tf_quant_finance.experimental.instruments.CMSCashflowStream

Last updated: 2023-03-16.

tf_quant_finance.experimental.instruments.CMSCashflowStream#

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Represents a batch of cashflows indexed to a CMS rate.

tf_quant_finance.experimental.instruments.CMSCashflowStream(
    start_date, end_date, coupon_spec, dtype=None, name=None
)

Example:#

import numpy as np
import tensorflow as tf
import tf_quant_finance as tff
dates = tff.datetime
instruments = tff.experimental.instruments

start_date = dates.convert_to_date_tensor([(2021, 1, 1)])
maturity_date = dates.convert_to_date_tensor([(2023, 1, 1)])
valuation_date = dates.convert_to_date_tensor([(2021, 1, 1)])
p3m = dates.periods.months(3)
p6m = dates.periods.months(6)
p1y = dates.periods.year()
fix_spec = instruments.FixedCouponSpecs(
    coupon_frequency=p6m,
    currency='usd',
    notional=1.,
    coupon_rate=0.0,  # Not needed
    daycount_convention=instruments.DayCountConvention.ACTUAL_365,
    businessday_rule=dates.BusinessDayConvention.NONE)
flt_spec = instruments.FloatCouponSpecs(
    coupon_frequency=p3m,
    reference_rate_term=p3m,
    reset_frequency=p3m,
    currency='usd',
    notional=1.,
    businessday_rule=dates.BusinessDayConvention.NONE,
    coupon_basis=0.,
    coupon_multiplier=1.,
    daycount_convention=instruments.DayCountConvention.ACTUAL_365)
cms_spec = instruments.CMSCouponSpecs(
    coupon_frequency=p3m,
    tenor=p1y,
    float_leg=flt_spec,
    fixed_leg=fix_spec,
    notional=1.,
    coupon_basis=0.,
    coupon_multiplier=1.,
    businessday_rule=None,
    daycount_convention=instruments.DayCountConvention.ACTUAL_365)

cms = instruments.CMSCashflowStream(
    start_date, maturity_date, [cms_spec], dtype=dtype)

curve_dates = valuation_date + dates.periods.years([0, 1, 2, 3, 5])
reference_curve = instruments.RateCurve(
    curve_dates,
    np.array([
        0.02, 0.02, 0.025, 0.03, 0.035
    ], dtype=np.float64),
    valuation_date=valuation_date,
    dtype=np.float64)
market = instruments.InterestRateMarket(
    reference_curve=reference_curve, discount_curve=reference_curve)

price = cms.price(valuation_date, market)
# Expected result: 55512.6295434207

Args:#

  • start_date: A rank 1 DateTensor specifying the starting dates of the accrual of the first coupon of the cashflow stream. The shape of the input correspond to the numbercof streams being created.

  • end_date: A rank 1 DateTensor specifying the end dates for accrual of the last coupon in each cashflow stream. The shape of the input should be the same as that of start_date.

  • coupon_spec: A list of CMSCouponSpecs specifying the details of the coupon payment for the cashflow stream. The length of the list should be the same as the number of streams being created. Each coupon within the list must have the same daycount_convention and businessday_rule.

  • dtype: tf.Dtype. If supplied the dtype for the real variables or ops either supplied to the FloatingCashflowStream object or created by the object. Default value: None which maps to the default dtype inferred by TensorFlow.

  • name: Python str. The name to give to the ops created by this class. Default value: None which maps to ‘floating_cashflow_stream’.

Methods#

price

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price(
    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.

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