Cox-Ingersoll-Ross mean-reverting square root diffusion model

Creates and displays `cir`

objects, which derive from the
`sdemrd`

(SDE with drift rate expressed in
mean-reverting form) class.

Use `cir`

objects to simulate sample paths of
`NVARS`

state variables expressed in mean-reverting drift-rate
form. These state variables are driven by `NBROWNS`

Brownian motion
sources of risk over `NPERIODS`

consecutive observation periods,
approximating continuous-time CIR stochastic processes with square root
diffusions.

You can simulate any vector-valued CIR process of the form:

$$d{X}_{t}=S(t)[L(t)-{X}_{t}]dt+D(t,{X}_{t}^{\frac{1}{2}})V(t)d{W}_{t}$$

where:

*X*is an_{t}`NVARS`

-by-`1`

state vector of process variables.*S*is an`NVARS`

-by-`NVARS`

matrix of mean reversion speeds (the rate of mean reversion).*L*is an`NVARS`

-by-`1`

vector of mean reversion levels (long-run mean or level).*D*is an`NVARS`

-by-`NVARS`

diagonal matrix, where each element along the main diagonal is the square root of the corresponding element of the state vector.*V*is an`NVARS`

-by-`NBROWNS`

instantaneous volatility rate matrix.*dW*is an_{t}`NBROWNS`

-by-`1`

Brownian motion vector.

creates a default `CIR`

= cir(`Speed`

,`Level`

,`Sigma`

)`CIR`

object.

Specify required input parameters as one of the following types:

A MATLAB

^{®}array. Specifying an array indicates a static (non-time-varying) parametric specification. This array fully captures all implementation details, which are clearly associated with a parametric form.A MATLAB function. Specifying a function provides indirect support for virtually any static, dynamic, linear, or nonlinear model. This parameter is supported via an interface, because all implementation details are hidden and fully encapsulated by the function.

You can specify combinations of array and function input parameters as needed.

Moreover, a parameter is identified as a deterministic function
of time if the function accepts a scalar time `t`

as its only input argument. Otherwise, a parameter is assumed to be
a function of time *t* and state
*X(t)* and is invoked with both input
arguments.

creates a `CIR`

= cir(___,`Name,Value`

)`CIR`

object with additional options specified by
one or more `Name,Value`

pair arguments.

`Name`

is a property name and `Value`

is
its corresponding value. `Name`

must appear inside single
quotes (`''`

). You can specify several name-value pair
arguments in any order as
`Name1,Value1,…,NameN,ValueN`

The `CIR`

object has the following Properties:

`StartTime`

— Initial observation time`StartState`

— Initial state at time`StartTime`

`Correlation`

— Access function for the`Correlation`

input argument, callable as a function of time`Drift`

— Composite drift-rate function, callable as a function of time and state`Diffusion`

— Composite diffusion-rate function, callable as a function of time and state`Simulation`

— A simulation function or method`Speed`

— Access function for the input argument`Speed`

, callable as a function of time and state`Level`

— Access function for the input argument`Level`

, callable as a function of time and state`Sigma`

— Access function for the input argument`Sigma`

, callable as a function of time and state

`interpolate` | Brownian interpolation of stochastic differential equations |

`simulate` | Simulate multivariate stochastic differential equations (SDEs) |

`simByEuler` | Euler simulation of stochastic differential equations (SDEs) |

`simByTransition` | Simulate Cox-Ingersoll-Ross sample paths with transition density |

When you specify the required input parameters as arrays, they are associated with a specific parametric form. By contrast, when you specify either required input parameter as a function, you can customize virtually any specification.

Accessing the output parameters with no inputs simply returns the original input specification. Thus, when you invoke these parameters with no inputs, they behave like simple properties and allow you to test the data type (double vs. function, or equivalently, static vs. dynamic) of the original input specification. This is useful for validating and designing methods.

When you invoke these parameters with inputs, they behave like functions, giving the
impression of dynamic behavior. The parameters accept the observation time
*t* and a state vector
*X _{t}*, and return an array of appropriate
dimension. Even if you originally specified an input as an array,

`cir`

treats it as a static function of time and state, by that means guaranteeing that all
parameters are accessible by the same interface.[1] Ait-Sahalia, Y. “Testing Continuous-Time Models of the Spot Interest
Rate.” *The Review of Financial Studies*, Spring 1996, Vol.
9, No. 2, pp. 385–426.

[2] Ait-Sahalia, Y. “Transition Densities for Interest Rate and Other
Nonlinear Diffusions.” *The Journal of Finance*, Vol. 54,
No. 4, August 1999.

[3] Glasserman, P. *Monte Carlo Methods in Financial
Engineering.* New York, Springer-Verlag, 2004.

[4] Hull, J. C. *Options, Futures, and Other Derivatives*, 5th
ed. Englewood Cliffs, NJ: Prentice Hall, 2002.

[5] Johnson, N. L., S. Kotz, and N. Balakrishnan. *Continuous Univariate
Distributions.* Vol. 2, 2nd ed. New York, John Wiley & Sons,
1995.

[6] Shreve, S. E. *Stochastic Calculus for Finance II: Continuous-Time
Models.* New York: Springer-Verlag, 2004.

`diffusion`

| `drift`

| `interpolate`

| `nearcorr`

| `sdeddo`

| `simByEuler`

| `simByTransition`

| `simulate`

- Creating Cox-Ingersoll-Ross (CIR) Square Root Diffusion Models
- Simulating Equity Prices
- Simulating Interest Rates
- Stratified Sampling
- Pricing American Basket Options by Monte Carlo Simulation
- Base SDE Models
- Drift and Diffusion Models
- Linear Drift Models
- Parametric Models
- SDEs
- SDE Models
- SDE Class Hierarchy
- Performance Considerations