> gx1 <- expression( theta[3]*x^theta[4] )
    R> mod1 <- fitsde(data=mydata,drift=fx1,diffusion=gx1,start = list(theta1=1,
    +           theta2=1,theta3=1,theta4=1),pmle="euler")
    R> ## competing model 2
    R> fx2 <- expression( theta[1]+theta[2]*x )
    R> gx2 <- expression( theta[3]*sqrt(x) )
    R> mod2 <- fitsde(data=mydata,drift=fx2,diffusion=gx2,start = list(theta1=1,
    +            theta2=1,theta3=1),pmle="euler")
    R> ## competing model 3
    R> fx3 <- expression( theta[1] )
    R> gx3 <- expression( theta[2]*x^theta[3] )
    R> mod3 <- fitsde(data=mydata,drift=fx3,diffusion=gx3,start = list(theta1=1,
    +            theta2=1,theta3=1),pmle="euler")
    R> ## Computes AIC
    R> AIC <- c(AIC(truemod),AIC(mod1),AIC(mod2),AIC(mod3))
    R> Test <- data.frame(AIC,row.names = c("True mod","Comp mod1","Comp mod2","Comp mod3"))
    R> Bestmod <- rownames(Test)[which.min(Test[,1])]
    R> Bestmod

    [1] "True mod"

the estimates under the different models:

    R> Theta1 <- c(coef(truemod)[[1]],coef(mod1)[[1]],coef(mod2)[[1]],coef(mod3)[[1]])
    R> Theta2 <- c(coef(truemod)[[2]],coef(mod1)[[2]],coef(mod2)[[2]],coef(mod3)[[2]])
    R> Theta3 <- c(coef(truemod)[[3]],coef(mod1)[[3]],coef(mod2)[[3]],coef(mod3)[[3]])
    R> Theta4 <- c("",round(coef(mod1)[[4]],5),"","")
    R> Parms  <- data.frame(Theta1,Theta2,Theta3,Theta4,row.names = c("True mod",
    +                       "Comp mod1","Comp mod2","Comp mod3"))
    R> Parms

                Theta1  Theta2  Theta3 Theta4
    True mod  1.988747 0.30575 0.49435
    Comp mod1 1.118869 1.80983 0.30573 0.4944
    Comp mod2 0.026459 1.98457 0.30285
    Comp mod3 9.113331 0.30498 0.49801

## Application to real data

We make use of real data of the U.S. Interest Rates monthly form \(06/1964\) to \(12/1989\) (see Figure 1) available in package Ecdat, and we estimate the parameters \(\underline{\theta}=(\theta_{1},\theta_{2},\theta_{3},\theta_{4})\) of CKLS model. These data have been analyzed by Brouste et all (2014) with yuima package, here we confirm the results of the estimates by several approximation methods.

    R> data(Irates)
    R> rates <- Irates[, "r1"]
    R> X <- window(rates, start = 1964.471, end = 1989.333)
    R> plot(X)

The U.S. Interest Rates monthly form \(06/1964\) to \(12/1989\)

we can now use all previous methods by `fitsde` function to estimate the parameters of CKLS model as follows:

    R> fx <- expression( theta[1]+theta[2]*x ) ## drift coefficient of CKLS model
    R> gx <- expression( theta[3]*x^theta[4] ) ## diffusion coefficient of CKLS model
    R> pmle <- eval(formals(fitsde.default)$pmle)
    R> fitres <- lapply(1:4, function(i) fitsde(X,drift=fx,diffusion=gx,pmle=pmle[i],
    +                   start = list(theta1=1,theta2=1,theta3=1,theta4=1)))
    R> Coef <- data.frame(do.call("cbind",lapply(1:4,function(i) coef(fitres[[i]]))))
    R> Info <- data.frame(do.call("rbind",lapply(1:4,function(i) logLik(fitres[[i]]))),
    +                    do.call("rbind",lapply(1:4,function(i) AIC(fitres[[i]]))),
    +                    do.call("rbind",lapply(1:4,function(i) BIC(fitres[[i]]))),
    +                    row.names=pmle)
    R> names(Coef) <- c(pmle)
    R> names(Info) <- c("logLik","AIC","BIC")
    R> Coef

              euler  kessler    ozaki    shoji
    theta1  2.07695  2.14335  2.11532  2.10150
    theta2 -0.26319 -0.27434 -0.26905 -0.26647
    theta3  0.13022  0.12598  0.12652  0.13167
    theta4  1.45132  1.46917  1.46491  1.45131

