Prediction of Cryptocurrency Prices through a Path Dependent Monte Carlo Simulation

ArXiv ID: 2405.12988 “View on arXiv”

Authors: Unknown

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.

Keywords: Jump Diffusion Models, Monte Carlo Simulation, Cryptocurrency, Volatility Modeling, Cryptocurrency

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced stochastic calculus (Merton jump diffusion SDEs, compound Poisson processes) and path-dependent Monte Carlo simulations, indicating high mathematical density. It is grounded in real cryptocurrency data (BTC/ETH on Binance) and uses statistical metrics (MAE, RMSE, F1-score) for backtesting, but lacks explicit code or complete implementation details.

  flowchart TD
    A["Research Goal:<br>Predict Cryptocurrency Prices"] --> B{"Data Source"}
    B --> C["Historical Price-Volume Data"]
    
    C --> D["Modeling Approach:<br>Merton's Jump Diffusion Model"]
    
    D --> E["Parameter Estimation<br>via ML, Statistical & Traditional Methods"]
    
    E --> F["Path-Dependent<br>Monte Carlo Simulation"]
    
    F --> G["Key Findings"]
    
    G --> H["Improved Price<br>Forecasting Accuracy"]
    G --> I["Effective Modeling of<br>Volatility & Jumps"]