---
title: Dynamic Agency Hedging
url: https://www.ml-quant.com/papers/ssrn/4903495/
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 4903495
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4903495
featured: 2024-07-24
citations: unknown
topic: Derivatives & Volatility
---


# Dynamic Agency Hedging

The research suggests a dynamic financial hedging strategy to spread a firm’s risk, demonstrating that reduced hedging costs result in increased investor value and firm investment.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4903495
- Identifier: SSRN 4903495
- Released: 2024-07-23
- First featured: Quant Letter No. 58 (2024-07-24): https://www.ml-quant.com/issues/2024-07-24/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Risk Revisited](https://www.ml-quant.com/papers/ssrn/4825844/): The study identifies recency, cluster, and sign as three factors shaping investors' risk perceptions of a stock, influencing trading volume and future volatility.
- [Rough Volatility: Fact or Artefact?](https://www.ml-quant.com/papers/arxiv/2203.13820/): Fact or Artifact: The study proposes a new method to estimate the roughness of financial asset volatility, attributing observed roughness to microstructure noise.
- [Is the difference between deep hedging and delta hedging a statistical arbitrage?](https://www.ml-quant.com/papers/arxiv/2407.14736/): The research compares deep hedging and delta hedging in a GARCH-based market model, suggesting that the difference between the two can be a statistical arbitrage if the risk measure doesn't adequately consider negative outcomes.
- [The Self-Organized Criticality Paradigm in Economics&Finance](https://www.ml-quant.com/papers/arxiv/2407.10284/): The article proposes Self-Organised Criticality as a reason for extreme volatility in financial markets and large business cycle fluctuations, calling for specific policy considerations.
- [Enhancing Black-Scholes Delta Hedging via Deep Learning](https://www.ml-quant.com/papers/arxiv/2407.19367/): The article introduces a deep learning framework for options hedging that enhances performance by learning the differences between the hedging function and the Black-Scholes delta, using less data for effective results.
- [CVA Sensitivities, Hedging and Risk](https://www.ml-quant.com/papers/arxiv/2407.18583/): The study offers a unified framework for calculating CVA sensitivities, hedging the CVA, and evaluating CVA risk using probabilistic machine learning, while also identifying the best practical trade-offs.