Binomial VS Monte Carlo

We compare the simulation methods commonly applied for such an underlying asset.

The two simulation methods I mentioned, namely Binomial and Monte Carlo simulations, are both widely used in options pricing. Here's a brief explanation of the differences between the two:

1. Binomial Simulation:

   • The binomial simulation method is based on the concept of a binomial tree, where the price of the underlying asset (in this case, the ERC20 token) is modeled as moving up or down at each time step.

   • The simulation divides the time to expiration into multiple discrete periods or steps.

   • At each step, the price can either move up or down based on specified probabilities.

   • By calculating the expected value at each step and working backward through the tree, the option price can be estimated.

   • The binomial model is relatively straightforward to implement and computationally less intensive compared to Monte Carlo simulation.

   • However, it can be less accurate than Monte Carlo simulation, especially for complex or highly volatile assets.

2. Monte Carlo Simulation:

   • Monte Carlo simulation uses random sampling to simulate a large number of possible price paths for the underlying asset.

   • The simulation involves generating random price movements based on specified statistical distributions, such as the log-normal distribution.

   • By simulating a large number of price paths, the option price is estimated based on the distribution of the final outcomes.

   • Monte Carlo simulation can handle complex and non-linear option pricing models more effectively than the binomial model.

   • It generally provides more accurate results, especially for assets with complex price dynamics or when considering multiple risk factors.

   • However, Monte Carlo simulation can be computationally more intensive and time-consuming compared to the binomial model.

In summary, the binomial simulation is simpler and computationally faster but may be less accurate for complex or highly volatile assets. On the other hand, Monte Carlo simulation is more flexible, accurate, and suitable for a wider range of option pricing scenarios but requires more computational resources.

The choice for the Beta Platform will be the Binamial Simulation Model.