---
title: Portfolio Vulnerability to Systemic Risk: Vine Copula and APARCH-DCC Approach
url: https://www.ml-quant.com/papers/repec/spr-fininn-v-10-y-2024-i-1-d-10-1186-s40854-023-00559-2/
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: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00559-2
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1186%2Fs40854-023-00559-2%3Bh%3Drepec%3Aspr%3Afininn%3Av%3A10%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1186_s40854-023-00559-2
featured: 2024-01-30
citations: unknown
topic: Portfolio & Allocation
---


# Portfolio Vulnerability to Systemic Risk: Vine Copula and APARCH-DCC Approach

Vine Copula and APARCH-DCC Approach: The study assesses the sensitivity and robustness of the Conditional Value-at-Risk (CoVaR) systemic risk measure, finding that CoVaR estimates vary with portfolio strategy and are especially high for cryptocurrency portfolios.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1186%2Fs40854-023-00559-2%3Bh%3Drepec%3Aspr%3Afininn%3Av%3A10%3Ay%3A2024%3Ai%3A1%3Ad%3A10.1186_s40854-023-00559-2
- Identifier: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-023-00559-2
- Released: 2024-01-30
- First featured: Quant Letter No. 35 (2024-01-30): https://www.ml-quant.com/issues/2024-01-30/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [Partial Information in a Mean‐Variance Portfolio Selection Game](https://www.ml-quant.com/papers/arxiv/2312.04045/): The article investigates how limited information affects investors' wealth and systemic risk, using a model where investors adjust their strategies based on their wealth compared to others.
- [Quantifying Credit Portfolio sensitivity to asset correlations with interpretable generative neural networks](https://www.ml-quant.com/papers/arxiv/2309.08652/): A unique method using Variational Autoencoders has been suggested to measure credit portfolio Value-at-Risk sensitivity to asset correlations, providing a clearer latent space representation.
- [Set risk measures](https://www.ml-quant.com/papers/arxiv/2407.18687/): The study presents set risk measures, which extend traditional risk measures to sets of random variables, and establishes an axiom scheme for them, demonstrating their use in systemic risk, portfolio optimization, and decision-making under uncertainty.
- [On the Separability of Vector-Valued Risk Measures](https://www.ml-quant.com/papers/arxiv/2407.16878/): The study argues that convex vector-valued risk measures are not suitable for defining capital allocation rules in multi-asset markets for a variety of financial applications, including systemic risk measures.
- [The Concentration Risk Indicator: Raising the Bar for Financial Stability and Portfolio Performance Measurement](https://www.ml-quant.com/papers/arxiv/2408.07271/): The Concentration Risk Indicator (CRI) is a new tool designed to assess risks associated with concentrated portfolios, useful in areas such as insurance risk and product portfolio mixes, especially where wealth is concentrated in few tokens.
- [Risk Budget Portfolios With Convex Non-negative Matrix Factorization](https://www.ml-quant.com/papers/arxiv/2204.02757/): A risk factor budgeting portfolio allocation method using NMF outperforms classical methods for diversification in cryptocurrency and traditional asset portfolios.
