bm - Brownian motion models

Synopsis

BM = bm(Mu, Sigma)

BM = bm(Mu, Sigma, 'Name1', Value1, 'Name2', Value2, ...)

Class

Description

This constructor creates and displays Brownian motion (sometimes called arithmetic Brownian motion or generalized Wiener process) objects that derive from the SDELD (SDE with drift rate expressed in linear form) class. Use BM objects to simulate sample paths of NVARS state variables driven by NBROWNS sources of risk over NPERIODS consecutive observation periods, approximating continuous-time Brownian motion stochastic processes. This enables you to transform a vector of NBROWNS uncorrelated, zero-drift, unit-variance rate Brownian components into a vector of NVARS Brownian components with arbitrary drift, variance rate, and correlation structure.

The bm method allows you to simulate any vector-valued BM process of the form:

(11-3)

where:

X_t is an NVARS-by-1 state vector of process variables.
μ is an NVARS-by-1 drift-rate vector.
V is an NVARS-by-NBROWNS instantaneous volatility rate matrix.
dW_t is an NBROWNS-by-1 vector of (possibly) correlated zero-drift/unit-variance rate Brownian components.

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:

Mu

Mu represents μ. If you specify Mu as an array, it must be an NVARS-by-1 column vector representing the drift rate (the expected instantaneous rate of drift, or time trend). If you specify Mu as a function, it calculates the expected instantaneous rate of drift. This function must generate an NVARS-by-1 column vector when invoked with two inputs:

A real-valued scalar observation time t.
An NVARS-by-1 state vector X_t.

Sigma

Sigma represents the parameter V. If you specify Sigma as an array, it must be an NVARS-by-NBROWNS 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 Sigma 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 X_t.

Although the constructor does not enforce restrictions on the sign of this argument, Sigma 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, `bm` applies the same initial value to all state variables on all trials. If `StartState` is a column vector, `bm` applies a unique initial value to each state variable on all trials. If `StartState` is a matrix, `bm` 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

BM

Object of class BM with the following displayed 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
Simulation: A simulation function or method
Mu: Access function for the input argument Mu, callable as a function of time and state
Sigma: Access function for the input argument Sigma, callable as a function of time and state

Remarks

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, bm treats it as a static function of time and state, thereby guaranteeing that all parameters are accessible by the same interface.

Examples

Creating Brownian Motion (BM) Models