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drift class

Superclasses:

Drift-rate model component

Description

The drift constructor specifies the drift-rate component of continuous-time stochastic differential equations (SDEs). The drift-rate specification supports the simulation of sample paths of NVARS state variables driven by NBROWNS Brownian motion sources of risk over NPERIODS consecutive observation periods, approximating continuous-time stochastic processes.

The drift-rate specification can be any NVARS-by-1 vector-valued function F of the general form:

F(t,Xt)=A(t)+B(t)Xt

where:

  • A is an NVARS-by-1 vector-valued function accessible using the (t, Xt) interface.

  • B is an NVARS-by-NVARS matrix-valued function accessible using the (t, Xt) interface.

And a drift-rate specification is associated with a vector-valued SDE of the form

dXt=F(t,Xt)dt+G(t,Xt)dWt

where:

  • Xt is an NVARS-by-1 state vector of process variables.

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

  • A and B are model parameters.

The drift-rate specification is flexible, and provides direct parametric support for static/linear drift models. It is also extensible, and provides indirect support for dynamic/nonlinear models via an interface. This enables you to specify virtually any drift-rate specification.

Construction

DriftRate = drift(A,B) constructs a default drift object.

For more information on constructing a drift object, see drift.

Input Arguments

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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.

A represents the parameter A, specified as an array or deterministic function of time.

If you specify A as an array, it must be an NVARS-by-1 column vector of intercepts.

As a deterministic function of time, when A is called with a real-valued scalar time t as its only input, A must produce an NVARS-by-1 column vector. If you specify A as a function of time and state, it must generate an NVARS-by-1 column vector of intercepts when invoked with two inputs:

  • A real-valued scalar observation time t.

  • An NVARS-by-1 state vector Xt.

Data Types: double | function_handle

B represents the parameter B, specified as an array or deterministic function of time.

If you specify B as an array, it must be an NVARS-by-NVARS 2-dimensional matrix of state vector coefficients.

As a deterministic function of time, when B is called with a real-valued scalar time t as its only input, B must produce an NVARS-by-NVARS matrix. If you specify B as a function of time and state, it must generate an NVARS-by-NVARS matrix of state vector coefficients when invoked with two inputs:

  • A real-valued scalar observation time t.

  • An NVARS-by-1 state vector Xt.

Data Types: double | function_handle

Properties

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Composite drift-rate function, specified as F(t,Xt). The function stored in Rate fully encapsulates the combined effect of A and B, where A and B are:

The drift object's displayed parameters are:

  • A: The intercept term, A(t,Xt), of F(t,Xt)

  • B: The first order term, B(t,Xt), of F(t,Xt)

Attributes:

SetAccessprivate
GetAccesspublic

Data Types: struct | double

Methods

Instance Hierarchy

The following figure illustrates the inheritance relationships among SDE classes.

For more information, see SDE Class Hierarchy.

Copy Semantics

Value. To learn how value classes affect copy operations, see Copying Objects (MATLAB).

Examples

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Create a drift-rate function F:

F = drift(0, 0.1)   % Drift rate function F(t,X)
F = 
   Class DRIFT: Drift Rate Specification  
   -------------------------------------  
      Rate: drift rate function F(t,X(t)) 
         A: 0
         B: 0.1

The drift object displays like a MATLAB® structure and contains supplemental information, namely, the object's class and a brief description. However, in contrast to the SDE representation, a summary of the dimensionality of the model does not appear, because the drift class creates a model component rather than a model. F does not contain enough information to characterize the dimensionality of a problem.

Algorithms

When you specify the input arguments A and B as MATLAB arrays, they are associated with a linear drift parametric form. By contrast, when you specify either A or B as a function, you can customize virtually any drift-rate specification.

Accessing the output drift-rate parameters A and B with no inputs simply returns the original input specification. Thus, when you invoke drift-rate 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 drift-rate parameters with inputs, they behave like functions, giving the impression of dynamic behavior. The parameters A and B accept the observation time t and a state vector Xt, and return an array of appropriate dimension. Specifically, parameters A and B evaluate the corresponding drift-rate component. Even if you originally specified an input as an array, drift treats it as a static function of time and state, by that means 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.

Introduced in R2008a

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