示例#1
0
def zbt_libor_yield(instruments,
                    yields,
                    pricing_date,
                    basis='Actual/Actual (Bond)',
                    compounding_freq='Continuous',
                    maturity_dates=None):
    """
    Bootstrap a zero-coupon curve from libor rates and swap yields

    Args:

    instruments:    list of instruments, of the form Libor?M for Libor rates
                   and Swap?Y for swap rates
    yields:        market rates
    pricing_date:  the date where market data is observed. Settlement
                   is by default 2 days after pricing_date

    Optional:

    compounding_frequency: ... of zero-coupon rates. By default:
                   'Continuous'

    Returns:

    zero_rate:     zero-coupon rate
    maturity_date: ... of corresponding rate
    """

    calendar = TARGET()

    settings = Settings()
    # must be a business day
    eval_date = calendar.adjust(pydate_to_qldate(pricing_date))
    settings.evaluation_date = eval_date

    rates = dict(zip(instruments, yields))
    ts = make_term_structure(rates, pricing_date)

    cnt = DayCounter.from_name(basis)

    if maturity_dates is None:
        # schedule of maturity dates from settlement date to last date on
        # the term structure

        s = Schedule(effective_date=ts.reference_date,
                     termination_date=ts.max_date,
                     tenor=Period(1, Months),
                     calendar=TARGET())
        maturity_dates = [qldate_to_pydate(dt) for dt in s.dates()]

    cp_freq = Compounding[compounding_freq]
    zc = [
        ts.zero_rate(pydate_to_qldate(dt),
                     day_counter=cnt,
                     compounding=cp_freq).rate for dt in maturity_dates
    ]

    return (maturity_dates, zc)
示例#2
0
def zbt_libor_yield(instruments, yields, pricing_date,
                    basis='Actual/Actual (Bond)',
                    compounding_freq='Continuous',
                    maturity_dates=None):
    """
    Bootstrap a zero-coupon curve from libor rates and swap yields

    Args:

    insruments:    list of instruments, of the form Libor?M for Libor rates
                   and Swap?Y for swap rates
    yields:        market rates
    pricing_date:  the date where market data is observed. Settlement
                   is by default 2 days after pricing_date

    Optional:

    compounding_frequency: ... of zero-coupon rates. By default:
                   'Continuous'

    Returns:

    zero_rate:     zero-coupon rate
    maturity_date: ... of corresponding rate
    """

    calendar = TARGET()

    settings = Settings()
    # must be a business day
    eval_date = calendar.adjust(pydate_to_qldate(pricing_date))
    settings.evaluation_date = eval_date

    rates = dict(zip(instruments, yields))
    ts = make_term_structure(rates, pricing_date)

    cnt = DayCounter.from_name(basis)

    if maturity_dates is None:
        # schedule of maturity dates from settlement date to last date on
        # the term structure

        s = Schedule(effective_date=ts.reference_date,
                     termination_date=ts.max_date,
                     tenor=Period(1, Months),
                     calendar=TARGET())
        maturity_dates = [qldate_to_pydate(dt) for dt in s.dates()]

    cp_freq = compounding_from_name(compounding_freq)
    zc = [ts.zero_rate(date=pydate_to_qldate(dt),
                       day_counter=cnt,
                       compounding=cp_freq).rate for dt in maturity_dates]

    return (maturity_dates, zc)
示例#3
0
    dtI = dtObs[range(0, len(dtObs) - 1, 60)]
    days = [
        10, 30, 90, 182, 365, 365 * 2, 365 * 3, 365 * 5, 365 * 10, 365 * 15
    ]

    # maturity in columns, observation days in rows
    zc_rate = np.empty((len(dtI), len(days)), dtype='float64')
    dt_maturity = np.empty_like(zc_rate, dtype='object')

    # one observation date at a time, construct a term structure from
    # deposit and swap rates, then compute zero-coupon rates at
    # selected maturities
    for i, obs_date in enumerate(dtI):
        print(obs_date)
        rates = df_libor.xs(obs_date) / 100.0
        ts = make_term_structure(rates, obs_date)
        (dt_maturity[i, ], zc_rate[i, ]) = zero_rate(ts, days, obs_date)

    # PCA on rate change
    zc_pca = ml.PCA(np.diff(zc_rate, axis=0))

    fig = plt.figure()
    fig.set_size_inches(10, 6)

    ax = fig.add_subplot(121)

    dtMin = dt_maturity[0, 0]
    dtMax = dt_maturity[-1, -1]
    ax.set_xlim(dtMin, dtMax)
    ax.set_ylim(0.0, 0.1)
示例#4
0
    dtI = dtObs[range(0, len(dtObs) - 1, 60)]
    days = [10, 30, 90, 182, 365, 365 * 2, 365 * 3,
            365 * 5, 365 * 10, 365 * 15]

    # maturity in columns, observation days in rows
    zc_rate = np.empty((len(dtI), len(days)), dtype='float64')
    dt_maturity = np.empty_like(zc_rate, dtype='object')

    # one observation date at a time, construct a term structure from
    # deposit and swap rates, then compute zero-coupon rates at
    # selected maturities
    for i, obs_date in enumerate(dtI):
        print(obs_date)
        rates = df_libor.xs(obs_date) / 100
        ts = make_term_structure(rates, obs_date)
        (dt_maturity[i, ], zc_rate[i, ]) = zero_rate(ts, days, obs_date)

    # PCA on rate change
    zc_pca = ml.PCA(np.diff(zc_rate, axis=0))

    fig = plt.figure()
    fig.set_size_inches(10, 6)

    ax = fig.add_subplot(121)

    dtMin = dt_maturity[0, 0]
    dtMax = dt_maturity[-1, -1]
    ax.set_xlim(dtMin, dtMax)
    ax.set_ylim(0.0, 0.1)