Documentation Center

  • Trials
  • Product Updates

capbylg2f

Price cap using Linear Gaussian two-factor model

Syntax

  • CapPrice = capbylg2f(ZeroCurve,a,b,sigma,eta,rho,Strike,
    Maturity)
    example
  • CapPrice = capbylg2f(___, Name,Value) example

Description

example

CapPrice = capbylg2f(ZeroCurve,a,b,sigma,eta,rho,Strike,
Maturity)
returns cap price for a two-factor additive Gaussian interest-rate model.

example

CapPrice = capbylg2f(___, Name,Value) returns cap price for a two-factor additive Gaussian interest-rate model using optional name-value pairs.

Examples

expand all

Price a Cap Using a Linear Gaussian Two-Factor Model

Define the ZeroCurve, a, b, sigma, eta, and rho parameters to price the cap.

Settle = datenum('15-Dec-2007');

ZeroTimes = [3/12 6/12 1 5 7 10 20 30]';
ZeroRates = [0.033 0.034 0.035 0.040 0.042 0.044 0.048 0.0475]';
CurveDates = daysadd(Settle,360*ZeroTimes);

irdc = IRDataCurve('Zero',Settle,CurveDates,ZeroRates);

a = .07;
b = .5;
sigma = .01;
eta = .006;
rho = -.7;

CapMaturity = daysadd(Settle,360*[1:5 7 10 15 20 25 30],1);

Strike = [0.035 0.037 0.038 0.039 0.040 0.042 0.044 0.046 0.047 0.047 0.047]';

Price = capbylg2f(irdc,a,b,sigma,eta,rho,Strike,CapMaturity)
Price =

    0.0316
    0.3225
    0.7761
    1.3240
    1.9394
    3.1247
    4.8451
    7.3752
    9.8582
   11.4673
   12.7850

Input Arguments

expand all

ZeroCurve — Zero-curve for Linear Gaussian two-factor modelstructure

Zero-curve for the Linear Gaussian two-factor model, specified using IRDataCurve or RateSpec.

Data Types: struct

a — Mean reversion for first factor for Linear Gaussian two-factor modelscalar

Mean reversion for first factor for the Linear Gaussian two-factor model, specified as a scalar.

Data Types: single | double

b — Mean reversion for second factor for Linear Gaussian two-factor modelscalar

Mean reversion for second factor for the Linear Gaussian two-factor model, specified as a scalar.

Data Types: single | double

sigma — Volatility for first factor for Linear Gaussian two-factor modelscalar

Volatility for first factor for the Linear Gaussian two-factor model, specified as a scalar.

Data Types: single | double

eta — Volatility for second factor for Linear Gaussian two-factor modelscalar

Volatility for second factor for the Linear Gaussian two-factor model, specified as a scalar.

Data Types: single | double

rho — Scalar correlation of the factorsscalar

Scalar correlation of the factors, specified as a scalar.

Data Types: single | double

Strike — Cap strike pricenonnegative integer | vector of nonnegative integers

Cap strike price, specified as a nonnegative integer using a NumCaps-by-1 vector.

Data Types: single | double

Maturity — Cap maturity datenonnegative integer | vector of nonnegative integers | string of dates

Cap maturity date, specified using a NumCaps-by-1 vector of serial date numbers or date strings.

Data Types: single | double | cell

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

Example: Price = capbylg2f(irdc,a,b,sigma,eta,rho,Strike,CapMaturity,'Reset',1,'Notional',100)

'Reset' — Frequency of cap payments per year2 (default) | positive integer from the set[1,2,3,4,6,12] | vector of positive integers from the set [1,2,3,4,6,12]

Frequency of cap payments per year, specified as positive integers for the values 1,2,4,6,12] in a NumCaps-by-1 vector.

Data Types: single | double

'Notional' — Notional value of cap 100 (default) | nonnegative integer | vector of nonnegative integers

Notional value of cap, specified as a nonnegative integer using a NumCaps-by-1 vector of notional amounts.

Data Types: single | double

Output Arguments

expand all

CapPrice — Cap pricescalar | vector

Expected prices of cap, returned as a scalar or an NumCaps-by-1 vector.

More About

expand all

Algorithms

The following defines the two-factor additive Gaussian interest rate model, given the ZeroCurve, a, b, sigma, eta, and rho parameters:

where is a two-dimensional Brownian motion with correlation ρ and ϕ is a function chosen to match the initial zero curve.

References

[1] Brigo, D. and F. Mercurio, Interest Rate Models - Theory and Practice, Springer Finance, 2006.

See Also

| |

Was this topic helpful?