Test BSM model implementation

Repeat the same for normal model implementation



In [1]:

    
import numpy as np



In [2]:

    
# import
#from option_models import bsm
#from option_models import normal

#import option_models as opt
from option_models import bsm



In [3]:

    
### only run this when you changed the class definition
import imp
imp.reload(bsm)









    Out[3]:





<module 'option_models.bsm' from 'C:\\Users\\jaehyuk\\Documents\\GitHub\\SABRmodel_Base\\option_models\\bsm.py'>



In [ ]:

Price



In [4]:

    
# create model
texp = 0.25
vol = 0.2
bsm1 = bsm.Model(texp, vol)



In [5]:

    
# price
strike = 102
spot = 100

price = bsm1.price(strike=strike, spot=spot, cp_sign=1)
print(price)
assert( abs(price - 3.10628366655) < 1e-10 )









    



3.10628366655

Implied vol



In [6]:

    
# Randomly generate spot/strike/expiry/intr/divr/cp_sign
# Then test implied volatility

for k in range(100):
    spot = np.random.uniform(80,100)
    strike = np.random.uniform(80,100)
    vol = np.random.uniform(0.0001, 0.4)
    texp = np.random.uniform(0.1, 5)
    intr = np.random.uniform(0, 0.3)
    divr = np.random.uniform(0, 0.3)
    cp_sign = 1 if np.random.rand() > 0.5 else -1

    #print( spot, strike, vol, texp, intr, divr, cp_sign)

    bsm2 = bsm.Model(texp=texp, vol=vol, intr=intr, divr=divr)
    price = bsm2.price(strike, spot, cp_sign=cp_sign )
    
    # get implied vol
    vol_imp = bsm2.impvol(price, strike, spot, cp_sign=cp_sign)
    
    # now price option with the obtained implied vol
    price_imp = bsm2.price(strike, spot, vol=vol_imp, cp_sign=cp_sign )
    
    # compare the two prices
    assert( abs(price - price_imp) < 1e-8 )



In [7]:









    Out[7]:





(-0.0, 0.0)



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