---
title: Natural Gas Pricing
url: https://www.ml-quant.com/papers/ssrn/5198298/
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 5198298
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5198298
featured: 2025-04-02
citations: unknown
topic: Derivatives & Volatility
---


# Natural Gas Pricing

The study suggests a no-arbitrage model that combines a seasonal stochastic convenience yield and a local volatility factor to accurately predict natural gas spot futures and options prices.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5198298
- Identifier: SSRN 5198298
- Released: 2025-03-29
- First featured: Quant Letter No. 91 (2025-04-02): https://www.ml-quant.com/issues/2025-04-02/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Deep Hedging of Green PPAs in Electricity Markets](https://www.ml-quant.com/papers/arxiv/2503.13056/): The paper introduces a 'deep hedging' approach using machine learning to develop hedging strategies in power markets, specifically for Green Power Purchase Agreements, which are subject to price and weather risks.
- [Option Pricing with Time-Varying Volatility Risk Aversion](https://www.ml-quant.com/papers/arxiv/2204.06943/): The paper presents a pricing kernel with fluctuating volatility risk aversion to account for changes in the pricing kernel's shape, showing reduced pricing errors in an empirical application to the S&P 500 index, the CBOE VIX, and option prices.
- [A statistical technique for cleaning option price data](https://www.ml-quant.com/papers/arxiv/2501.11164/): The article addresses three common issues in recorded option price datasets and suggests solutions to ensure the reliability of analyses based on these datasets.
- [Neural Jumps for Option Pricing](https://www.ml-quant.com/papers/arxiv/2506.05137/): A neural jump stochastic differential equation model is proposed for option pricing, combining neural networks with the traditional jump diffusion model, enhancing pricing accuracy.
- [Applying Informer for Option Pricing: A Transformer-Based Approach](https://www.ml-quant.com/papers/doi/10-5220-0013320900003890/): The research uses the Informer neural network for option pricing in financial markets, showing its improved performance over traditional models and improving financial forecasting.
- [Option pricing under non-Markovian stochastic volatility models: A deep signature approach](https://www.ml-quant.com/papers/arxiv/2508.15237/): The paper introduces a deep signature approach to asset pricing that simplifies rough stochastic differential equations into classical ones, addressing issues with non-Markovian stochastic volatility models.