Monte Carlo Simulation-based Framework for Cryptocurrency Portfolio Risk Assessment

DOI

10.2991/978-94-6239-701-9_92How to use a DOI?

Keywords

Cryptocurrency; portfolio investment; risk management; Value at Risk (VaR); Expected Loss (ES); Monte Carlo simulation; t-distribution; Gamma distribution

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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zihang Qi
PY  - 2026
DA  - 2026/07/30
TI  - Monte Carlo Simulation-based Framework for Cryptocurrency Portfolio Risk Assessment
BT  - Proceedings of the 2026 11th International Conference on Social Sciences and Economic Development (ICSSED 2026)
PB  - Atlantis Press
SP  - 887
EP  - 902
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-701-9_92
DO  - 10.2991/978-94-6239-701-9_92
ID  - Qi2026
ER  -