---
title: Risk Management of Stock Portfolios With Jumps at Exogenous Default Events
url: https://www.ml-quant.com/papers/ssrn/4545190/
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 4545190
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4545190
featured: 2023-08-24
citations: 0
topic: Portfolio & Allocation
---


# Risk Management of Stock Portfolios With Jumps at Exogenous Default Events

The paper discusses managing equity risk in stock portfolios with defaults, deriving formulas for loss distributions and applying them to Value-at-Risk calculations.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4545190
- Identifier: SSRN 4545190
- Released: 2023-08-18
- First featured: Quant Letter No. 13 (2023-08-24): https://www.ml-quant.com/issues/2023-08-24/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

- [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.
- [Value-at-Risk-Based Portfolio Insurance: Performance Evaluation and Benchmarking Against CPPI in a Markov-Modulated Regime-Switching Market](https://www.ml-quant.com/papers/arxiv/2305.12539/): Study compares VaR-based and constant proportion portfolio insurance strategies in regime-switching framework.
- [Enhanced VaR Estimation](https://www.ml-quant.com/papers/repec/gam-jdataj-v-8-y-2023-i-8-p-133-d-1219341/): Machine learning is enhancing the accuracy and reliability of Value at Risk (VaR), a tool used in risk management for estimating potential portfolio losses.
- [VaR and ES Forecasting in Large Portfolios: A Dynamic Factor Model Approach](https://www.ml-quant.com/papers/repec/eee-ecosta-v-27-y-2023-i-c-p-1-15/): A Dynamic Factor Model Approach: The article introduces two superior methods for predicting and estimating Value-at-Risk (VaR) and Expected Shortfall (ES) in large portfolios.
- [Portfolio Vulnerability to Systemic Risk: Vine Copula and APARCH-DCC Approach](https://www.ml-quant.com/papers/repec/spr-fininn-v-10-y-2024-i-1-d-10-1186-s40854-023-00559-2/): 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.
- [Comparative Study of Portfolio Risk Management](https://www.ml-quant.com/papers/ssrn/4779957/): The article suggests a new method for portfolio risk management and capital allocation, combining value-at-risk with other statistical measures, proving its effectiveness in reducing potential portfolio losses.