Documentation Center

  • Trial Software
  • Product Updates

sdeld

Construct stochastic differential equation from linear drift-rate models

Synopsis

SDE = sdeld(A, B, Alpha, Sigma)

SDE = sdeld(A, B, Alpha, Sigma, 'Name1', Value1, 'Name2', Value2, ...)

Class

SDELD

Description

This constructor creates and displays SDE objects whose drift rate is expressed in linear drift-rate form and that derive from the SDEDDO (SDE from drift and diffusion objects class).

Use SDELD objects to simulate sample paths of NVARS state variables expressed in linear drift-rate form. They provide a parametric alternative to the mean-reverting drift form (see sdemrd).

These state variables are driven by NBROWNS Brownian motion sources of risk over NPERIODS consecutive observation periods, approximating continuous-time stochastic processes with linear drift-rate functions.

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

(18-10)

where:

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

  • A is an NVARS-by-1 vector.

  • B is an NVARS-by-NVARS matrix.

  • D is an NVARS-by-NVARS diagonal matrix, where each element along the main diagonal is the corresponding element of the state vector raised to the corresponding power of α.

  • 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.

The required input parameters are:

AA represents the parameter A. If you specify A as an array, it must be an NVARS-by-1 column vector of intercepts. If you specify A as a function, 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.

BB represents the parameter B. If you specify B as an array, it must be an NVARS-by-NVARS matrix of state vector coefficients. If you specify B as a function, 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.

AlphaAlpha determines the format of the parameter D. If you specify Alpha as an array, it represents an NVARS-by-1 column vector of exponents. If you specify it as a function, it must return an NVARS-by-1 column vector of exponents when invoked with two inputs:
  • A real-valued scalar observation time t.

  • An NVARS-by-1 state vector Xt.

SigmaSigma represents the parameter V. If you specify Sigma as an array, it represents is 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. If you specify it as a function, 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 Alpha or Sigma, each parameter 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:

StartTimeScalar starting time of the first observation, applied to all state variables. If you do not specify a value for StartTime, the default is 0.
StartStateScalar, NVARS-by-1 column vector, or NVARS-by-NTRIALS matrix of initial values of the state variables.

If StartState is a scalar, sdeld applies the same initial value to all state variables on all trials.

If StartState is a column vector, sdeld applies a unique initial value to each state variable on all trials.

If StartState is a matrix, sdeld 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.

CorrelationCorrelation 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.

SimulationA 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

SDE

Object of class sdeld with the following parameters:

  • 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

  • A: Access function for the input argument A, callable as a function of time and state

  • B: Access function for the input argument B, callable as a function of time and state

  • Alpha: Access function for the input argument Alpha, callable as a function of time and state

  • Sigma: Access function for the input argument Sigma, callable as a function of time and state

  • Simulation: A simulation function or method

More About

expand all

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 and a state vector Xt, and return an array of appropriate dimension. Even if you originally specified an input as an array, sdeld 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.

See Also

| |

Was this topic helpful?