---
title: Hedging Strategy with Transaction Costs
url: https://www.ml-quant.com/papers/ssrn/4990913/
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 4990913
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4990913
featured: 2024-10-23
citations: unknown
topic: Derivatives & Volatility
---


# Hedging Strategy with Transaction Costs

The traditional binomial model for derivative security pricing is enhanced to include transaction costs, portfolio constraints, and dividend-paying assets, aiming to identify the best hedging strategy.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4990913
- Identifier: SSRN 4990913
- Released: 2024-04-01
- First featured: Quant Letter No. 71 (2024-10-23): https://www.ml-quant.com/issues/2024-10-23/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Deep Gamma Hedging](https://www.ml-quant.com/papers/arxiv/2409.13567/): The study uses neural networks to determine optimal replication strategies for an option, indicating that gamma hedging is used to manage model uncertainty rather than to lessen transaction costs.
- [Why do financial prices exhibit Brownian motion despite predictable order flow?](https://www.ml-quant.com/papers/arxiv/2502.17906/): A new econophysics model unifies several empirical laws, including the square-root law of price impact, and shows that price dynamics remain diffusive under this law, with volatility having a long memory.
- [Deep Hedging with Options Using the Implied Volatility Surface](https://www.ml-quant.com/papers/arxiv/2504.06208/): A new deep hedging framework for index option portfolios, which includes surface-informed decisions and transaction costs, has been proposed and outperforms traditional methods in both simulated and historical data from 1996 to 2020.
- [Model-Free Deep Hedging with Transaction Costs and Light Data Requirements](https://www.ml-quant.com/papers/arxiv/2505.22836/): The research shows that a neural network trained with just 256 trajectories can outperform the Black & Scholes formula and the Leland model in the Geometric Brownian Motion framework, indicating potential for real-time financial series application.
- [FlowOE: Imitation Learning with Flow Policy from Ensemble RL Experts for Optimal Execution under Heston Volatility and Concave Market Impacts](https://www.ml-quant.com/papers/arxiv/2506.05755/): The article introduces flowOE, a new imitation learning framework that improves traditional financial market strategies, resulting in increased profits and lower risk.
- [Hedging Barrier Options Using Reinforcement Learning](https://www.ml-quant.com/papers/ssrn/4566384/): The research indicates that reinforcement learning can be an effective alternative to traditional hedging methods for barrier options, potentially reducing transaction costs due to fewer trades.
