Hybrid Poisson-Gaussian Stochastic Modeling for Simulating Ethereum Price Dynamics

Abstract

This study introduces a novel hybrid stochastic modeling framework for simulating Ethereum price dynamics by integrating Poisson and Gaussian processes. The model captures both abrupt price jumps, modeled using a Poisson process, and continuous price variations, represented by a Gaussian process. Our analysis reveals that significant price fluctuations occur approximately every 4.33 days, with an average daily return of 0.0041 and an annualized volatility of 0.8631, underscoring the extreme volatility inherent in Ethereum’s market behavior. By combining these processes, the model effectively encapsulates the intrinsic price patterns of Ethereum, including persistent oscillations and sudden surges. Simulations of future price trajectories demonstrate the model’s efficacy in replicating real world Ethereum price dynamics, offering valuable insights for traders and analysts in devising risk management strategies and making informed decisions in highly volatile cryptocurrency markets. The findings highlight the importance of hybrid models in addressing the unique challenges of modeling Ethereum’s price behavior.