simBySolution

Simulate approximate solution of diagonal-drift HWV processes

Syntax

[Paths,Times,Z] = simBySolution(MDL,NPeriods)

[Paths,Times,Z] = simBySolution(___,Name,Value)

Description

[Paths,Times,Z] = simBySolution(MDL,NPeriods) simulates approximate solution of diagonal-drift for Hull-White/Vasicek Gaussian Diffusion (HWV) processes.

[Paths,Times,Z] = simBySolution(___,Name,Value) adds optional name-value pair arguments.

Examples

Use simBySolution with an hwv Object

Create an hwv object to represent the model:

$$d{X}_t=0.2(0.1-X_t)dt+0.05d{W}_t.$$

hwv = hwv(0.2, 0.1, 0.05)  % (Speed, Level, Sigma)

hwv = 

   Class HWV: Hull-White/Vasicek
   ----------------------------------------
     Dimensions: State = 1, Brownian = 1
   ----------------------------------------
      StartTime: 0
     StartState: 1
    Correlation: 1
          Drift: drift rate function F(t,X(t)) 
      Diffusion: diffusion rate function G(t,X(t)) 
     Simulation: simulation method/function simByEuler
          Sigma: 0.05
          Level: 0.1
          Speed: 0.2

The simBySolution function simulates the state vector X_t using an approximation of the closed-form solution of diagonal drift HWV models. Each element of the state vector X_t is expressed as the sum of NBROWNS correlated Gaussian random draws added to a deterministic time-variable drift.

nPeriods = 100
[Paths,Times,Z] = simBySolution(hwv, nPeriods,'nTrials', 10);

Input Arguments

MDL — Hull-White/Vasicek (HWV) model

hwv object

Hull-White/Vasicek (HWV) mode, specified as a hwv object that is created using hwv.

Data Types: object

NPeriods — Number of simulation periods

positive integer

Number of simulation periods, specified as a positive scalar integer. The value of NPeriods determines the number of rows of the simulated output series.

Data Types: double

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: [Paths,Times,Z] = simBySolution(HWV,NPeriods,'DeltaTime',dt,'NTrials',10)

'NTrials' — Simulated trials (sample paths) of NPERIODS observations each

1 (single path of correlated state variables) (default) | positive integer

Simulated trials (sample paths) of NPERIODS observations each, specified as the comma-separated pair consisting of 'NTrials' and a positive scalar integer.

Data Types: double

'DeltaTimes' — Positive time increments between observations

1 (default) | scalar | column vector

Positive time increments between observations, specified as the comma-separated pair consisting of 'DeltaTimes' and a scalar or a NPERIODS-by-1 column vector.

DeltaTime represents the familiar dt found in stochastic differential equations, and determines the times at which the simulated paths of the output state variables are reported.

Data Types: double

'NSteps' — Number of intermediate time steps within each time increment dt (specified as DeltaTime)

1 (indicating no intermediate evaluation) (default) | positive integer

Number of intermediate time steps within each time increment dt (specified as DeltaTime), specified as the comma-separated pair consisting of 'NSteps' and a positive scalar integer.

The simBySolution function 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.

Data Types: double

'Antithetic' — Flag to indicate whether simBySolution uses antithetic sampling to generate the Gaussian random variates

False (no antithetic sampling) (default) | logical with values True or False

Flag to indicate whether simBySolution uses antithetic sampling to generate the Gaussian random variates that drive the Brownian motion vector (Wiener processes), specified as the comma-separated pair consisting of 'Antithetic' and a scalar logical flag with a value of True or False.

When you specify True, simBySolution performs sampling such that all primary and antithetic paths are simulated and stored in successive matching pairs:

• Odd trials (1,3,5,...) correspond to the primary Gaussian paths.

• Even trials (2,4,6,...) are the matching antithetic paths of each pair derived by negating the Gaussian draws of the corresponding primary (odd) trial.

Note

If you specify an input noise process (see Z), simBySolution ignores the value of Antithetic.

Data Types: logical

'Z' — Direct specification of the dependent random noise process used to generate the Brownian motion vector

generates correlated Gaussian variates based on the Correlation member of the SDE object (default) | function | three-dimensional array of dependent random variates

Direct specification of the dependent random noise process used to generate the Brownian motion vector (Wiener process) that drives the simulation, specified as the comma-separated pair consisting of 'Z' and a function or as an (NPERIODS * NSTEPS)-by-NBROWNS-by-NTRIALS three-dimensional array of dependent random variates.

