def u(self, _t, _x):
mean = (self.a - 0.5 * self.b * self.b) * _t
variance = self.b * self.b * _t
nd = dist.NormalDistribution(mean, variance)
strk = np.log(self.strike / _x)
return 1 - nd.cdf(strk)
def u(self, _t, _x):
nd = dist.NormalDistribution(_x, _t)
return nd.excess_probability(self._strike)
def u(self, _t, _x):
nd = dist.NormalDistribution(_x, _t)
return nd.expected_positive_exposure()
def u(self, _t, _x):
nd = dist.NormalDistribution(_x, _t)
return nd.second_moment()
def u(self, _t, _x):
nd = dist.NormalDistribution(_x, _t)
return nd.pdf(self._start)