hwv - Hull-White/Vasicek mean-reverting Gaussian diffusion models

Synopsis

HWV = hwv(Speed, Level, Sigma)

HWV = hwv(Speed, Level, Sigma, 'Name1', Value1, 'Name2', Value2, ...)

Class

Description

This constructor creates and displays HWV objects, which derive from the SDEMRD (SDE with drift rate expressed in mean-reverting form) class. Use HWV 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 HWV stochastic processes with Gaussian diffusions.

This method allows you to simulate vector-valued HWV processes of the form:

(11-9)

where:

X_t is an NVARS-by-1 state vector of process variables.
S is an NVARS-by-NVARS 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).
V is an NVARS-by-NBROWNS instantaneous volatility rate matrix.
dW_tis 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:

Speed

Speed represents the function S. If you specify Speed as an array, it must be an NVARS-by-NVARS matrix of mean-reversion speeds (the rate at which the state vector reverts to its long-run average Level). If you specify Speed as a function, it calculates the speed of mean reversion. This function must generate an NVARS-by-NVARS matrix of reversion rates when called with two inputs:

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

Level

Level represents the function L. If you specify Level as an array, it must be an NVARS-by-1 column vector of reversion levels. If you specify Level as a function, it must generate an NVARS-by-1 column vector of reversion levels when called 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 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, Sigma must return 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.

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

Optional Input Arguments

Specify optional input arguments as variable-length lists of matching parameter name/value pairs: 'Name1', Value1, 'Name2', Value2, ... and so on. The following rules apply when specifying parameter-name pairs:

Specify the parameter name as a character string, followed by its corresponding parameter 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, `hwv` applies the same initial value to all state variables on all trials. If `StartState` is a column vector, `hwv` applies a unique initial value to each state variable on all trials. If `StartState` is a matrix, `hwv` 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

HWV

Object of class hwv with the following displayed parameters:

StartTime: Initial observation time
StartState: Initial state at StartTime
Correlation: Access function for the Correlation input, 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
Speed: 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

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

Examples

Creating Hull-White/Vasicek (HWV) Gaussian Diffusion Models