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| R2011b Documentation → Econometrics Toolbox | |
Learn more about Econometrics Toolbox |
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| Contents | Index |
[Paths, Times, Z] = OBJ.simBySolution(NPERIODS)
[Paths, Times, Z] = OBJ.simBySolution(NPERIODS, 'Name1', Value1,'Name2', Value2, ...)
The simBySolution method simulates NTRIALS sample paths of NVARS correlated state variables, driven by NBROWNS Brownian motion sources of risk over NPERIODS consecutive observation periods, approximating continuous-time Hull-White/Vasicek (HWV) and geometric Brownian motion (GBM) short-rate models by an approximation of the closed-form solution.
Consider a separable, vector-valued HWV model of the form:
 | (9-14) |
where:
X is an NVARS-by-1 state vector of process variables.
S is an NVARS-by-NVARS matrix 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.
W is an NBROWNS-by-1 Brownian motion vector.
or a separable, vector-valued GBM model of the form:
 | (9-15) |
where:
Xt is an NVARS-by-1 state vector of process variables.
μ is an NVARS-by-NVARS generalized expected instantaneous rate of return matrix.
V is an NVARS-by-NBROWNS instantaneous volatility rate matrix.
dWt is an NBROWNS-by-1 Brownian motion vector.
The simBySolution method simulates the state vector Xt using an approximation of the closed-form solution of diagonal-drift models.
When evaluating the expressions, simBySolution assumes that all model parameters are piecewise-constant over each simulation period.
In general, this is not the exact solution to the models, because the probability distributions of the simulated and true state vectors are identical only for piecewise-constant parameters.
When parameters are piecewise-constant over each observation period, the simulated process is exact for the observation times at which Xt is sampled.
| OBJ | Hull-White/Vasicek (HWV) or geometric Brownian motion (GBM) model. |
| NPERIODS | Positive scalar integer number of simulation periods. The value of this argument determines the number of rows of the simulated output series. |
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:
| NTRIALS | Positive scalar integer number of simulated trials (sample paths) of NPERIODS observations each. If you do not specify a value for this argument, the default is 1, indicating a single path of correlated state variables. |
| DeltaTime | Scalar or NPERIODS-by-1 column vector of positive time increments between observations. DeltaTime represents the familiar dt found in stochastic differential equations, and determines the times at which simBySolution reports the simulated paths of the output state variables. If you do not specify a value for this argument, the default is 1. |
| NSTEPS | Positive scalar integer number of intermediate time steps within each time increment dt (specified as DeltaTime). simBySolution partitions each time increment dt into NSTEPS subintervals of length dt/NSTEPS, and refines the simulation by evaluating the simulated state vector at NSTEPS - 1 intermediate points. Although simBySolution does not report the output state vector at these intermediate points, the refinement improves accuracy by allowing the simulation to more closely approximate the underlying continuous-time process. If you do not specify a value for NSTEPS, the default is 1, indicating no intermediate evaluation. |
| Antithetic | Scalar logical flag that indicates whether antithetic sampling
is used to generate the Gaussian random variates that drive the Brownian
motion vector (Wiener processes). When Antithetic is TRUE (logical 1), simBySolution performs
sampling such that all primary and antithetic paths are simulated
and stored in successive matching pairs:
If you specify Antithetic to be any value other than TRUE,simBySolution assumes that it is FALSE (logical 0) by default, and does not perform antithetic sampling. When you specify an input noise process (see Z), simBySolution ignores the value of Antithetic. |
| Z | Direct specification of the dependent random noise process
used to generate the Brownian motion vector (Wiener process) that
drives the simulation. Specify this argument as a function, or as
an (NPERIODS * NSTEPS)-by-NBROWNS-by-NTRIALS array
of dependent random variates. If you specify Z as
a function, it must return an NBROWNS-by-1 column
vector, and you must call it with two inputs:
If you do not specify a value for Z, simBySolution generates correlated Gaussian variates based on the Correlation member of the SDE object. |
| StorePaths | Scalar logical flag that indicates how simBySolution stores the output array Paths and returns it to the caller. If StorePaths is TRUE(the default value) or is unspecified, simBySolution returns Paths as a three-dimensional time series array. If StorePaths is FALSE (logical 0), simBySolution returns the Paths output array as an empty matrix. |
| Processes | Function or cell array of functions that indicates a sequence
of end-of-period processes or state vector adjustments of the form
If you specify more than one processing function, simBySolution invokes the functions in the order in which they appear in the cell array. You can use this argument to specify boundary conditions, prevent negative prices, accumulate statistics, plot graphs, and more. If you do not specify a processing function, simBySolution makes no adjustments and performs no processing. |
| Paths | (NPERIODS + 1)-by-NVARS-by-NTRIALS three-dimensional time series array, consisting of simulated paths of correlated state variables. For a given trial, each row of Paths is the transpose of the state vector Xt at time t. When the input flag StorePaths = FALSE, simBySolution returns Paths as an empty matrix. |
| Times | (NPERIODS + 1)-by-1 column vector of observation times associated with the simulated paths. Each element of Times is associated with the corresponding row of Paths. |
| Z | (NPERIODS * NSTEPS)-by-NBROWNS-by-NTRIALS three-dimensional time series array of dependent random variates used to generate the Brownian motion vector (Wiener processes) that drive the simulation. |
The input argument Z allows you to directly specify the noise generation process. This process takes precedence over the Correlation parameter of the SDE object and the value of the Antithetic input flag. If you do not specify a value for Z, simBySolution generates correlated Gaussian variates, with or without antithetic sampling as requested.
Gaussian diffusion models, such as HWV, allow negative states. By default, simBySolution does nothing to prevent negative states, nor does it guarantee that the model be strictly mean-reverting. Thus, the model may exhibit erratic or explosive growth.
The end-of-period Processes argument allows you to terminate a given trial early. At the end of each time step, simBySolution tests the state vector Xt for an all-NaN condition. Thus, to signal an early termination of a given trial, all elements of the state vector Xt must be NaN. This test enables a user-defined Processes function to signal early termination of a trial, and offers significant performance benefits in some situations (for example, pricing down-and-out barrier options).
Implementing Multidimensional Equity Market Models, Implementation 6: Using GBM Simulation Methods
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