gaussLaguerrePricer: Laguerre quadrature pricing.

In piotrek-orlowski/transformOptionPricer: Characteristic-function based option pricer.

Description

Calculate option prices using a Laguerre quadrature of the difference between a reference characteristic function (Black-Scholes for high volatility) and user-defined characteristic function. Only European call and put options.

Usage

gaussLaguerrePricer(strikeMat, mkt, alpha = 0, N = 64,
  sigma.ref = NULL, N.factors = 3, charFun, preCalc = NULL, ...)

Arguments

strikeMat	array of size TxKxS of relative log-strikes
mkt	data.frame with T rows and fields: r – risk-free rate, q – dividend yield, t – option maturity.
alpha	parameter of the laguerre quadrature.
N	number of points of integration.
sigma.ref	variance (volatility squared) value for the reference characteristic function, length S. If not provided, ... will be checked for existence of a state matrix and rowSums of variance states will be taken.
N.factors	integer, number of stochastic volatility factors, argument for charFun. If your charFun doesn't accept such an argument, for example you're pricing in the Black-Scholes model, use N.factors = 0.
preCalc	optional a list containing preCalculated values of the characteristic function, and the quadrature parameters (useful if derivatives are reevaluated many times for different states, but the same parameter)
...	arguments to charFun required for pricing (state variables, parameters, etc.)

Details

In extensive tests this pricer yields results comparable to the Fourier-cosine series pricer, with fewer characteristic function evaluations. Care should be taken at very high values of variance factors.

Value

list of arrays of size T x K x S with relative prices of call options, put options, out of the money options.

piotrek-orlowski/transformOptionPricer documentation built on July 21, 2020, 11:51 a.m.