def price(self, valuation_date, market, model=None, name=None):
"""Returns the present value of the stream on the valuation date.
Args:
valuation_date: A scalar `DateTensor` specifying the date on which
valuation is being desired.
market: A namedtuple of type `InterestRateMarket` which contains the
necessary information for pricing the cashflow stream.
model: Reserved for future use.
name: Python str. The name to give to the ops created by this function.
Default value: `None` which maps to 'price'.
Returns:
A Rank 1 `Tensor` of real type containing the modeled price of each stream
contract based on the input market data.
"""
del model
name = name or (self._name + '_price')
with tf.name_scope(name):
discount_curve = market.discount_curve
reference_curve = market.reference_curve
libor_rate = rc.get_rate_index(market,
self._start_date,
rc.RateIndexType.LIBOR,
dtype=self._dtype)
libor_rate = tf.repeat(
tf.convert_to_tensor(libor_rate, dtype=self._dtype),
self._num_cashflows)
discount_factors = discount_curve.get_discount_factor(
self._payment_dates)
forward_rates = reference_curve.get_forward_rate(
self._accrual_start_date, self._accrual_end_date,
self._daycount_fractions)
forward_rates = tf.where(self._daycount_fractions > 0.,
forward_rates,
tf.zeros_like(forward_rates))
# If coupon end date is before the valuation date, the payment is in the
# past. If valuation date is between coupon start date and coupon end
# date, then the rate has been fixed but not paid. Otherwise the rate is
# not fixed and should be read from the curve.
forward_rates = tf.where(
self._coupon_end_dates < valuation_date,
tf.constant(0., dtype=self._dtype),
tf.where(self._coupon_start_dates < valuation_date, libor_rate,
forward_rates))
coupon_rate = self._coupon_multiplier * (forward_rates +
self._coupon_basis)
cashflow_pvs = self._notional * (self._daycount_fractions *
coupon_rate * discount_factors)
return tf.math.reduce_sum(tf.reshape(
cashflow_pvs, (self._batch_size, self._num_cashflows)),
axis=1)
def price(self, valuation_date, market, model=None, pricing_context=None,
name=None):
"""Returns the present value of the stream on the valuation date.
Args:
valuation_date: A scalar `DateTensor` specifying the date on which
valuation is being desired.
market: A namedtuple of type `InterestRateMarket` which contains the
necessary information for pricing the cashflow stream.
model: An optional input of type `InterestRateModelType` to specify which
model to use for pricing.
Default value: `None` in which case `NORMAL_RATE` model is used.
pricing_context: An optional input to provide additional parameters (such
as model parameters) relevant for pricing.
name: Python str. The name to give to the ops created by this function.
Default value: `None` which maps to 'price'.
Returns:
A Rank 1 `Tensor` of real type containing the modeled price of each stream
contract based on the input market data.
"""
name = name or (self._name + '_price')
with tf.name_scope(name):
valuation_date = dates.convert_to_date_tensor(valuation_date)
discount_curve = market.discount_curve
past_fixing = rc.get_rate_index(
market, self._start_date, rc.RateIndexType.SWAP, dtype=self._dtype)
past_fixing = tf.repeat(
tf.convert_to_tensor(past_fixing, dtype=self._dtype),
self._num_cashflows)
discount_factors = discount_curve.get_discount_factor(self._payment_dates)
cms_rates = self._swap.par_rate(valuation_date, market, model)
cms_rates = tf.where(self._daycount_fractions > 0., cms_rates,
tf.zeros_like(cms_rates))
# If coupon end date is before the valuation date, the payment is in the
# past. If valuation date is between coupon start date and coupon end
# date, then the rate has been fixed but not paid. Otherwise the rate is
# not fixed and should be read from the curve.
cms_rates = tf.where(
self._coupon_end_dates < valuation_date,
tf.constant(0., dtype=self._dtype),
tf.where(self._coupon_start_dates < valuation_date,
past_fixing, cms_rates))
cms_rates = self._adjust_convexity(
valuation_date, market, model, pricing_context, cms_rates,
discount_factors)
coupon_rate = self._coupon_multiplier * (
cms_rates + self._coupon_basis)
cashflow_pvs = self._notional * (
self._daycount_fractions * coupon_rate * discount_factors)
return tf.math.segment_sum(cashflow_pvs, self._contract_index)
def _get_forward_rate(self, valuation_date, market):
"""Returns the relevant forward rates from the market data."""
forward_rates = market.reference_curve.get_forward_rate(
self._accrual_start_dates,
self._accrual_end_dates,
self._daycount_fractions)
forward_rates = tf.where(self._daycount_fractions > 0.0, forward_rates,
tf.zeros_like(forward_rates))
libor_rate = rc.get_rate_index(
market, self._start_date, rc.RateIndexType.LIBOR, dtype=self._dtype)
libor_rate = tf.repeat(
tf.convert_to_tensor(libor_rate, dtype=self._dtype), self._num_caplets)
forward_rates = tf.where(
self._accrual_end_dates < valuation_date,
tf.constant(0., dtype=self._dtype),
tf.where(self._accrual_start_dates < valuation_date, libor_rate,
forward_rates))
return forward_rates