Quantifying Crypto Portfolio Risk: A Simulation-Based Framework Integrating Volatility, Hedging, Contagion, and Monte Carlo Modeling
By: Kiarash Firouzi
Potential Business Impact:
Helps predict crypto crashes and protect money.
Plain English Summary
This helps people who invest in cryptocurrencies better protect their money from sudden crashes and unpredictable market swings. It does this by simulating different worst-case scenarios, showing how risks can spread, and suggesting ways to reduce losses. Anyone holding crypto can use this to make smarter, safer investment choices instead of guessing or panicking during downturns. This means more confidence and stability for your digital savings, even when the market gets wild.
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.
Similar Papers
A Predictive Framework Integrating Multi-Scale Volatility Components and Time-Varying Quantile Spillovers: Evidence from the Cryptocurrency Market
General Economics
Predicts big money swings in digital coins.
Optimising cryptocurrency portfolios through stable clustering of price correlation networks
Popular Physics
Finds hidden crypto patterns for steady profits.
Crypto Inverse-Power Options and Fractional Stochastic Volatility
Pricing of Securities
Protects against sudden crypto price drops.