---
title: Biomass Futures for Aquaculture
url: https://www.ml-quant.com/papers/ssrn/5263189/
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 5263189
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5263189
featured: 2025-05-21
citations: unknown
topic: Derivatives & Volatility
---


# Biomass Futures for Aquaculture

The article introduces a model using machine learning to help fish farmers hedge against production risks through biomass futures contracts.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5263189
- Identifier: SSRN 5263189
- Released: 2025-05-01
- First featured: Quant Letter No. 98 (2025-05-21): https://www.ml-quant.com/issues/2025-05-21/
- 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.
- [Foundation Time-Series AI Model for Realized Volatility Forecasting](https://www.ml-quant.com/papers/arxiv/2505.11163/): The study finds that the TimesFM model, with incremental fine-tuning, is effective for volatility forecasting in financial risk management, outperforming traditional models.
- [Revisiting the excess volatility puzzle through the lens of the Chiarella model](https://www.ml-quant.com/papers/arxiv/2505.07820/): The Chiarella model is expanded to handle long-term value drifts, revealing excess volatility and bimodal mispricings in various assets.
- [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.
- [Fast Derivative Valuation from Volatility Surfaces using Machine Learning](https://www.ml-quant.com/papers/arxiv/2505.22957/): The paper presents a Machine Learning framework for pricing derivative products based on volatility surfaces, offering accurate results and faster processing than traditional methods, thus facilitating real-time risk analytics and large-scale scenario analysis.