Description Usage Arguments Details Author(s) References Examples
Financial options based on scalar geometric Brownian motion and Heston models, similar to Mike Giles' original 2008 Operations Research paper, using an Euler-Maruyama discretisation
1 | opre_l(l, N, option)
|
l |
the level to be simulated. |
N |
the number of samples to be computed. |
option |
the option type, between 1 and 5. The options are:
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This function is based on GPL-2 'Matlab' code by Mike Giles.
Louis Aslett <aslett@stats.ox.ac.uk>
Mike Giles <Mike.Giles@maths.ox.ac.uk>
Tigran Nagapetyan <nagapetyan@stats.ox.ac.uk>
M.B. Giles. Multilevel Monte Carlo path simulation. Operations Research, 56(3):607-617, 2008.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | ## Not run:
# These are similar to the MLMC tests for the original
# 2008 Operations Research paper, using an Euler-Maruyama
# discretisation with 4^l timesteps on level l.
#
# The differences are:
# -- the plots do not have the extrapolation results
# -- two plots are log_2 rather than log_4
# -- the new MLMC driver is a little different
# -- switch to X_0=100 instead of X_0=1
M <- 4 # refinement cost factor
N0 <- 1000 # initial samples on coarse levels
Lmin <- 2 # minimum refinement level
Lmax <- 6 # maximum refinement level
test.res <- list()
for(option in 1:5) {
if(option==1) {
cat("\n ---- Computing European call ---- \n")
N <- 2000000 # samples for convergence tests
L <- 5 # levels for convergence tests
Eps <- c(0.005, 0.01, 0.02, 0.05, 0.1)
} else if(option==2) {
cat("\n ---- Computing Asian call ---- \n")
N <- 2000000 # samples for convergence tests
L <- 5 # levels for convergence tests
Eps <- c(0.005, 0.01, 0.02, 0.05, 0.1)
} else if(option==3) {
cat("\n ---- Computing lookback call ---- \n")
N <- 2000000 # samples for convergence tests
L <- 5 # levels for convergence tests
Eps <- c(0.01, 0.02, 0.05, 0.1, 0.2)
} else if(option==4) {
cat("\n ---- Computing digital call ---- \n")
N <- 3000000 # samples for convergence tests
L <- 5 # levels for convergence tests
Eps <- c(0.02, 0.05, 0.1, 0.2, 0.5)
} else if(option==5) {
cat("\n ---- Computing Heston model ---- \n")
N <- 2000000 # samples for convergence tests
L <- 5 # levels for convergence tests
Eps <- c(0.005, 0.01, 0.02, 0.05, 0.1)
}
test.res[[option]] <- mlmc.test(opre_l, M, N, L, N0, Eps, Lmin, Lmax, option=option)
# print exact analytic value, based on S0=K
T <- 1
r <- 0.05
sig <- 0.2
K <- 100
d1 <- (r+0.5*sig^2)*T / (sig*sqrt(T))
d2 <- (r-0.5*sig^2)*T / (sig*sqrt(T))
if(option==1) {
val <- K*( pnorm(d1) - exp(-r*T)*pnorm(d2) )
cat(sprintf("\n Exact value: %f, MLMC value: %f \n", val, test.res[[option]]$P[1]))
} else if(option==3) {
k <- 0.5*sig^2/r
val <- K*( pnorm(d1) - pnorm(-d1)*k - exp(-r*T)*(pnorm(d2) - pnorm(d2)*k) )
cat(sprintf("\n Exact value: %f, MLMC value: %f \n", val, test.res[[option]]$P[1]))
} else if(option==4) {
val <- K*exp(-r*T)*pnorm(d2)
cat(sprintf("\n Exact value: %f, MLMC value: %f \n", val, test.res[[option]]$P[1]))
}
# plot results
plot(test.res[[option]])
}
## End(Not run)
# The level sampler can be called directly to retrieve the relevant level sums:
opre_l(l=7, N=10, option=1)
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