def generate_paths(self, fixed_seed=False, day_count=365.0):
if self.time_grid is None:
self.generate_time_grid()
M = len(self.time_grid)
I = self.paths
paths = np.zeros((M, I))
paths[0] = self.initial_value
if self.correlated is False:
sn1 = sn_random_numbers((1, M, I), fixed_seed=fixed_seed)
else:
sn1 = self.random_numbers
# standard normally distributed pseudorandom numbers for the jump component
sn2 = sn_random_numbers((1, M, I), fixed_seed=fixed_seed)
rj = self.lamb * (np.exp(self.mu + 0.5 * self.delt ** 2) - 1)
short_rate = self.discount_curve.short_rate
for t in range(1, len(self.time_grid)):
if self.correlated is False:
ran = sn1[t]
else:
ran = np.dot(self.cholesky_matrix, sn1[:, t, :])
ran = ran[self.rn_set]
dt = (self.time_grid[t] - self.time_grid[t - 1]).days / day_count
poi = np.random.poisson(self.lamb * dt, I)
paths[t] = paths[t - 1] * (np.exp((short_rate - rj
- 0.5 * self.volatility ** 2) * dt
+ self.volatility * np.sqrt(dt) * ran)
+ (np.exp(self.mu + self.delt * sn2[t]) - 1) * poi)
self.instrument_values = paths
def generate_paths(self, fixed_seed=False, day_count=365.0):
if self.time_grid is None:
self.generate_time_grid()
# number of dates for time grid
M = len(self.time_grid)
I = self.paths
paths = np.zeros((M, I))
paths[0] = self.initial_value
if not self.correlated:
rand = sn_random_numbers((1, M, I), fixed_seed=fixed_seed)
else:
rand = self.random_numbers
short_rate = self.discount_curve.short_rate
for t in range(1, len(self.time_grid)):
if not self.correlated:
ran = rand[t]
else:
ran = np.dot(self.cholesky_matrix, rand[:, t, :])
ran = ran[self.rn_set]
dt = (self.time_grid[t] - self.time_grid[t - 1]).days / day_count
paths[t] = paths[t - 1] * np.exp((short_rate - 0.5
* self.volatility ** 2) * dt
+ self.volatility * np.sqrt(dt) * ran)
self.instrument_values = paths