shortrate (3) - Linux Manuals


Short-rate modelling framework -


class AffineModel
Affine model class.
class TermStructureConsistentModel
Term-structure consistent model class.
class ShortRateModel
Abstract short-rate model class.
class OneFactorModel
Single-factor short-rate model abstract class.
class OneFactorAffineModel
Single-factor affine base class.
class BlackKarasinski
Standard Black-Karasinski model class.
class CoxIngersollRoss
Cox-Ingersoll-Ross model class.
class ExtendedCoxIngersollRoss
Extended Cox-Ingersoll-Ross model class.
class HullWhite
Single-factor Hull-White (extended Vasicek) model class.
class Vasicek
Vasicek model class
class TwoFactorModel
Abstract base-class for two-factor models.
class G2
Two-additive-factor gaussian model class.

Detailed Description

This framework (corresponding to the ql/ShortRateModels directory) implements some single-factor and two-factor short rate models. The models implemented in this library are widely used by practitionners. For the moment, the ShortRateModels::Model class defines the short-rate dynamics with stochastic equations of the type [ dx_i = mu(t,x_i) dt + igma(t,x_i) dW_t ] where $ r = f(t,x) $. If the model is affine (i.e. derived from the QuantLib::AffineModel class), analytical formulas for discount bonds and discount bond options are given (useful for calibration).

Single-factor models

The Hull & White model.RS 4 [ dr_t = ( heta(t) - lpha(t) r_t)dt + igma(t) dW_t ] When $ lpha $ and $ igma $ are constants, this model has analytical formulas for discount bonds and discount bond options.

The Black-Karasinski model.RS 4 [ d

ded Cox-Ingersoll-Ross model.RS 4 [ dr_t = ( heta(t) - k r_t)dt + igma qrt{r_t} dW_t ] There are analytical formulas for discount bonds (and soon for discount bond options).


The class CalibrationHelper is a base class that facilitates the instanciation of market instruments used for calibration. It has a method marketValue() that gives the market price using a Black formula, and a modelValue() method that gives the price according to a model

Derived classed are QuantLib::CapHelper and QuantLib::SwaptionHelper.

For the calibration itself, you must choose an optimization method that will find constant parameters such that the value: [ V = qrt{um_{i=1}^{n} ac{(T_i - M_i)^2}{M_i}}, ] where $ T_i $ is the price given by the model and $ M_i $ is the market price, is minimized. A few optimization methods are available in the ql/Optimization directory.

Two-factor models


Analytical pricers.RS 4

If the model is affine, i.e. discount bond options formulas exist, caps are easily priced since they are a portfolio of discount bond options. Such a pricer is implemented in QuantLib::AnalyticalCapFloor. In the case of single-factor affine models, swaptions can be priced using the Jamshidian decomposition, implemented in QuantLib::JamshidianSwaption.

Using Finite Differences.RS 4

(Doesn't work for the moment) For the moment, this is only available for single-factor affine models. If $ x = x(t, r) $ is the state variable and follows this stochastic process: [ dx_t = mu(t,x)dt + igma(t,x)dW_t ] any european-style instrument will follow the following PDE:

[ ac{
artial P}{
artial t} + mu ac{
artial P}{
artial x} + ac{1}{2} igma^2 ac{
artial^2 P}{
artial x^2} = r(t,x)P ].PP The adequate operator to feed a Finite Difference Model instance is defined in the QuantLib::OneFactorOperator class.

Using Trees.RS 4

Each model derived from the single-factor model class has the ability to return a trinomial tree. For yield-curve consistent models, the fitting parameter can be determined either analytically (when possible) or numerically. When a tree is built, it is then pretty straightforward to implement a pricer for any path-independant derivative. Just implement a class derived from NumericalDerivative (see QuantLib::NumericalSwaption for example) and roll it back until the present time... Just look at QuantLib::TreeCapFloor and QuantLib::TreeSwaption for working pricers.


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