def test_simulate(self):
daycounter = ActualActual()
risk_free_ts = flat_rate(0.1, daycounter)
dividend_ts = flat_rate(0.04, daycounter)
s0 = s0 = SimpleQuote(100.0)
v0 = 0.05
kappa = 5.0
theta = 0.05
sigma = 1.0e-4
rho = 0.0
process = HestonProcess(risk_free_ts, dividend_ts, s0, v0, kappa, theta, sigma, rho)
# simulate and plot Heston paths
nbPaths = 4
nbSteps = 100
horizon = 1
seed = 12345
res = simulate(process, nbPaths, nbSteps, horizon, seed)
self.assertAlmostEqual(res[1, -1], 152.50, delta=0.1)
# <markdowncell>
# The simulation
# --------------
#
# The *simulate* function is not part of Quantlib. It has been added to the pyQL interface (see folder quantlib/sim). This illustrates how to crerate extensions to Quantlib and expose them to python.
# <codecell>
import pylab as pl
from quantlib.sim.simulate import simulate
# simulate and plot Heston paths
paths = 20
steps = 100
horizon = 2
seed = 12345
res = simulate(process, paths, steps, horizon, seed)
time = res[0,:]
simulations = res[1:, :].T
pl.plot(time, simulations)
pl.xlabel('Time')
pl.ylabel('Stock Price')
pl.title('Heston Process Simulation')
show()
# <markdowncell>
# The simulation
# --------------
#
# The *simulate* function is not part of Quantlib. It has been added
# to the pyQL interface (see folder quantlib/sim). This illustrates
# how to create extensions to Quantlib and expose them to python.
# <codecell>
import pylab as pl
from quantlib.sim.simulate import simulate
# simulate and plot Heston paths
paths = 20
steps = 100
horizon = 2
seed = 12345
res = simulate(process, paths, steps, horizon, seed)
time = res[0, :]
simulations = res[1:, :].T
pl.plot(time, simulations)
pl.xlabel('Time')
pl.ylabel('Stock Price')
pl.title('Heston Process Simulation')
pl.show()
