Paper: arXiv 2507.08915

Authors: Kiarash Firouzi

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.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 5.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a structured, modular simulation framework incorporating Monte Carlo methods, contagion models, and stablecoin hedging for crypto portfolios. While it synthesizes standard financial and graph-theoretic techniques, the empirical validation relies on basic asset choices (BTC, ETH, USDT) without introducing radically new methodologies.

Research Flowchart

  flowchart TD
    A["Research Goal: Quantify Crypto Portfolio Risk via Modular Simulation"] --> B["Data Inputs: BTC, ETH, USDT (2020-2024)"]
    B --> C["Volatility Stress Testing & Stablecoin Hedging"]
    C --> D["Contagion Modeling & Stochastic Monte Carlo Simulation"]
    D --> E["Mean-Variance Portfolio Optimization"]
    E --> F["Outcome: Robust Risk Analysis Addressing Volatility & Systemic Fragility"]

Cited References (39)

  1. Bitcoin: A peer-to-peer electronic cash system (2008) — link
  2. Common Risk Factors in Cryptocurrency (2019) — link
  3. Cryptocurrency market reactions to regulatory news (2019) — link
  4. Risk Measurement of Cryptocurrencies Using Value at Risk and Expected Shortfall (2022) — link
  5. Systemic risk, contagion, and financial networks: A survey (2015) — link
  6. DeFi Risks and Regulation (2023) — link
  7. The Global Political Economy of Digital Technology (2024) — link
  8. International business and decentralized finance (2024) — link
and 31 more
  1. Tail risk measurement in crypto-asset markets (2021) — link
  2. Extreme Value Behavior in Cryptocurrency Market (2023) — link
  3. Value-at-risk and expected shortfall in cryptocurrencies’ portfolio: A vine copula–based approach (2020) — link
  4. Modelling and forecasting the volatility of cryptocurrencies: A comparison of nonlinear garch-type models (2020) — link
  5. Vine copula-based dependence and portfolio value-at-risk analysis of the cryptocurrency market (2019) — link
  6. An approach to predict and forecast the price of constituents and index of cryptocurrency using machine learning (2020) — link
  7. Developing a stress testing framework based on market risk models (2008) — link
  8. Stablecoin devaluation risk (2025) — link
  9. Contagion!: Systemic Risk in Financial Networks (2016) — link
  10. Modelling the social dynamics of contagion and discovery using dynamical processes on complex networks. (2021) — link
  11. Introduction to monte carlo simulation (2010) — link
  12. Second order approximation for the customer time in queue distribution under the FIFO service discipline (2003) — link
  13. Regulation of cryptocurrencies and blockchain technologies (2018) — link
  14. Crypto-Native Credit Score: Between Financial Inclusion and Predatory Lending (2023) — link
  15. The Bitcoin gold correlation puzzle (2018) — link
  16. The inefficiency of Bitcoin revisited: A dynamic approach (2017) — link
  17. Is Bitcoin a Safe Haven for US Equity Market?–A Comparison with Gold and the Dollar (2023) — link
  18. Dynamic volatility modelling of Bitcoin using time-varying transition probability Markov-switching GARCH model (2021) — link
  19. Volatility co-movement between Bitcoin and Ether (2019) — link
  20. On multivariate contribution measures of systemic risk with applications in cryptocurrency market — → 2411.13384
  21. Review of blockchain technology vulnerabilities and blockchain-system attacks (2019) — link
  22. Blockchain technology and crypto-assets market analysis: vulnerabilities and risk assessment (2021) — link
  23. Beyond risk parity–A machine learning-based hierarchical risk parity approach on cryptocurrencies (2021) — link
  24. A systematic literature review of volatility and risk management on cryptocurrency investment: A methodological point of view (2022) — link
  25. Stablecoins: risks, potential and regulation (2020) — link
  26. Intelligent design: stablecoins (in) stability and collateral during market turbulence (2023) — link
  27. Algorithmic Stablecoins: Mechanisms, Risks, and Lessons from the Fall of TerraUSD (2025) — link
  28. Game-based modeling of delayed risk contagion in cryptocurrency exchanges (2025) — link
  29. Markowitz revisited: Mean-variance models in financial portfolio analysis (2001) — link
  30. Critical appraisal of the Basel fundamental review of the trading book regulations (2017) — link
  31. Arbitrage theory in continuous time (4th edn) (2019) — link

1 of 39 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.