---
title: Hedge Fund Risk Management
url: https://www.ml-quant.com/papers/ssrn/5151327/
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 5151327
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5151327
featured: 2025-03-20
citations: unknown
topic: Derivatives & Volatility
---


# Hedge Fund Risk Management

Banks demand lower haircuts from hedge funds with more bargaining power in secured lending, potentially increasing the risk of insufficient haircuts based on standard value-at-risk models.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5151327
- Identifier: SSRN 5151327
- Released: 2025-02-25
- First featured: Quant Letter No. 89 (2025-03-20): https://www.ml-quant.com/issues/2025-03-20/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Hedge Funds in German Bonds](https://www.ml-quant.com/papers/ssrn/5057388/): Daily data (2005–2024) show hedge funds became key liquidity providers in German government bonds after 2015 as banks cut back due to higher balance‑sheet costs.
- [Measuring the Time-varying Systemic Risks of Hedge Funds](https://www.ml-quant.com/papers/ssrn/4807133/): A study defines hedge funds' systemic risk based on a banking index's sensitivity to extreme losses, finding that larger funds, use of leverage, and uncertain market conditions indicate higher systemic risk levels.
- [Hedge Fund Performance and Managerial Structure](https://www.ml-quant.com/papers/ssrn/4604909/): Solo-managed hedge funds perform better than team-managed ones in terms of abnormal returns and market volatility skills, but they also have higher idiosyncratic and tail risk, and are less likely to be liquidated.
- [Compounding Effects in Leveraged ETFs: Beyond the Volatility Drag Paradigm](https://www.ml-quant.com/papers/arxiv/2504.20116/): The performance of leveraged ETFs is shown to depend on return autocorrelation and dynamics, with daily-rebalanced LETFs boosting returns in momentum-driven markets.
- [PolyModel for Hedge Funds' Portfolio Construction Using Machine Learning](https://www.ml-quant.com/papers/arxiv/2412.11019/): The use of machine learning and PolyModel feature selection in hedge fund investments improves returns and portfolio optimization, but also increases volatility, questioning the reliability of larger funds.
- [A Risk Sensitive Contract-unified Reinforcement Learning Approach for Option Hedging](https://www.ml-quant.com/papers/arxiv/2411.09659/): The paper proposes a risk-sensitive reinforcement learning approach for dynamic hedging of options, reducing tail risk using historical market data.