    R> Info

             logLik    AIC    BIC
    euler   -237.88 483.76 487.15
    kessler -237.78 483.57 486.96
    ozaki   -237.84 483.67 487.07
    shoji   -237.88 483.76 487.15

In Figure 2 we reports the sample mean of the solution of the CKLS model with the estimated parameters and real data, their empirical \(95\%\) confidence bands (from the \(2.5th\) to the \(97.5th\) percentile), we can proceed as follows:

    R> f <- expression( (2.076-0.263*x) )
    R> g <- expression( 0.130*x^1.451 )
    R> mod <- snssde1d(drift=f,diffusion=g,x0=X[1],M=500, N=length(X),t0=1964.471, T=1989.333)
    R> mod

    Itô Sde 1D:
        | dX(t) = (2.076 - 0.263 * X(t)) * dt + 0.13 * X(t)^1.451 * dW(t)
    Method:
        | Euler scheme with order 0.5
    Summary:
        | Size of process   | N  = 299.
        | Number of simulation  | M  = 500.
        | Initial value     | x0 = 3.317.
        | Time of process   | t in [1964.5,1989.3].
        | Discretization    | Dt = 0.08343.

    R> plot(mod,type="n",ylim=c(0,30))
    R> lines(X,col=4,lwd=2)
    R> lines(time(mod),apply(mod$X,1,mean),col=2,lwd=2)
    R> lines(time(mod),apply(mod$X,1,bconfint,level=0.95)[1,],col=5,lwd=2)
    R> lines(time(mod),apply(mod$X,1,bconfint,level=0.95)[2,],col=5,lwd=2)
    R> legend("topleft",c("real data","mean path",paste("bound of", 95,"% confidence")),inset = .01,col=c(4,2,5),lwd=2,cex=0.8)

The path mean of the solution of the CKLS model with the estimated parameters and real data

# Further reading

1. `snssdekd()` & `dsdekd()` & `rsdekd()`- Monte-Carlo Simulation and Analysis of Stochastic Differential Equations.
2. `bridgesdekd()` & `dsdekd()` & `rsdekd()` - Constructs and Analysis of Bridges Stochastic Differential Equations.
3. `fptsdekd()` & `dfptsdekd()` - Monte-Carlo Simulation and Kernel Density Estimation of First passage time.
4. `MCM.sde()` & `MEM.sde()` - Parallel Monte-Carlo and Moment Equations for SDEs.
5. `TEX.sde()` - Converting Sim.DiffProc Objects to LaTeX.
6. `fitsde()` - Parametric Estimation of 1-D Stochastic Differential Equation.

# References

1. Brouste A, Fukasawa M, Hino H, Iacus SM, Kamatani K, Koike Y, Masuda H, Nomura R,Ogihara T, Shimuzu Y, Uchida M, Yoshida N (2014). The YUIMA Project: A ComputationalFramework for Simulation and Inference of Stochastic Differential Equations." Journal of Statistical Software, 57(4), 1-51. URL http://www.jstatsoft.org/v57/i04.

2. Guidoum AC, Boukhetala K (2020). Sim.DiffProc: Simulation of Diffusion Processes. R package version 4.6, URL https://cran.r-project.org/package=Sim.DiffProc.

3. Iacus SM (2008). Simulation and Inference for Stochastic Differential Equations: With R Examples. Springer-Verlag, New York.

1. Department of Probabilities & Statistics, Faculty of Mathematics, University of Science and Technology Houari Boumediene, BP 32 El-Alia, U.S.T.H.B, Algeria, E-mail (acguidoum@usthb.dz)↩

2. Faculty of Mathematics, University of Science and Technology Houari Boumediene, BP 32 El-Alia, U.S.T.H.B, Algeria, E-mail (kboukhetala@usthb.dz)↩