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# cir

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

## Synopsis

CIR = cir(Speed, Level, Sigma)

CIR = cir(Speed, Level, Sigma, 'Name1', Value1, 'Name2', Value2, ...)

CIR

## Description

This constructor 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.

This method allows you to simulate any vector-valued SDE of the form:

 $d{X}_{t}=S\left(t\right)\left[L\left(t\right)-{X}_{t}\right]dt+D\left(t,{X}_{t}^{\frac{1}{2}}\right)V\left(t\right)d{W}_{t}$ (18-3)

where:

• Xt is an 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.

• dWt is an NBROWNS-by-1 Brownian motion vector.

## Input Arguments

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.

 Note:   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.

The required input parameters are:

 Speed Speed represents S. If you specify Speed as an array, it must be an NVARS-by-NVARS matrix of mean-reversion speeds (the rate or speed at which the state vector reverts to its long-run average Level). As a deterministic function of time, when Speed is called with a real-valued scalar time t as its only input, Speed must produce an NVARS-by-NVARS matrixIf you specify Speed as a function of time and state, it must generate an NVARS-by-NVARS matrix of reversion rates when invoked with two inputs:A real-valued scalar observation time t.An NVARS-by-1 state vector Xt. Level Level represents L. If you specify Level as an array, it must be an NVARS-by-1 column vector of reversion levels. As a deterministic function of time, when Level is called with a real-valued scalar time t as its only input, Level must produce an NVARS-by-1 matrix. If you specify Level as a function of time and state, it must generate an NVARS-by-1 column vector of reversion levels when invoked with two inputs: A real-valued scalar observation time t.An NVARS-by-1 state vector Xt. Sigma Sigma represents the parameter V. If you specify Sigma as an array, it must be an NVARS-by-NBROWNS 2-dimensional matrix of instantaneous volatility rates. In this case, each row of Sigma corresponds to a particular state variable. Each column of Sigma corresponds to a particular Brownian source of uncertainty, and associates the magnitude of the exposure of state variables with sources of uncertainty. As a deterministic function of time, when Sigma is called with a real-valued scalar time t as its only input, Sigma must produce an NVARS-by-NBROWNS matrix. If you specify Sigma as a function of time and state, it must generate an NVARS-by-NBROWNS matrix of volatility rates when invoked with two inputs:A real-valued scalar observation time t.An NVARS-by-1 state vector Xt.

 Note:   Although the constructor does not enforce restrictions on the signs of these input arguments, each argument is usually specified as a positive value.

## Optional Input Arguments

Specify optional inputs as matching parameter name/value pairs as follows:

• Specify the parameter name as a character string, followed by its corresponding value.

• You can specify parameter name/value pairs in any order.

• Parameter names are case insensitive.

• You can specify unambiguous partial string matches.

Valid parameter names are:

 StartTime Scalar starting time of the first observation, applied to all state variables. If you do not specify a value for StartTime, the default is 0. StartState Scalar, NVARS-by-1 column vector, or NVARS-by-NTRIALS matrix of initial values of the state variables. If StartState is a scalar, cir applies the same initial value to all state variables on all trials.If StartState is a column vector, cir applies a unique initial value to each state variable on all trials.If StartState is a matrix, cir applies a unique initial value to each state variable on each trial. If you do not specify a value for StartState, all variables start at 1. Correlation Correlation between Gaussian random variates drawn to generate the Brownian motion vector (Wiener processes). Specify Correlation as an NBROWNS-by-NBROWNS positive semidefinite matrix, or as a deterministic function C(t) that accepts the current time t and returns an NBROWNS-by-NBROWNS positive semidefinite correlation matrix. A Correlation matrix represents a static condition.As a deterministic function of time, Correlation allows you to specify a dynamic correlation structure.If you do not specify a value for Correlation, the default is an NBROWNS-by-NBROWNS identity matrix representing independent Gaussian processes. Simulation A user-defined simulation function or SDE simulation method. If you do not specify a value for Simulation, the default method is simulation by Euler approximation (simByEuler).

## Output Arguments

 CIR Object of class CIR with the following displayed parameters: StartTime: Initial observation timeStartState: Initial state at time StartTimeCorrelation: 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 methodSpeed: 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

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### Algorithms

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 followed by a state vector Xt, 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, thereby guaranteeing that all parameters are accessible by the same interface.

## References

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.

Ait-Sahalia, Y., "Transition Densities for Interest Rate and Other Nonlinear Diffusions," The Journal of Finance, Vol. 54, No. 4, August 1999.

Glasserman, P., Monte Carlo Methods in Financial Engineering, New York: Springer-Verlag, 2004.

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

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

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