---
# System prepended metadata

title: Binomail Option Pricing Model

---

# Binomail Option Pricing Model

## Binomial Tree Model

### Assumption

$$ Stock \ price \ at \ t=0: \ S_0=50 $$
$$ Excercise\ price:\ K=52$$
$$ Risk\ free\ rate:\ r=5\% $$
$$ Volitility:\ \sigma=30\%$$
$$ Time\ period:\ T=2$$

```pythonscript

S0 = 50
sigma = 0.3
K = 52
T = 2
r = 0.05

```

Δt is time step. For example, when Δt = 1 month, there are 12 * 2 = 24 steps in 2 years.

Before construct a binomial tree, it is necessary to compute the extent of the upward and downward of stock price under risk-neutral measure. 

$$ u = e^{\sigma\sqrt{\Delta t}}$$
$$ d = e^{-\sigma\sqrt{\Delta t}} = \frac{1}{u}$$

Also we have to calculate the probability of upward and downward.

$$risk-neutral\ probability\ =\ p\ =\ \frac{e^{r\Delta t}-d}{u-d}$$

### Import library

``` pythonscript

import math
import numpy as np
import random
import matplotlib.pyplot as plt

```

### Define a function to calculate u, d, p in different number of steps.

``` pythonscript

def udp(t):
    u = math.exp(sigma*np.sqrt(2/t))
    d = math.exp(-sigma*np.sqrt(2/t))
    p = (math.exp(r * 2/t) - d) / (u - d)
    
    return (u, d, p)
    
```

### Define a function to calculate the price of the option.

``` pythonscript

def pricing(t, OptionType):    
    
    u = udp(t)[0]
    d = udp(t)[1]
    p = udp(t)[2]
    
    S = []
    for i in range(0, t + 1):
        St = S0 * (u ** (t - i)) * (d ** (i))
        S.append(St)

    put_payoff = []
    if OptionType == 'put':  
      for i in range(0, t + 1):
          pi = max(K - S[i], 0)
          put_payoff.append(pi)
    elif OptionType == 'call':
      for i in range(0, t + 1):
          pi = max(S[i] - K, 0)
          put_payoff.append(pi)

    price = []
    for i in range(t+1):
        price.append([])
    price[0] = put_payoff 

    for j in range(0, t): 
        for i in range(0, t - j):
            St = (price[j][i] * p + price[j][i+1] * (1 - p)) * math.exp(-r*2/t)
            price[j+1].append(St)
    
    return (price[t], S)

```



### Caculate prices of put options in the following steps: 12(1 month), 52(1 week), 252(1 trading day)

``` pythonscript

steps = [12, 52, 252]
for i in range(0, 3):
    print(f"Put Price({steps[i]}T steps) = {pricing(steps[i], 'put')[0]}")
    print('\n')
    
```

`Put Price(12T steps) = [6.761718527861203]`
`Put Price(52T steps) = [6.786837673688396]`
`Put Price(252T steps) = [6.763849905842416]`

### The result of binomial tree(steps = 2500)

``` pythonscript
print(pricing(2500, 'put')[0])
```
`[6.760639211934076]
`

## Compare the results of Binomial Tree whith the result of Black-Scholes formula

### Black-Scholes formula

$$ c=S_0N(d_1)-Ke^{-rT}N(d_2)$$
$$ p=Ke^{-rT}N(-d_2)-S_0N(-d_1)$$
$$;d_1=\frac{ln(S_0/K)+(r+\sigma^2/2)T}{\sigma\sqrt{T}}$$
$$ d_2 = \frac{ln(S_0/K)+(r-\sigma^2/2)T}{\sigma\sqrt{T}}=d_1-\sigma\sqrt{T}$$

Using the assumption on the beginig of article, we can calculate the put price by using Black-Sholes Formula.

``` pythonscript

import numpy as np
from scipy.stats import norm

d1 = (np.log(S0/K)+(r+sigma**2/2)*T)/(sigma*math.sqrt(T))
d2 = d1-sigma*math.sqrt(T)

BS_putprice = K * math.exp(-r*T) * norm.cdf(-d2) - S0 * norm.cdf(-d1)

print(f"Put price from BS model = {BS_putprice}")

```

`Put price from BS model = 6.760140373699151`

When the number of steps increase large enough, discrete form becomes closer to continuous form. So the result of binomial tree is very close to the result of Black-Sholes model.

## Terminal stock price(6, 12, 52 steps)

```pythonscript
steps = [6, 12, 52]

for i in steps:
    x_axis = []
    for k in range(0, i + 2):    
        x_axis.append(k)
    y_axis = pricing(i, 'put')[1]
    y_axis.insert(0, 0)
    plt.plot(x_axis, y_axis)
    plt.ylabel('Stock Price')
    plt.show()
    print('\n')  

```
![](https://i.imgur.com/EevH9kG.png)
![](https://i.imgur.com/9hVRp39.png)
![](https://i.imgur.com/MlTepnF.png)

We can find that the terminal stock price follow lognormal distribution.

## American Option

American options can excercise at any time before the excercise day.

So we Define new functions to measure the price of American funtion.

``` pythonscript
def us_pricing(t, OptionType):

    u = udp(t)[0]
    d = udp(t)[1]
    p = udp(t)[2]

    binomial = []
    for i in range(t+1):
        binomial.append([])

    for j in range(0, t+1):
        S = []
        for i in range(0, j+1):
            St = S0 * (d ** (i)) * (u ** (j-i))
            S.append(St)
        binomial[j] = S

    #print(binomial)    

    put_payoff = []
    if OptionType == 'put':  
      for i in range(0, t+1):
          pi = max(K - binomial[t][i], 0)
          put_payoff.append(pi)
    elif OptionType == 'call':
      for i in range(0, t+1):
          pi = max(binomial[t][i] - K, 0)
          put_payoff.append(pi)

    #print(put_payoff)

    price = []
    for i in range(2*t+1):
        price.append([])
    price[0] = put_payoff 

    #print(price)

    for j in range(0, t+1):
      if OptionType == 'put':
        for i in range(0, t-j): 
          pi = (price[j][i] * p + price[j][i+1] * (1 - p)) / math.exp(r *2/ t)
          s = max(K - binomial[t-1-j][i], 0)
          price[j+1].append(max(pi, s))
      elif OptionType == 'call':
        for i in range(0, t-j): 
          pi = (price[j][i] * p + price[j][i+1] * (1 - p)) / math.exp(r *2/ t)
          s = max(binomial[t-1-j][i] - K, 0)
          price[j+1].append(max(pi, s))

    return price[t][0]
    
```

Ex. calculate the price of American put whith number of steps = 12, 52, 252

```pythonscript

steps = [12, 52, 252]
for i in range(0, 3):
    print(f"Put Price({steps[i]}T steps) = {us_pricing(steps[i], 'put')}")
    
```