---
title: Does CoVaR understate contagion? Conformal forecasts for crypto treasury companies
url: https://www.ml-quant.com/papers/ssrn/7665847/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-30
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 7665847
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7665847
featured: 2026-10-30
citations: unknown
topic: Crypto & DeFi
---


# Does CoVaR understate contagion? Conformal forecasts for crypto treasury companies

Conformal prediction corrects CoVaR understatement of crypto-treasury contagion by two- to fourfold, reducing simulated distress-day loss frequency from 30% to 7%.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7665847
- Identifier: SSRN 7665847
- Released: 2026-10-24
- First featured: Quant Letter No. 137 (2026-10-30): https://www.ml-quant.com/issues/2026-10-30/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Crypto & DeFi
- Authors: Lamia Sebai

## Related

- [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.
- [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.
- [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.
- [Electoral Risk and Crypto Contagion: Evidence from Global Market Connectedness](https://www.ml-quant.com/papers/ssrn/7613178/): Elections in major democracies raise cryptocurrency correlation, reducing portfolio diversification, with the effect driven by shifting expectations about future regulatory environments.
- [Predicting Crypto Contagion: Machine Learning Forecasts of Return Spillovers](https://www.ml-quant.com/papers/ssrn/7578304/): The study forecasts time-varying return spillovers among cryptocurrencies using machine learning, finding monetary policy and funding rates drive connectedness and classifying events that shift spillover composition into contagion types.
- [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.