diff options
Diffstat (limited to 'python/swaption.py')
| -rw-r--r-- | python/swaption.py | 237 |
1 files changed, 7 insertions, 230 deletions
diff --git a/python/swaption.py b/python/swaption.py index 68486cc8..2238abe6 100644 --- a/python/swaption.py +++ b/python/swaption.py @@ -1,232 +1,9 @@ -from pyisda.flat_hazard import strike_vec -from quantlib.time.api import Date -import array -from scipy.optimize import brentq -from scipy.integrate import simps import datetime -import numpy as np -import pandas as pd -from pandas.tseries.offsets import BDay -from tranche_functions import GHquad -from yieldcurve import roll_yc -from scipy.stats import norm +from analytics import Index, Swaption -def year_frac(d1, d2, day_count_conv = "Actual/365"): - """ compute the year fraction between two dates """ - if day_count_conv.lower() in ["actual/365", "act/365"]: - return (d2-d1).days/365 - elif day_count_conv.lower() in ["actual/360", "act/360"]: - return (d2-d1).days/360 - -def calib(S0, fp, exercise_date, exercise_date_settle, index, - rolled_curve, tilt, w): - S = S0 * tilt * 1e-4 - a, b = strike_vec(S, rolled_curve, exercise_date, exercise_date_settle, - index.start_date, index.end_date, index.recovery) - vec = a - index.fixed_rate * b * 1e-4 - return np.inner(vec, w) - fp - -def g(index, spread, exercise_date, use_rolled_curve = True): - """ computes the strike clean price using the expected forward yield curve """ - if use_rolled_curve: - rolled_curve = roll_yc(index._yc, exercise_date) - else: - rolled_curve = index._yc - step_in_date = exercise_date + datetime.timedelta(days=1) - exercise_date_settle = (pd.Timestamp(exercise_date) + 3* BDay()).date() - sc = SpreadCurve(exercise_date, rolled_curve, index.start_date, - step_in_date, exercise_date_settle, - [index.end_date], array.array('d', [spread * 1e-4]), - index.recovery) - a = index._fee_leg.pv(exercise_date, step_in_date, exercise_date_settle, - rolled_curve, sc, True) - return (spread - index.fixed_rate) * a *1e-4 - -def ATMstrike(index, exercise_date): - exercise_date_settle = (pd.Timestamp(exercise_date) + 3* BDay()).date() - fp = index.forward_pv(exercise_date) - closure = lambda S: g(index, S, exercise_date) - fp - eta = 1.1 - a = index.spread - b = index.spread * eta - while True: - if closure(b) > 0: - break - b *= eta - return brentq(closure, a, b) - -class Option: - def __init__(self, index, exercise_date, strike, option_type="payer"): - self.index = index - self._exercise_date = exercise_date - self._forward_yc = roll_yc(self.index._yc, self.exercise_date) - self.exercise_date_settle = (pd.Timestamp(self.exercise_date) + 3* BDay()).date() - self._T = None - self.strike = strike - self.option_type = option_type.lower() - self._Z, self._w = GHquad(50) - self.notional = 1 - - @property - def exercise_date(self): - return self._exercise_date - - @exercise_date.setter - def exercise_date(self, d : datetime.date): - self._exercise_date = d - self.exercise_date_settle = (pd.Timestamp(d) + 3* BDay()).date() - self._forward_yc = roll_yc(self.index._yc, self.exercise_date) - - @property - def pv(self): - fp = self.index.forward_pv(self.exercise_date) / self.index.notional - T = self.T - tilt = np.exp(-self.sigma**2/2 * T + self.sigma * self._Z * math.sqrt(T)) - rolled_curve = roll_yc(self.index._yc, self.exercise_date) - args = (fp, self.exercise_date, self.exercise_date_settle, - self.index, self._forward_yc, tilt, self._w) - eta = 1.1 - a = self.index.spread - b = self.index.spread * eta - while True: - if calib(*((b,) + args)) > 0: - break - b *= eta - - S0 = brentq(calib, a, b, args) - - G = g(self.index, self.strike, self.exercise_date) - if T == 0: - pv = self.notional * (g(self.index, self.index.spread, self.exercise_date) - G) - if self.option_type == "payer": - return pv if self.index.spread > self.strike else 0 - else: - return - pv if self.index.spread < self.strike else 0 - - Zstar = (math.log(self.strike/S0) + self.sigma**2/2 * T) / \ - (self.sigma * math.sqrt(T)) - - if self.option_type == "payer": - Z = Zstar + np.logspace(0, 1.5, 300) - 1 - elif self.option_type == "receiver": - Z = Zstar - np.logspace(0, 1.5, 300) + 1 - else: - raise ValueError("option_type needs to be either 'payer' or 