def calc_OAS(m, theta_df, kappa, sigma, gamma, p, beta, r0, bond_list,
Tranche_bal_arr, wac, tenor, antithetic, Pool1_bal, Pool2_bal,
Pool1_mwac, Pool1_age, Pool1_term, Pool2_mwac, Pool2_age,
Pool2_term, coupon_rate):
spot_simulate_df = lib.simulate_rate(m, theta_df, kappa, sigma, r0,
antithetic)
# Calculate 10 yr rates
tenor_rate = calc_tenor_rate(spot_simulate_df, kappa, sigma, theta_df,
tenor)
v1 = wac - tenor_rate
v2 = v1.copy() * 0.0
v2[(v2.index.month >= 5) & (v2.index.month <= 8)] = 1
smm_df = calc_hazard(gamma, p, beta, v1, v2)
cash_df = np.vectorize(
calc_cashflow_CA,
signature='(n),(n),(),(),(),(),(),(),(),(),(),(k)->(m)')(
smm_df.T.values.astype(float),
spot_simulate_df.T.values.astype(float), Pool1_bal, Pool2_bal,
Pool1_mwac, Pool1_age, Pool1_term, Pool2_mwac, Pool2_age,
Pool2_term, coupon_rate, Tranche_bal_arr)
cash_df = pd.DataFrame(cash_df)
foward_rate = calc_tenor_rate(spot_simulate_df, kappa, sigma, theta_df, 1)
r0 = 0
oas = scipy.optimize.fsolve(calc_PV_diff,
r0,
args=(cash_df, foward_rate,
Tranche_bal_arr[-2]))[0]
return oas
def mc_bond(m, theta_df, kappa, sigma, gamma, p, beta, r0, bond_list,
Tranche_bal_arr, wac, tenor, antithetic, Pool1_bal, Pool2_bal,
Pool1_mwac, Pool1_age, Pool1_term, Pool2_mwac, Pool2_age,
Pool2_term, coupon_rate):
spot_simulate_df = lib.simulate_rate(m, theta_df, kappa, sigma, r0,
antithetic)
# Calculate 10 yr rates
tenor_rate = calc_tenor_rate(spot_simulate_df, kappa, sigma, theta_df,
tenor)
v1 = wac - tenor_rate
v2 = v1.copy() * 0.0
v2[(v2.index.month >= 5) & (v2.index.month <= 8)] = 1
smm_df = calc_hazard(gamma, p, beta, v1, v2)
price_df = np.vectorize(
calc_cashflow,
signature='(n),(n),(),(),(),(),(),(),(),(),(),(k)->(m)')(
smm_df.T.values.astype(float),
spot_simulate_df.T.values.astype(float), Pool1_bal, Pool2_bal,
Pool1_mwac, Pool1_age, Pool1_term, Pool2_mwac, Pool2_age,
Pool2_term, coupon_rate, Tranche_bal_arr)
price_df = pd.DataFrame(price_df, columns=bond_list)
return price_df, smm_df
def mc_bond(m, theta_df, kappa, sigma, sol_arm_p, sol_arm_d, sol_frm_p,
sol_frm_d, r0, tenor, antithetic, Pool1_bal, Pool2_bal, Pool1_mwac,
Pool1_age, Pool1_term, sprd, Pool2_age, Pool2_term, sprd_arr,
current_principal, current_ltv, orig_bal, Tranche_bal_arr, cds_mat,
m5_fixed_coupon, m2_fixed_coupon):
"""
current_principal current_ltv array[2]
gamma, p: 2D arrays for hazards. (FRM, ARM) x (prepay, default)
beta : 2x2x2 array for covariate coefficients for hazards.
(FRM, ARM) x (prepay, default) x ()
[0,:] is for prepay, [1,0] is for default (LTV)
"""
spot_simulate_df = lib.simulate_rate(m, theta_df, kappa, sigma, r0,
antithetic).astype(float) / 12
hp_array1, hp_array2 = simulate_homeprice(m, spot_simulate_df.values,
current_principal, current_ltv)
# Calculate 10 yr rates
tenor_rate = lib_2.calc_tenor_rate(spot_simulate_df, kappa, sigma,
theta_df, tenor)
v_frm_prepay1 = Pool1_mwac * 12 - tenor_rate
v_frm_prepay2 = v_frm_prepay1.copy() * 0.0
v_frm_prepay2[(v_frm_prepay2.index.month >= 5)
& (v_frm_prepay2.index.month <= 8)] = 1
v_arm_prepay1 = sprd * 12 - tenor_rate + spot_simulate_df
v_arm_prepay2 = v_arm_prepay1.copy() * 0.0
v_arm_prepay2[(v_arm_prepay2.index.month >= 5)
& (v_arm_prepay2.index.month <= 8)] = 1
smm_frm_df = lib_2.calc_hazard(sol_frm_p[0],
sol_frm_p[1],
sol_frm_p[2:],
v_frm_prepay1,
v_frm_prepay2,
age=Pool1_age).astype(
float) # prepay hazard
smm_arm_df = lib_2.calc_hazard(sol_arm_p[0],
sol_arm_p[1],
sol_arm_p[2:],
v_arm_prepay1,
v_arm_prepay2,
age=Pool2_age).astype(
float) # prepay hazard
# function definition
#def calc_cashflow(SMM_array_arm, SMM_array_frm, r, hpi_1, hpi_2, Pool1_bal, Pool2_bal, Pool1_mwac, Pool1_age, Pool1_term,
# Pool2_sprd, Pool2_age, Pool2_term, sprd_arr, Tranche_bal_arr, orig_bal, def_haz):
(price_df, cds_val_df) = np.vectorize(
calc_cashflow,
signature=
'(n),(n),(n),(n),(n),(),(),(),(),(),(),(),(),(k),(k),(),(t),(t),(),(),()->(m),(l)'
)(smm_frm_df.T.values, smm_arm_df.T.values, spot_simulate_df.T.values,
hp_array1.T, hp_array2.T, Pool1_bal, Pool2_bal, Pool1_mwac, Pool1_age,
Pool1_term, sprd, Pool2_age, Pool2_term, sprd_arr, Tranche_bal_arr,
orig_bal, sol_frm_d, sol_arm_d, cds_mat, m5_fixed_coupon,
m2_fixed_coupon)
price_df = pd.DataFrame(price_df, columns=range(Tranche_bal_arr.shape[0]))
cds_val_df = pd.DataFrame(cds_val_df,
columns=range(Tranche_bal_arr.shape[0]))
return price_df, cds_val_df