diff options
Diffstat (limited to 'python/analytics')
| -rw-r--r-- | python/analytics/tranche_basket.py | 28 |
1 files changed, 20 insertions, 8 deletions
diff --git a/python/analytics/tranche_basket.py b/python/analytics/tranche_basket.py index 9b63ee97..e28a356c 100644 --- a/python/analytics/tranche_basket.py +++ b/python/analytics/tranche_basket.py @@ -1,7 +1,7 @@ from .basket_index import BasketIndex from .tranche_functions import ( credit_schedule, adjust_attachments, GHquad, BCloss_recov_dist, - BCloss_recov_trunc, tranche_cl, tranche_pl) + BCloss_recov_trunc, tranche_cl, tranche_pl, tranche_pl_trunc, tranche_cl_trunc) from .index_data import get_tranche_quotes from .utils import memoize, build_table from collections import namedtuple @@ -24,7 +24,8 @@ class DualCorrTranche(): attach: float, detach: float, corr_attach: float, corr_detach: float, tranche_running: float, notional: float=10_000_000, - value_date: pd.Timestamp=pd.Timestamp.today().normalize()): + value_date: pd.Timestamp=pd.Timestamp.today().normalize(), + use_trunc=False): self._index = BasketIndex(index_type, series, [tenor], value_date=value_date) def get_quotes(self, spread): @@ -52,6 +53,7 @@ class DualCorrTranche(): self.cs = credit_schedule(value_date, None, 1., self._index.yc, self._index.maturities[0]) self._accrued = cds_accrued(value_date, tranche_running * 1e-4) + self.use_trunc = use_trunc self._ignore_hash = set(['_Z', '_w', 'cs', '_cache', '_ignore_hash']) def _default_prob(self, epsilon=0.): @@ -111,13 +113,23 @@ class DualCorrTranche(): elif np.isnan(rho): raise ValueError("ρ needs to be a real number between 0. and 1.") else: - L, R = BCloss_recov_dist(self._default_prob(epsilon), - self._index.weights, - self._index.recovery_rates, - rho, - self._Z, self._w, self._Ngrid) Legs = namedtuple('TrancheLegs', 'coupon_leg, protection_leg') - return Legs(tranche_cl(L, R, self.cs, 0., K), tranche_pl(L, self.cs, 0., K)) + if self.use_trunc: + EL, ER = BCloss_recov_trunc(self._default_prob(epsilon), + self._index.weights, + self._index.recovery_rates, + rho, K, + self._Z, self._w, self._Ngrid) + return Legs(tranche_cl_trunc(EL, ER, self.cs, 0., K), + tranche_pl_trunc(EL, self.cs, 0., K)) + else: + L, R = BCloss_recov_dist(self._default_prob(epsilon), + self._index.weights, + self._index.recovery_rates, + rho, + self._Z, self._w, self._Ngrid) + return Legs(tranche_cl(L, R, self.cs, 0., K), + tranche_pl(L, self.cs, 0., K)) @property def direction(self): |