stochcorr (version 0.0.1)

stochcorr.sim: Simulate stochastic correlation model

Description

stochcorr.sim returns the paths of stock price under a stochastic correlation model

Usage

stochcorr.sim(m=500, n, dt, S1_0, S2_0, mu1, sigma1, mu2, sigma2,
mu, lambda, sigma, corr_process)

Value

Returns a list with prices of two assets S1 and S2 under the stochastic correlation model

Arguments

m

number of paths (Default is 500)

n

number of steps in each simulated path

dt

time step

S1_0

initial price of the first asset

S2_0

initial price of the second asset

mu1

drift of the first asset

sigma1

volatility of the first asset

mu2

drift of the second asset

sigma2

volatility of the second asset

mu

mean direction of the correlation process (if corr_process=vmp)

lambda

drift of the correlation process (if corr_process=vmp)

sigma

volatility of the correlation process (if corr_process=vmp or corr_process=cbm)

corr_process

specify the correlation process, vmp for von Mises process or cbm for Circular Brownian Motion

Details

This function returns the simulated paths of two stock prices following a stochastic correlation model. See stochcorr() details of the stochastic correlation model

Examples

library(stochcorr)
# Generate 500 paths of two geometric Brownian motions, S1 and S2, of length 100 each
# following the von Mises process with mu=pi/2, lambda=1 and sigma =1

a<-stochcorr.sim(m=500,100,0.01,100,100,0.05,0.05,0.06,0.1,pi/2,1,1,"vmp")
t<-seq(0,100*0.01-0.01,0.01)

# Plot the first realization of S1 and S2

plot(t,a$S1[1,], ylim=c(min(a$S1[1,],a$S2[1,]),max(a$S1[1,],a$S2[1,])),type="l")
lines(t,a$S2[1,], col="red",type="l")
legend(0.01,max(a$S1[1,],a$S2[1,]), legend = c("S1","S2"), col = c("black", "red"), lty=1)