---
title: Univariate vs Multivariate Models for Forecasting Crude Oil Basis Volatility
url: https://www.ml-quant.com/papers/ssrn/4590792/
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 4590792
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4590792
featured: 2023-10-04
citations: unknown
topic: Derivatives & Volatility
---


# Univariate vs Multivariate Models for Forecasting Crude Oil Basis Volatility

Simple univariate models are more effective than multivariate models in predicting the volatility of oil futures basis, resulting in higher Sharpe ratios and better forecasting accuracy.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4590792
- Identifier: SSRN 4590792
- Released: 2023-10-03
- First featured: Quant Letter No. 18 (2023-10-04): https://www.ml-quant.com/issues/2023-10-04/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Predicting the volatility of major energy commodity prices: The dynamic persistence model](https://www.ml-quant.com/papers/arxiv/2402.01354/): The article proposes a new method for forecasting oil-based volatility that models varying persistence shocks together, improving predictions and surpassing standard models.
- [Deep Learning and GARCH Models for Financial Volatility](https://www.ml-quant.com/papers/ssrn/4589950/): A hybrid approach combining GARCH time series models with deep learning neural networks is proposed for forecasting financial volatility and risk, tested on S&P 500, gold, and Bitcoin prices.
- [Optimal Commodity Hedging](https://www.ml-quant.com/papers/ssrn/4594616/): The paper presents a new procurement policy for data-driven commodity purchasing, combining operational and financial instruments, and tests the policy on real market data of four major commodities.
- [New Tests of the Theory of Storage and the Theory of Normal Backwardation: Time and Frequency Dimensions](https://www.ml-quant.com/papers/ssrn/4617533/): A study of the oil futures market from 1986 to 2020 reveals patterns and relationships between inventory, basis, hedging pressure, and futures risk premium, emphasizing the importance of the data measurement period.
- [Bond-Commodity Volatility Spillover & Global Liquidity Risk](https://www.ml-quant.com/papers/repec/voj-journl-v-70-y-2023-i-1-p-71-100-id-604/): Research reveals significant volatility spillovers between gold and bond markets, and oil and some bond markets, suggesting limited diversification benefits for investors.
- [CFO Pay and Hedging](https://www.ml-quant.com/papers/ssrn/4551639/): The study explores the impact of a CFO's risk-taking incentives and equity compensation on the hedging strategy of US oil and gas companies.