---
title: Modeling Volatility Spillovers Between Petroleum and Stocks
url: https://www.ml-quant.com/papers/ssrn/5273787/
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 5273787
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5273787
featured: 2025-05-30
citations: unknown
topic: Derivatives & Volatility
---


# Modeling Volatility Spillovers Between Petroleum and Stocks

The study analyzes the relationship between petroleum prices and stock sector indices in Canada, Saudi Arabia, the US, and China, revealing diverse volatility interdependencies and fluctuating optimal portfolio weights and hedge ratios.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5273787
- Identifier: SSRN 5273787
- Released: 2025-05-29
- First featured: Quant Letter No. 99 (2025-05-30): https://www.ml-quant.com/issues/2025-05-30/
- 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.
- [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.
- [Farm size matters: A spatially explicit ecological-economic framework for biodiversity and pest management.](https://www.ml-quant.com/papers/arxiv/2505.17687/): Research shows that the success of eco-restoration strategies in European agriculture depends on farm size, with smaller farms benefiting economically from less pesticide use and hedgerow restoration, while larger farms face challenges.
- [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.