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178 lines (155 loc) · 5.48 KB
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# Chapter 5 code ----------------------------------------------------------
# Last updated: Aug 29, 2017
set.seed(19930225)
numTime = 50
npath = 1000 # number of sample paths
# test performance of the EM scheme using 4 sets of parameters
lambda0_1 = 0.3
kappa1 = 0.1
sigma1 = 2
theta1 = 0.4
t0 = 0
t = 1
cir_paths = replicate(npath,
EM_cir(
n = numTime,
kappa = kappa1,
sigma = sigma1,
theta = theta1,
start = lambda0_1,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
pct_neg = sum(colSums(is.na(cir_paths)) > 0)/npath
pct_neg # find the percentage of cir paths that at one point become negative
lambda0_2 = 0.1
kappa2 = 0.2
sigma2 = 1.2
theta2 = 0.2
cir_paths = replicate(npath,
EM_cir(
n = numTime,
kappa = kappa2,
sigma = sigma2,
theta = theta2,
start = lambda0_2,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
pct_neg = sum(colSums(is.na(cir_paths)) > 0)/npath
pct_neg # find the percentage of cir paths that at one point become negative
lambda0_3 = 0.05
kappa3 = 0.4
sigma3 = 1
theta3 = 0.1
cir_paths = replicate(npath,
EM_cir(
n = numTime,
kappa = kappa3,
sigma = sigma3,
theta = theta3,
start = lambda0_3,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
pct_neg = sum(colSums(is.na(cir_paths)) > 0)/npath
pct_neg # find the percentage of cir paths that at one point become negative
lambda0_4 = 0.01
kappa4 = 0.6
sigma4 = 0.14
theta4 = 0.02
cir_paths = replicate(npath,
EM_cir(
n = numTime,
kappa = kappa4,
sigma = sigma4,
theta = theta4,
start = lambda0_4,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
pct_neg = sum(colSums(is.na(cir_paths)) > 0)/npath
pct_neg # find the percentage of cir paths that at one point become negative
# test performance of the reflected EM scheme
cir_paths = replicate(npath,
refEM_cir(
n = numTime,
kappa = kappa1,
sigma = sigma1,
theta = theta1,
start = lambda0_1,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
cir_paths = replicate(npath,
refEM_cir(
n = numTime,
kappa = kappa2,
sigma = sigma2,
theta = theta2,
start = lambda0_2,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
cir_paths = replicate(npath,
refEM_cir(
n = numTime,
kappa = kappa3,
sigma = sigma3,
theta = theta3,
start = lambda0_3,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
cir_paths = replicate(npath,
refEM_cir(
n = numTime,
kappa = kappa4,
sigma = sigma4,
theta = theta4,
start = lambda0_4,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
# test performance of the step-by-step direct sampling
cir_paths = replicate(npath,
chi_sampling(
n = numTime,
kappa = kappa1,
sigma = sigma1,
theta = theta1,
start = lambda0_1,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
cir_paths = replicate(npath,
chi_sampling(
n = numTime,
kappa = kappa2,
sigma = sigma2,
theta = theta2,
start = lambda0_2,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
cir_paths = replicate(npath,
chi_sampling(
n = numTime,
kappa = kappa3,
sigma = sigma3,
theta = theta3,
start = lambda0_3,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
cir_paths = replicate(npath,
chi_sampling(
n = numTime,
kappa = kappa4,
sigma = sigma4,
theta = theta4,
start = lambda0_4,
t0 = t0,
t = t))
mean(cir_paths[(numTime+1),1:npath],na.rm=T)
# end of chapter 5 code