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:

     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:


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?

  • 1
    $\begingroup$ 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. $\endgroup$ – will Jan 25 '20 at 19:02
  • $\begingroup$ @will This fixes the problem in the example that I originally posted. Hovewer, it does not work with a new example. $\endgroup$ – 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++) {
            if (price>bs_price(z2)) {
            } else {
        return z/(1.0-z);
  • $\begingroup$ I see. One just uses the bisection method. $\endgroup$ – Appliqué Jan 25 '20 at 21:39
  • 1
    $\begingroup$ 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. $\endgroup$ – will Jan 26 '20 at 0:13

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