Monte Carlo Simulation-based Framework for Cryptocurrency Portfolio Risk Assessment

The cryptocurrency market has emerged as one of the most volatile and high-risk sectors in global finance, characterized by extreme volatility and speculative behavior. Digital assets like Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) exhibit distinct skewness, kurtosis, and fat-tail distributions-features that traditional Gaussian risk models struggle to capture. These statistical characteristics indicate that conventional risk models often underestimate the probability of extreme losses, making the development of effective risk measurement tools critical for investors and risk managers. This paper proposes a comprehensive Monte Carlo simulation framework that integrates fat-tail distributions with portfolio asset correlation structures to estimate Value at Risk (VaR) and Exponential Risk (ES) for cryptocurrency portfolios. Through Monte Carlo simulations using t-distributions and Gamma distributions, the study more accurately characterizes tail risks. The research demonstrates significant variations in risk estimates under different distribution assumptions, underscoring the importance of incorporating real market characteristics in risk management. This study aims to provide robust risk measurement tools for highly volatile digital asset markets and offer risk managers more reliable guidance.