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sdeddo

Construct `sdeddo` model from Drift and Diffusion objects

Synopsis

`SDE = sdeddo(DriftRate, DiffusionRate)`

```SDE = sdeddo(DriftRate, DiffusionRate, 'Name1', Value1, 'Name2', Value2, ...)```

`sdeddo`

Description

This constructor creates and displays `sdeddo` objects, specifically instantiated with objects of class`drift` and `diffusion`. These restricted `sdeddo` objects contain the input `drift` and `diffusion` objects; therefore, you can directly access their displayed parameters.

This abstraction also generalizes the notion of drift and diffusion-rate objects as functions that `sdeddo` evaluates for specific values of time t and state Xt. Like`sde` objects, `sdeddo` objects allow you to simulate sample paths of `NVARS` state variables driven by `NBROWNS` Brownian motion sources of risk over `NPERIODS` consecutive observation periods, approximating continuous-time stochastic processes.

This method enables you to simulate any vector-valued SDE of the form:

 $d{X}_{t}=F\left(t,{X}_{t}\right)dt+G\left(t,{X}_{t}\right)d{W}_{t}$ (18-6)
where:

• Xt is an `NVARS`-by-`1` state vector of process variables.

• dWt is an `NBROWNS`-by-`1` Brownian motion vector.

• F is an `NVARS`-by-`1` vector-valued drift-rate function.

• G is an `NVARS`-by-`NBROWNS` matrix-valued diffusion-rate function.

Input Arguments

 `DriftRate` Object of class`drift` that encapsulates a user-defined drift-rate specification, represented as F. `DiffusionRate` Object of class `diffusion` that encapsulates a user-defined diffusion-rate specification, represented as G.

Optional Input Arguments

Specify optional inputs as matching parameter name/value pairs as follows:

• Specify the parameter name as a character vector, 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 character vector 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, `sdeddo` applies the same initial value to all state variables on all trials.If `StartState` is a column vector, `sdeddo` applies a unique initial value to each state variable on all trials. If `StartState` is a matrix, `sdeddo` 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

 `SDE` Object of class `sdeddo` with the following 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 `A`: Access function for the drift-rate property `A`, callable as a function of time and state `B`: Access function for the drift-rate property `B`, callable as a function of time and state `Alpha`: Access function for the diffusion-rate property `Alpha`, callable as a function of time and state `Sigma`: Access function for the diffusion-rate property `Sigma`, callable as a function of time and state `Simulation`: A simulation function or method

Algorithms

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 Xt, and return an array of appropriate dimension. Even if you originally specified an input as an array, `sdeddo` treats as a static function of time and state, by that means guaranteeing that all parameters are accessible by the same interface.

References

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.

Ait-Sahalia, 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, Springer-Verlag, 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: Continuous-Time Models. New York: Springer-Verlag, 2004.

Introduced in R2008a

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