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Brownian motion models
BM = bm(Mu, Sigma)
BM = bm(Mu, Sigma, 'Name1', Value1, 'Name2', Value2, ...)
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 continuoustime Brownian motion stochastic processes. This enables you to transform a vector of NBROWNS uncorrelated, zerodrift, unitvariance 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 vectorvalued BM process of the form:
(181) 
where:
X_{t} is an NVARSby1 state vector of process variables.
μ is an NVARSby1 driftrate vector.
V is an NVARSbyNBROWNS instantaneous volatility rate matrix.
dW_{t} is an NBROWNSby1 vector of (possibly) correlated zerodrift/unitvariance rate Brownian components.
Specify required input parameters as one of the following types:
A MATLAB^{®} array. Specifying an array indicates a static (nontimevarying) 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.
The required input parameters are:
Mu  Mu represents μ.
If you specify Mu as an array, it must be an NVARSby1
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 NVARSby1 column
vector when invoked with two inputs:

Sigma  Sigma represents the parameter V.
If you specify Sigma as an array, it must be an NVARSbyNBROWNS 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 NVARSbyNBROWNS matrix of
volatility rates when invoked with two inputs:
Although the constructor does not enforce restrictions on the sign of this argument, Sigma is usually specified as a positive value. 
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, NVARSby1 column vector, or NVARSbyNTRIALS 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 NBROWNSbyNBROWNS positive
semidefinite matrix, or as a deterministic function C(t) that
accepts the current time t and returns an NBROWNSbyNBROWNS 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 NBROWNSbyNBROWNS identity matrix representing independent Gaussian processes. 
Simulation  A userdefined simulation function or SDE simulation method. If you do not specify a value for Simulation, the default method is simulation by Euler approximation (simByEuler). 
BM  Object of class BM with the following displayed parameters:

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.
AitSahalia, Y., "Testing ContinuousTime Models of the Spot Interest Rate," The Review of Financial Studies, Spring 1996, Vol. 9, No. 2, pp. 385–426.
AitSahalia, 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: SpringerVerlag, 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: ContinuousTime Models, New York: SpringerVerlag, 2004.