f.beta: Mixture Distribution of Outer Scenarios

In chenqi57/GreenSim: Nested Green Simulation

Description

Calculate the density of mixture distribution, consisting each outer scenario distibution, with given weights.

Usage

f.beta(x, beta, outer, df)

Arguments

x	vector of random variables.
beta	vecotr of weights for each outer scenario distribution.
outer	vector of parameters simulated in outer scenario.
df	density functions for the class of distributions inner simulation random variables follow.

Details

The MLR density is equivalent to having beta as equal wights.

Value

Density of the input random variable vector.

Examples

library(functional)
r = 5e-2    # risk-free rate
S0 = 100    # initial stock price
vol = 30e-2 # annual volatility

tau = 1/12    # one month
T = 1         # time to maturity (from time 0)

N_Out = 10     # number of outer samples

T2M = T - tau

min <- qlnorm(1e-4, meanlog = (r-0.5*vol^2)*tau + log(S0), sdlog = (vol*sqrt(tau)))
max <- qlnorm(1-1e-4, meanlog = (r-0.5*vol^2)*tau + log(S0), sdlog = (vol*sqrt(tau)))
S_tau <- seq(from = min, to = max, length.out = N_Out)
mu <- log(S_tau) + (r-0.5*vol^2)*T2M
sig <- vol * sqrt(T2M)
df <- Curry(dnorm, sd = sig)

x <- seq(from = 4, to = 6, length.out = 10)
beta <- rep(1/N_Out, N_Out)

f.beta(x, beta, mu, df)

chenqi57/GreenSim documentation built on Dec. 19, 2021, 3:04 p.m.