Here we learn how to price an American option using the binomial tree model. Within the binomial tree model we compute the maximum of the exercise value or continuation/hold value at each node, and then discount the expected payoff based.
In this tutorial we use the binomial tree model to price a path dependant option, namely a up-and-out barrier put option.
How does one choose the ‘correct’ parameters for the Binomial Asset Pricing Model. In this tutorial we implement Cox, Ross and Rubinstein (CRR), Jarrow and Rudd (JR), Equal probabilities (EQP) and the Trigeorgis (TRG) method!
In this tutorial we will be implementating a simple slow and fast binomial pricing model in python. We will treat binomial tree as a network with nodes (i,j) with i representing the time steps and j representing the number of ordered price outcome.