The input argument Z allows you to directly specify the noise generation process. This process takes precedence over the Correlation parameter of the input gbm object and the value of the Antithetic input flag.

Note

If you specify Z as a function, it must return an NBROWNS-by-1 column vector, and you must call it with two inputs:

• A real-valued scalar observation time t.

• An NVARS-by-1 state vector X_t.

Data Types: double | function

'StorePaths' — Flag that indicates how the output array Paths is stored and returned

True (default) | logical with values True or False

Flag that indicates how the output array Paths is stored and returned, specified as the comma-separated pair consisting of 'StorePaths' and a scalar logical flag with a value of True or False.

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.

Data Types: logical

'Processes' — Sequence of end-of-period processes or state vector adjustments of the form

simBySolution makes no adjustments and performs no processing (default) | function | cell array of functions

Sequence of end-of-period processes or state vector adjustments of the form, specified as the comma-separated pair consisting of 'Processes' and a function or cell array of functions of the form

$$X_t=P(t,X_t)$$

The simBySolution function runs processing functions at each interpolation time. They must accept the current interpolation time t, and the current state vector X_t, and return a state vector that may be an adjustment to the input state.

simBySolution applies processing functions at the end of each observation period. These functions must accept the current observation time t and the current state vector X_t, and return a state vector that may be an adjustment to the input state.

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 X_t for an all-NaN condition. Thus, to signal an early termination of a given trial, all elements of the state vector X_t 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).

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.

Data Types: cell | function

Output Arguments

Paths — Simulated paths of correlated state variables

array

Simulated paths of correlated state variables, returned as a (NPERIODS + 1)-by-NVARS-by-NTRIALS three-dimensional time series array.

For a given trial, each row of Paths is the transpose of the state vector X_t at time t. When the input flag StorePaths = False, simBySolution returns Paths as an empty matrix.

Times — Observation times associated with the simulated paths

column vector

Observation times associated with the simulated paths, returned as a (NPERIODS + 1)-by-1 column vector. Each element of Times is associated with the corresponding row of Paths.

Z — Dependent random variates used to generate the Brownian motion vector

array

Dependent random variates used to generate the Brownian motion vector (Wiener processes) that drive the simulation, returned as a (NPERIODS * NSTEPS)-by-NBROWNS-by-NTRIALS three-dimensional time series array.

More About

Antithetic Sampling

Simulation methods allow you to specify a popular variance reduction technique called antithetic sampling.

This technique attempts to replace one sequence of random observations with another of the same expected value, but smaller variance. In a typical Monte Carlo simulation, each sample path is independent and represents an independent trial. However, antithetic sampling generates sample paths in pairs. The first path of the pair is referred to as the primary path, and the second as the antithetic path. Any given pair is independent of any other pair, but the two paths within each pair are highly correlated. Antithetic sampling literature often recommends averaging the discounted payoffs of each pair, effectively halving the number of Monte Carlo trials.

This technique attempts to reduce variance by inducing negative dependence between paired input samples, ideally resulting in negative dependence between paired output samples. The greater the extent of negative dependence, the more effective antithetic sampling is.

Algorithms

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) by an approximation of the closed-form solution.

Consider a separable, vector-valued HWV model of the form:

$$d{X}_t=S(t)[L(t)-X_t]dt+V(t)d{W}_t$$

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.

The simBySolution method simulates the state vector X_t 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 X_t is sampled.

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.

References

[1] 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.

[2] Ait-Sahalia, Y. “Transition Densities for Interest Rate and Other Nonlinear Diffusions.” The Journal of Finance, Vol. 54, No. 4, August 1999.

[3] Glasserman, P. Monte Carlo Methods in Financial Engineering. New York, Springer-Verlag, 2004.

[4] Hull, J. C. Options, Futures, and Other Derivatives, 5th ed. Englewood Cliffs, NJ: Prentice Hall, 2002.

[5] Johnson, N. L., S. Kotz, and N. Balakrishnan. Continuous Univariate Distributions. Vol. 2, 2nd ed. New York, John Wiley & Sons, 1995.

[6] Shreve, S. E. Stochastic Calculus for Finance II: Continuous-Time Models. New York: Springer-Verlag, 2004.