Prediction of Cryptocurrency Prices through a Path Dependent Monte Carlo Simulation

TL;DR

This paper introduces a path-dependent Monte Carlo simulation method combined with machine learning and statistical techniques to forecast cryptocurrency prices, accounting for jumps and volatility in the Merton's jump diffusion model.

Contribution

It presents a novel integration of path-dependent Monte Carlo simulation with diverse modeling techniques for improved cryptocurrency price prediction.

Findings

Effective modeling of jumps and volatility in cryptocurrency prices

Enhanced forecasting accuracy using combined simulation and machine learning methods

Demonstrated applicability of the approach on real price-volume data

Abstract

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.