sim.logOU_Discrete: Simulate from 1-D discretized exponential Ornstein Uhlenbeck...

In mludkov/mlOSP: Machine Learning and Regression Monte Carlo Algorithms for Optimal Stopping

Simulate from 1-D discretized exponential Ornstein Uhlenbeck process See Bender (SIFIN 2011). Only works in one dimension

Description

Simulate from 1-D discretized exponential Ornstein Uhlenbeck process See Bender (SIFIN 2011). Only works in one dimension

Usage

sim.logOU_Discrete(x0, model, dt = model$dt)

Arguments

x0	is the starting values (matrix of size N x model$dim)
model	a list containing all the other parameters, including volatility model$sigma, interest rate model$r and continuous dividend yield model$div.
dt	is the step size in time. Defaults to model$dt

Details

Requires

• model$rho – similar to mean-reversion rate, should be close to 1

• model$mu – mean-reversion level

• model$sigma – volatility

mludkov/mlOSP documentation built on April 29, 2023, 7:56 p.m.