/Option-pricing-using-ML

My first internship program , A small recap to what i worked on.

Primary LanguageJupyter Notebook

To implement deep learning algorithm in option pricing and compare the result with Black Scholes formula for calculating options.

Option Price Basics:

  1. Options are high risky derivatives that give option buyers the right to buy/sell a security at price on or before maturity.
  2. Price of options is also called premiums which consist of sum of its intrinsic and extrinsic value.
  3. Intrinsic value is the amount of money received immediately if an option were exercised less than strike price.
  4. Extrinsic value is remaining value which exceeds the intrinsic value in premiums

Step for parameter and training model:-

  1. Creating Dataset - We randomly generated data of 300,000 option calls.
  2. Training Set - Comprises of 2,40,000 call prices.
  3. Validation set - 60,000 prices.
  4. Pricing function is linearly normalized i.e,C(S, K)/K = C(S/K, 1)
  5. Finally the normalized data is fit into deep net.

Details of Deep neural network

  1. The size of input is 6 parameters

  2. Passed with 4 hidden layers consisting of 100 neurons each.

  3. At each layer we have used different Activation function according to the inputs that are

    • ELU
    • ReLU
    • LeakyReLU

  4. The loss function used is MSE

  5. Epoch = 30

  6. Batch size = 100

These are the basics for learning Neural network for Financial models. There are many models Black-Scholes is one

Enjoy !!