Quantifying Crypto Portfolio Risk: A Simulation-Based Framework Integrating Volatility, Hedging, Contagion, and Monte Carlo Modeling

TL;DR

This paper introduces a comprehensive simulation framework for crypto portfolio risk that combines volatility stress testing, hedging, contagion modeling, and Monte Carlo methods, validated with recent market data.

Contribution

It presents a novel modular framework integrating multiple risk components specifically tailored for cryptocurrencies, grounded in mathematical finance theory.

Findings

Framework effectively captures crypto market risks.

Empirical validation shows accurate risk estimation.

Modular design allows flexible risk analysis.

Abstract

Extreme volatility, nonlinear dependencies, and systemic fragility are characteristics of cryptocurrency markets. The assumptions of normality and centralized control in traditional financial risk models frequently cause them to miss these changes. Four components-volatility stress testing, stablecoin hedging, contagion modeling, and Monte Carlo simulation-are integrated into this paper's modular simulation framework for crypto portfolio risk analysis. Every module is based on mathematical finance theory, which includes stochastic price path generation, correlation-based contagion propagation, and mean-variance optimization. The robustness and practical relevance of the framework are demonstrated through empirical validation utilizing 2020-2024 USDT, ETH, and BTC data.