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Class JointStochasticProcess

Hierarchy

Implements

Index

Classes

Constructors

Properties

Methods

Constructors

constructor

Properties

Private _correlationCache

_correlationCache: Map<string, Matrix> = new Map<string, Matrix>()

_discretization

_discretization: discretization

Private _factors

_factors: Size

_isDisposed

_isDisposed: boolean = false

Protected _l

_l: StochasticProcess[]

Private _modelFactors

_modelFactors: Size

_observables

_observables: Set<Observable> = new Set()

_observers

_observers: Set<Observer> = new Set()

Private _size

_size: Size

Private _vfactors

_vfactors: Size[]

Private _vsize

_vsize: Size[]

dispose

dispose: () => void

Type declaration

    • (): void

    • Returns void

isDisposed

isDisposed: boolean

notifyObservers

notifyObservers: () => void

Type declaration

    • (): void

    • Returns void

registerObserver

registerObserver: (o: Observer) => void

Type declaration

registerWith

registerWith: (h: Observable) => void

Type declaration

registerWithObservables

registerWithObservables: (o: Observer) => void

Type declaration

unregisterObserver

unregisterObserver: (o: Observer) => void

Type declaration

unregisterWith

unregisterWith: (h: Observable) => Size

Type declaration

unregisterWithAll

unregisterWithAll: () => void

Type declaration

    • (): void

    • Returns void

Methods

apply1

constituents

correlationIsStateDependent

  • correlationIsStateDependent(): boolean

  • Returns boolean

covariance

crossModelCorrelation

  • Parameters

    Returns Matrix

deepUpdate

  • deepUpdate(): void

diffusion1

drift1

evolve1

expectation1

factors

init

initialValues

  • initialValues(): Real[]

numeraire

postEvolve

  • Parameters

    Returns Real[]

preEvolve

  • Parameters

    Returns void

size

Protected slice

  • Parameters

    Returns Real[]

stdDeviation

  • Parameters

    Returns Matrix

stdDeviation1

  • returns the standard deviation $$ S(\mathrm{x}{t_0 + \Delta t} | \mathrm{x}{t_0} = \mathrm{x}_0) $$ of the process after a time interval $ \Delta t $ according to the given discretization. This method can be overridden in derived classes which want to hard-code a particular discretization.

    Parameters

    Returns Matrix

time

  • time(date: Date): Time

update

  • update(): void