---
title: Assessing Network Risk with FRM in Cryptos
url: https://www.ml-quant.com/papers/ssrn/4606863/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4606863
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4606863
featured: 2023-10-25
citations: unknown
topic: Crypto & DeFi
---


# Assessing Network Risk with FRM in Cryptos

The Financial Risk Meter uses quantile-LASSO regression to identify systemic financial risk and dependencies in the crypto market, showing strong predictive abilities for future systemic risk.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4606863
- Identifier: SSRN 4606863
- Released: 2023-10-19
- First featured: Quant Letter No. 23 (2023-10-25): https://www.ml-quant.com/issues/2023-10-25/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Crypto & DeFi

## Related

- [Assessing the solvency of virtual asset service providers: are current standards sufficient?](https://www.ml-quant.com/papers/arxiv/2309.16408/): Insights and Strategies: The article suggests a method to evaluate the financial stability of virtual asset service providers using data from crypto wallets, balance sheets, and regulatory bodies, emphasizing the importance of regular audits for crypto assets.
- [Tail Risk and Systemic Risk Estimation of Cryptocurrencies: an Expectiles and Marginal Expected Shortfall based approach](https://www.ml-quant.com/papers/arxiv/2311.17239/): The paper introduces an expectile-based approach to assess the tail risk of cryptocurrencies, presenting the Marginal Expected Shortfall as a tool to measure the impact of a single cryptocurrency on the market's systemic risk.
- [On Vulnerability Conditional Risk Measures: Comparisons and Applications in Cryptocurrency Market](https://www.ml-quant.com/papers/arxiv/2411.09676/): The article introduces Vulnerability Conditional risk measures, a new systemic risk measure to assess tail risk during financial distress among market participants.
- [Investor-Induced Contagion](https://www.ml-quant.com/papers/ssrn/5188945/): The study investigates the contagion mechanism from cryptocurrency to stock markets, identifying the wealth effect as the main driver and stressing the need for protection against cyberattacks.
- [Quantifying Crypto Portfolio Risk: A Simulation-Based Framework Integrating Volatility, Hedging, Contagion, and Monte Carlo Modeling](https://www.ml-quant.com/papers/arxiv/2507.08915/): The paper introduces a modular simulation framework for analyzing cryptocurrency portfolio risk, incorporating various testing and modeling methods, and validated with recent cryptocurrency data.
- [Factors Influencing Cryptocurrency Prices: Evidence from Bitcoin, Ethereum, Dash, Litcoin, and Monero](https://www.ml-quant.com/papers/arxiv/2511.22782/): The study examines the price factors influencing five major cryptocurrencies from 2010-2018, highlighting the roles of market conditions, long-term appeal, and the SP500 index.