def test_var_has_increasing_estimates_with_increasing_levels(self):
np.random.seed(0)
var_levels = [0.95, 0.98, 0.999]
drift = np.asarray([0.1, 0.3, -0.05], dtype=np.float32)
vol_matrix = 0.2 * np.asarray(
[[1.5, 0.2, 0.3], [0.2, 1.1, -0.1], [0.3, -0.1, 0.8]],
dtype=np.float32)
maturity = 1.0
dt = 0.01
num_samples = 8
num_dims = drift.shape[0]
key_placeholder = tf.placeholder(shape=(), dtype=tf.int32)
def _dynamics_op(s, t, dt):
return dynamics.gbm_euler_step_nd(s, drift, vol_matrix, t, dt)
initial_state = tf.ones([num_dims]) * 100.0
initial_pf_value = tf.reduce_sum(initial_state)
payoff_fn = lambda s: tf.reduce_sum(s, axis=-1) - initial_pf_value
_, _, portfolio_returns = monte_carlo_manager.non_callable_price_mc(
initial_state=initial_state,
dynamics_op=_dynamics_op,
payoff_fn=payoff_fn,
maturity=maturity,
num_samples=num_samples,
dt=dt)
var_ests, cvar_ests = risk_metrics.var_and_cvar(
portfolio_returns, var_levels, key_placeholder, {})
self.assertLen(var_ests, len(var_levels))
self.assertLen(cvar_ests, len(var_levels))
for i in range(len(var_ests) - 1):
self.assertLessEqual(var_ests[i], var_ests[i + 1])
self.assertLessEqual(cvar_ests[i], cvar_ests[i + 1])
def test_var_in_normal_case(self):
np.random.seed(0)
var_levels = [0.95, 0.98, 0.999]
key_placeholder = tf.placeholder(shape=(), dtype=tf.int32)
num_samples = int(1e6)
maturity = 1.0
dt = 1.0
def _dynamics_op(unused_s, t, dt):
return dynamics.random_normal([num_samples], t, dt)
payoff_fn = tf.identity
initial_state = tf.constant(0.0)
_, _, portfolio_returns = monte_carlo_manager.non_callable_price_mc(
initial_state=initial_state,
dynamics_op=_dynamics_op,
payoff_fn=payoff_fn,
maturity=maturity,
num_samples=num_samples,
dt=dt)
var_ests, cvar_ests = risk_metrics.var_and_cvar(
portfolio_returns, var_levels, key_placeholder, {})
self.assertLen(var_ests, len(var_levels))
self.assertLen(cvar_ests, len(var_levels))
for i in range(len(var_ests) - 1):
self.assertLessEqual(var_ests[i], var_ests[i + 1])
self.assertLessEqual(cvar_ests[i], cvar_ests[i + 1])
for i, var_level in enumerate(var_levels):
self.assertAlmostEqual(var_ests[i],
-scipy.stats.norm.isf(var_level),
delta=1e-2)