# Computing implied volatilities of ITM and OTM options

For an ATM call the implied volatility can be computed by using the Newton-Raphson method:

import numpy as np
from scipy.stats import norm

def bs_d1(S,K,T,sigma,R):
return (np.log(S/K) + (r+sigma**2/2)*T)/(sigma*np.sqrt(T))

def bs_call(S,K,T,sigma,r):
d1 = bs_d1(S,K,T,sigma,r)
return S*norm.cdf(d1) - np.exp(-r*T)*K*norm.cdf(d1-sigma*np.sqrt(T))

def bs_vega(S,K,T,sigma,r):
d1 = bs_d1(S,K,T,sigma,r)
return S*np.sqrt(T)*norm.pdf(d1)

def sigma_i(c,S,K,T,r,eps=1e-3):

# first approximation
sigma = np.sqrt(2*np.pi/T)*c/S

while True:
diff = bs_call(S,K,T,sigma,r) - c
vega = bs_vega(S,K,T,sigma,r)

if np.abs(diff) < eps:
break

sigma = sigma - diff/vega

return sigma


However the method fails when I use OTM and ITM calls since Vega becomes very close to zero. One example:

# OTM

S = 100
K = 140
T = 0.25
r = 0.03
c = 0.0015581689540368482 # bs_call(S,K,T,0.2,r)

sigma = sigma_i(c,S,K,T,r)


Another example:

# ITM

S = 100
K = 80
T = 1.5
r = 0.03
c = 42.36652921873271 # bs_call(S,K,T,0.7,r)

sigma = sigma_i(c,S,K,T,r)


How to compute the implied volatility in these cases?

• the problem you have, is that if the initially vol is too small, then the vega is tiny - i.e. nearly zero, which you then divide by. The best way of doing it i'm aware of is peter jaeckel's, you can see it [here](www.jaeckel.org), in a paper called "Let's Be Rational". I advise you read all of his papers, they're all interesting. – will Jan 25 '20 at 19:02
• @will This fixes the problem in the example that I originally posted. Hovewer, it does not work with a new example. – Appliqué Jan 25 '20 at 19:14

you can use this algoithm to calibrate volatility (the use of vega deos not work for far OTM and ITM options):

        define vol1=0.0;
define vol2=1.99;
define z;
define z2;
for (i=0;i<max_iterations;i++) {
z=0.5*(vol1+vol2);
z2=z/(1.0-z);
if (price>bs_price(z2)) {
vol1=z;
vol2=vol2;
} else {
vol1=vol1;
vol2=z;
}
}
z=0.5*(vol1+vol2);
return z/(1.0-z);

• I see. One just uses the bisection method. – Appliqué Jan 25 '20 at 21:39
• if you're going to do a bisection method, then if i recall, it's actually more efficient to use the golden ratio to get your next guess, rather than half way. – will Jan 26 '20 at 0:13