'receiver'") - S = S0 * np.exp(-self.sigma**2/2 * T + self.sigma * Z * math.sqrt(T)) - a, b = strike_vec(S * 1e-4, rolled_curve, self.exercise_date, - self.exercise_date_settle, - self.index.start_date, self.index.end_date, self.index.recovery) - val = ((a - b * self.index.fixed_rate*1e-4) - G) * 1/math.sqrt(2*math.pi) * np.exp(-Z**2/2) - df_scale = self.index._yc.discount_factor(self.exercise_date_settle) - return self.notional * simps(val, Z) * df_scale - - @property - def pv2(self): - G = g(self.index, self.strike, self.exercise_date) - fp = self.index.forward_pv(self.exercise_date) / self.index.notional - forward_annuity = self.index.forward_annuity(self.exercise_date) - DA_forward_spread = fp / forward_annuity + self.index.fixed_rate * 1e-4 - strike_tilde = self.index.fixed_rate * 1e-4 + G / forward_annuity - return forward_annuity * black(DA_forward_spread, - strike_tilde, - self.T, - self.sigma, - self.option_type) * self.notional - - @property - def delta(self): - old_index_pv = self.index.pv - old_pv = self.pv - self.index.spread += 0.1 - notional_ratio = self.index.notional/self.notional - delta = (self.pv - old_pv)/(self.index.pv - old_index_pv) * notional_ratio - self.index.spread -= 0.1 - return delta - - - @property - def T(self): - if self._T: - return self._T - else: - return year_frac(self.index.trade_date, self.exercise_date) + 1/365 - - @property - def gamma(self): - pass - - @property - def theta(self): - old_pv = self.pv - self._T = self.T - 1/365 - theta = self.pv - old_pv - self._T = None - return theta - - @property - def vega(self): - old_pv = self.pv - self.sigma += 0.01 - vega = self.pv - old_pv - self.sigma -= 0.01 - return vega - -def d1(F, K, sigma, T): - return (np.log(F / K) + sigma**2 * T / 2) / (sigma * np.sqrt(T)) - -def d2(F, K, sigma, T): - return d1(F, K, sigma, T) - sigma * np.sqrt(T) - -def black(F, K, T, sigma, option_type = "payer"): - chi = 1 if option_type == "payer" else -1 - if option_type == "payer": - return F * norm.cdf(d1(F, K, sigma, T)) - K * norm.cdf(d2(F, K, sigma, T)) - else: - return K * norm.cdf(- d2(F, K, sigma, T)) - F * norm.cdf(- d1(F, K, sigma, T)) - -def option(index, exercise_date, sigma, K, option_type="payer"): - """ computes the pv of an option using Pedersen's model """ - fp = index.forward_pv(exercise_date)/index.notional - #forward_yc = yield_curve.expected_forward_curve(exercise_date) - #expiry is end of day (not sure if this is right) - T = year_frac(index.trade_date, exercise_date) - Z, w = GHquad(50) - tilt = np.exp(-sigma**2/2 * T + sigma * Z * math.sqrt(T)) - exercise_date_settle = (pd.Timestamp(exercise_date) + 3* BDay()).date() - args = (fp, exercise_date, exercise_date_settle, index, tilt, w) - ## atm forward is greater than spread - eta = 1.1 - a = index.spread - b = index.spread * eta - while True: - if calib(*((b,) + args)) > 0: - break - b *= eta - S0 = brentq(calib, a, b, args) - S = S0 * tilt - G = g(index, K, exercise_date) - handle = lambda Z: g(index, S0 * math.exp(-sigma**2/2 * T + sigma * Z * math.sqrt(T)), - exercise_date) - G - Zstar = brentq(handle, -3, 3) - if option_type.lower() == "payer": - Z = Zstar + np.logspace(0, 1.1, 300) - 1 - elif option_type.lower() == "receiver": - Z = Zstar - np.logspace(0, 1.1, 300) + 1 - else: - raise ValueError("option_type needs to be either 'payer' or 'receiver'") - S = S0 * np.exp(-sigma**2/2 * T + sigma * Z * math.sqrt(T)) - a, b = strike_vec(S, index._yc, exercise_date, exercise_date_settle, - index.start_date, index.end_date, index.recovery) - val = ((a - b * index.fixed_rate)/df - G) * 1/math.sqrt(2*math.pi) * np.exp(-Z**2/2) - return simps(val, Z) * yield_curve.discount_factor(exercise_date_settle) - -if __name__ == "__main__": - import datetime - from analytics import Index - from swaption import Option - - ig27_5yr = Index.from_name('ig', 27, '5yr', datetime.date(2016, 10, 24)) - ig27_5yr.spread = 74 - payer = Option(ig27_5yr, datetime.date(2016, 12, 21), 65) - payer.sigma = 0.428 - payer.notional = 10e6 +ig27_5yr = Index.from_name('ig', 27, '5yr', datetime.date(2016, 10, 24)) +ig27_5yr.spread = 74 +payer = Swaption(ig27_5yr, datetime.date(2016, 12, 21), 65) +payer.sigma = 0.428 +payer.notional = 10e6 +print(payer.pv) |