# Uses of Classnet.finmath.montecarlo.interestrate.models.covariance.AbstractLIBORCovarianceModel

Package
Description
Contains covariance models and their calibration as plug-ins for the LIBOR market model and volatility and correlation models which may be used to build a covariance model.
• ## Uses of AbstractLIBORCovarianceModel in net.finmath.montecarlo.interestrate.models.covariance

Modifier and Type
Class
Description
class
AbstractLIBORCovarianceModelParametric
Base class for parametric covariance models, see also AbstractLIBORCovarianceModel.
class
BlendedLocalVolatilityModel
Blended model (or displaced diffusion model) build on top of a standard covariance model.
class
DisplacedLocalVolatilityModel
Displaced model build on top of a standard covariance model.
class
ExponentialDecayLocalVolatilityModel
Exponential decay model build on top of a given covariance model.
class
HullWhiteLocalVolatilityModel
Special variant of a blended model (or displaced diffusion model) build on top of a standard covariance model using the special function corresponding to the Hull-White local volatility.
class
LIBORCovarianceModelBH
A five parameter covariance model corresponding.
class
LIBORCovarianceModelExponentialForm5Param
class
LIBORCovarianceModelExponentialForm7Param

class
LIBORCovarianceModelFromVolatilityAndCorrelation
A covariance model build from a volatility model implementing LIBORVolatilityModel and a correlation model implementing LIBORCorrelationModel.
class
LIBORCovarianceModelStochasticHestonVolatility
As Heston like stochastic volatility model, using a process $$\lambda(t) = \sqrt(V(t))$$ $dV(t) = \kappa ( \theta - V(t) ) dt + \xi \sqrt{V(t)} dW_{1}(t), \quad V(0) = 1.0,$ where $$\lambda(0) = 1$$ to scale all factor loadings $$f_{i}$$ returned by a given covariance model.
class
LIBORCovarianceModelStochasticVolatility
Simple stochastic volatility model, using a process $d\lambda(t) = \nu \lambda(t) \left( \rho \mathrm{d} W_{1}(t) + \sqrt{1-\rho^{2}} \mathrm{d} W_{2}(t) \right) \text{,}$ where $$\lambda(0) = 1$$ to scale all factor loadings $$f_{i}$$ returned by a given covariance model.