Monte Carlo Simulation-based Framework for Cryptocurrency Portfolio Risk Assessment

Zihang Qi ()
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Zihang Qi: China Agricultural University

A chapter in Proceedings of the 2026 11th International Conference on Social Sciences and Economic Development (ICSSED 2026), 2026, pp 887-902 from Springer

Abstract: Abstract 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.

Keywords: Cryptocurrency; portfolio investment; risk management; Value at Risk (VaR); Expected Loss (ES); Monte Carlo simulation; t-distribution; Gamma distribution (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6239-701-9_92

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DOI: 10.2991/978-94-6239-701-9_92

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