vollib.ref_python.black_scholes package

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Submodules

vollib.ref_python.black_scholes.implied_volatility module

vollib.ref_python.black_scholes.implied_volatility

A library for option pricing, implied volatility, and greek calculation. vollib is based on lets_be_rational, a Python wrapper for LetsBeRational by Peter Jaeckel as described below.

copyright:

© 2017 Gammon Capital LLC

license:

MIT, see LICENSE for more details.

vollib.ref_python.black_scholes.implied_volatility.implied_volatility(price, S, K, t, r, flag)[source]

Calculate the Black-Scholes implied volatility.

Parameters:

  • price (float) – the Black-Scholes option price

  • S (float) – underlying asset price

  • K (float) – strike price

  • t (float) – time to expiration in years

  • r (float) – risk-free interest rate

  • flag (str) – ‘c’ or ‘p’ for call or put.

>>> S = 100
>>> K = 100
>>> sigma = .2
>>> r = .01
>>> flag = 'c'
>>> t = .5

>>> price = black_scholes(flag, S, K, t, r, sigma)
>>> iv = implied_volatility(price, S, K, t, r, flag)

>>> expected_price = 5.87602423383
>>> expected_iv = 0.2

>>> abs(expected_price - price) < 0.00001
True
>>> abs(expected_iv - iv) < 0.01
True

>>> sigma = 0.3
>>> S, K, t, r, flag = 100.0, 1000.0, 0.5, 0.05, 'p'
>>> price = black_scholes(flag, S, K, t, r, sigma)
>>> print (price)
875.309912028
>>> iv = implied_volatility(price, S, K, t, r, flag)

>>> print (round(iv, 1))
0.0

Module contents

vollib.ref_python.black_scholes

A library for option pricing, implied volatility, and greek calculation. vollib is based on lets_be_rational, a Python wrapper for LetsBeRational by Peter Jaeckel as described below.

copyright:

© 2017 Gammon Capital LLC

license:

MIT, see LICENSE for more details.

vollib.ref_python is a pure python version of vollib without any dependence on LetsBeRational. It is provided purely as a reference implementation for sanity checking. It is not recommended for industrial use.

vollib.ref_python.black_scholes.black_scholes(flag, S, K, t, r, sigma)[source]
Return the Black-Scholes option price implemented in

python (for reference).

Parameters:

  • S (float) – underlying asset price

  • K (float) – strike price

  • sigma (float) – annualized standard deviation, or volatility

  • t (float) – time to expiration in years

  • r (float) – risk-free interest rate

  • flag (str) – ‘c’ or ‘p’ for call or put.

>>> S,K,t,r,sigma = 60,65,.25,.08,.3
>>> expected = 2.13336844492
>>> actual = black_scholes('c',S,K,t,r,sigma)
>>> abs(expected-actual) < 1e-11
True
vollib.ref_python.black_scholes.d1(S, K, t, r, sigma)[source]

Calculate the d1 component of the Black-Scholes PDE.

Parameters:

  • S (float) – underlying asset price

  • K (float) – strike price

  • sigma (float) – annualized standard deviation, or volatility

  • t (float) – time to expiration in years

  • r (float) – risk-free interest rate

John C. Hull, “Options, Futures and Other Derivatives,” 7th edition, Example 13.6, page 294

>>> S = 42
>>> K = 40
>>> r = .10
>>> sigma = .20
>>> t = 0.5
>>> calculated_d1 = d1(S,K,t,r,sigma)
>>> text_book_d1 = 0.7693
>>> abs(calculated_d1 - text_book_d1) < 0.0001
True
vollib.ref_python.black_scholes.d2(S, K, t, r, sigma)[source]

Calculate the d2 component of the Black-Scholes PDE.

Parameters:

  • S (float) – underlying asset price

  • K (float) – strike price

  • sigma (float) – annualized standard deviation, or volatility

  • t (float) – time to expiration in years

  • r (float) – risk-free interest rate

John C. Hull, “Options, Futures and Other Derivatives,” 7th edition, Example 13.6, page 294

>>> S = 42
>>> K = 40
>>> r = .10
>>> sigma = .20
>>> t = 0.5
>>> calculated_d2 = d2(S,K,t,r,sigma) #0.627841271869
>>> text_book_d2 = 0.6278
>>> abs(calculated_d2 - text_book_d2) < 0.0001
True