  In this paper, our focus lies on the Merton's jump diffusion model, employing
jump processes characterized by the compound Poisson process. Our primary
objective is to forecast the drift and volatility of the model using a variety
of methodologies. We adopt an approach that involves implementing different
drift, volatility, and jump terms within the model through various machine
learning techniques, traditional methods, and statistical methods on
price-volume data. Additionally, we introduce a path-dependent Monte Carlo
simulation to model cryptocurrency prices, taking into account the volatility
and unexpected jumps in prices.
