def dfToZeroCurve(df_rates, dtSettlement, daycounter=Actual365Fixed()):
"""
Convert a panda data frame into a QL zero curve
"""
dates = [dateToQLDate(dt) for dt in df_rates.index]
dates.insert(0, dateToQLDate(dtSettlement))
dates.append(dates[-1] + 365 * 2)
vx = list(df_rates.values)
vx.insert(0, vx[0])
vx.append(vx[-1])
return ZeroCurve(dates, vx, daycounter)
def df_to_zero_curve(rates, settlement_date, daycounter=Actual365Fixed()):
""" Converts a pandas data frame into a QL zero curve. """
dates = [pydate_to_qldate(dt) for dt in rates.index]
dates.insert(0, pydate_to_qldate(settlement_date))
# arbitrarily extend the curve a few years to provide flat
# extrapolation
dates.append(dates[-1] + 365 * 2)
values = rates.values.tolist()
values.insert(0, values[0])
values.append(values[-1])
return ZeroCurve(dates, values, daycounter)
def test_DAX_calibration(self):
# this example is taken from A. Sepp
# Pricing European-Style Options under Jump Diffusion Processes
# with Stochstic Volatility: Applications of Fourier Transform
# http://math.ut.ee/~spartak/papers/stochjumpvols.pdf
settlement_date = Date(5, July, 2002)
self.settings.evaluation_date = settlement_date
daycounter = Actual365Fixed()
calendar = TARGET()
t = [13, 41, 75, 165, 256, 345, 524, 703]
r = [0.0357,0.0349,0.0341,0.0355,0.0359,0.0368,0.0386,0.0401]
dates = [settlement_date] + [settlement_date + val for val in t]
rates = [0.0357] + r
risk_free_ts = ZeroCurve(dates, rates, daycounter)
dividend_ts = FlatForward(
settlement_date, forward=0.0, daycounter=daycounter
)
v = [
0.6625,0.4875,0.4204,0.3667,0.3431,0.3267,0.3121,0.3121,
0.6007,0.4543,0.3967,0.3511,0.3279,0.3154,0.2984,0.2921,
0.5084,0.4221,0.3718,0.3327,0.3155,0.3027,0.2919,0.2889,
0.4541,0.3869,0.3492,0.3149,0.2963,0.2926,0.2819,0.2800,
0.4060,0.3607,0.3330,0.2999,0.2887,0.2811,0.2751,0.2775,
0.3726,0.3396,0.3108,0.2781,0.2788,0.2722,0.2661,0.2686,
0.3550,0.3277,0.3012,0.2781,0.2781,0.2661,0.2661,0.2681,
0.3428,0.3209,0.2958,0.2740,0.2688,0.2627,0.2580,0.2620,
0.3302,0.3062,0.2799,0.2631,0.2573,0.2533,0.2504,0.2544,
0.3343,0.2959,0.2705,0.2540,0.2504,0.2464,0.2448,0.2462,
0.3460,0.2845,0.2624,0.2463,0.2425,0.2385,0.2373,0.2422,
0.3857,0.2860,0.2578,0.2399,0.2357,0.2327,0.2312,0.2351,
0.3976,0.2860,0.2607,0.2356,0.2297,0.2268,0.2241,0.2320
]
s0 = SimpleQuote(4468.17)
strikes = [
3400, 3600, 3800, 4000, 4200, 4400, 4500, 4600, 4800, 5000, 5200,
5400, 5600
]
options = []
for s, strike in enumerate(strikes):
for m in range(len(t)):
vol = SimpleQuote(v[s * 8 + m])
# round to weeks
maturity = Period((int)((t[m] + 3) / 7.), Weeks)
options.append(
HestonModelHelper(
maturity, calendar, s0.value, strike, vol,
risk_free_ts, dividend_ts,
ImpliedVolError
)
)
v0 = 0.1
kappa = 1.0
theta = 0.1
sigma = 0.5
rho = -0.5
process = HestonProcess(
risk_free_ts, dividend_ts, s0, v0, kappa, theta, sigma, rho
)
model = HestonModel(process)
engine = AnalyticHestonEngine(model, 64)
for option in options:
option.set_pricing_engine(engine)
om = LevenbergMarquardt(1e-8, 1e-8, 1e-8)
model.calibrate(
options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8)
)
sse = 0
for i in range(len(strikes) * len(t)):
diff = options[i].calibration_error() * 100.0
sse += diff * diff
expected = 177.2 # see article by A. Sepp.
self.assertAlmostEqual(expected, sse, delta=1.0)