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Hierarchy

Implements

Index

Constructors

constructor

Properties

_cachedMktFactor

_cachedMktFactor: Handle<Quote>

_copula

_copula: any

_factorWeights

_factorWeights: Real[][] = [[]]

_idiosyncFctrs

_idiosyncFctrs: Real[] = []

_isDisposed

_isDisposed: boolean = false

_nFactors

_nFactors: Size

_nVariables

_nVariables: Size

_observables

_observables: Set<Observable> = new Set()

_observers

_observers: Set<Observer> = new Set()

copulaPolicy

copulaPolicy: any

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

allFactorCumulInverter

  • allFactorCumulInverter(probs: Real[]): Real[]

conditionalDefaultProbability1

conditionalDefaultProbability2

conditionalDefaultProbabilityInvP

conditionalExpLossRR

  • conditionalExpLossRR(d: Date, iName: Size, mktFactors: Real[]): Real

conditionalExpLossRRInv

conditionalRecovery

  • conditionalRecovery(latentVarSample: Real, iName: Size, d: Date): Real

conditionalRecoveryInvPinvRR

copula

  • copula(): any
  • Returns any

cumulativeY

cumulativeZ

deepUpdate

  • deepUpdate(): void

density

expCondRecovery

  • expCondRecovery(d: Date, iName: Size, mktFactors: Real[]): Real
  • Expected conditional spot recovery rate. Conditional on a set of systemic factors and default returns the integrated attainable recovery values. \par Corresponds to a multifactor generalization of the model in eq. 44 on p.15 of Extension of Spot Recovery Model for Gaussian Copula Hui Li. 2009 Only remember that $\rho_l Z $ there is here (multiple betas): $ \sum_k \beta_{ik}^l Z_k $ and that $ \rho_d \rho_l $ there is here: $ \sum_k \beta_{ik}^d \beta_{ik}^l $ \par (d,l corresponds to first and last set of betas)

    Parameters

    • d: Date
    • iName: Size
    • mktFactors: Real[]

    Returns Real

expCondRecoveryInvPinvRR

  • expCondRecoveryInvPinvRR(invUncondDefP: Real, invUncondRR: Real, iName: Size, mktFactors: Real[]): Real

expCondRecoveryP

expectedLoss

  • expectedLoss(d: Date, iName: Size): Real
  • Single name expected loss.\par The main reason of this method is for the testing of this model. The model is coherent in that it preserves the single name expected loss and thus is coherent with the single name CDS market when used in the pricing context. i.e. it should match: $pdef_i(d) \times RR_i $

    Parameters

    • d: Date
    • iName: Size

    Returns Real

factorWeights

  • factorWeights(): Real[][]

idiosyncFctrs

  • idiosyncFctrs(): Real[]

init

  • Parameters

    • copulaPolicyImpl: any

    Returns LatentModel

integratedExpectedValue1

integratedExpectedValue2

integration

inverseCumulativeDensity

inverseCumulativeY

inverseCumulativeZ

latentRRVarValue

latentVarValue

latentVariableCorrel

lmInit1

  • Parameters

    • factorWeights: Real[][]
    • Default value ini: any = null

    Returns LatentModel

lmInit2

  • Parameters

    • factorWeights: Real[]
    • Default value ini: any = null

    Returns LatentModel

lmInit3

lmInit4

numFactors

  • numFactors(): Size

numTotalFactors

  • numTotalFactors(): Size

resetBasket

  • resetBasket(basket: Basket): void

size

  • Returns Size

srlmInit

update

  • update(): void