Crude oil and soft commodities volatility spillover patterns and portfolio diversification strategies in times of oil crises

This study employs the Diebold and Yilmaz (Int J Forecast 28:57–66, 2012) spillover model to investigate volatility spillover effects between crude oil and soft agricultural commodities. The results indicate a significant increase in systemic risk following the global financial crisis (GFC) of 2008–09, highlighting the importance of understanding the drivers of risk transmission. The findings underscore the need for strategic portfolio selection to mitigate extreme market fluctuations. Despite offering higher returns, crude oil exhibits substantial volatility in terms of spillover reception and transmission in the postcrisis period. Additionally, commodities such as rubber, sugar, and coffee are identified as major contributors to spillover intensification after the crisis. To assist in asset selection, this study proposes an asset allocation strategy based on a connectedness network derived from a spillover matrix designed to minimize systemic risk. The approach is applied to three crisis periods: the GFC (2008–09), the global commodity crisis (2014–15), and the COVID-19 pandemic (2020–21). In each case, a portfolio of four assets is selected, with the following commodities chosen: GFC (2008–09): wheat, rapeseed, and corn; the global commodity crisis (2014–15): cotton, rapeseed, and rice; and the COVID-19 pandemic (2020–21): wheat, rubber, and rice. The proposed approach provides valuable insights for policymakers, facilitating informed decision-making to address inflationary pressures and improve portfolio resilience during periods of market volatility.

Authors

Institutions

Publication Details

Journal
Financial Innovation
Published
2026-02-13
DOI
https://doi.org/10.1186/s40854-025-00851-3
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Crude oil and soft commodities volatility spillover patterns and portfolio diversification strategies in times of oil crises

Pawan Kumar, Vipul Kumar Singh
Financial Innovation
Market Dynamics and Volatility
article

Crude oil and soft commodities volatility spillover patterns and portfolio diversification strategies in times of oil crises

Pawan Kumar, Vipul Kumar Singh
article en

Abstract

This study employs the Diebold and Yilmaz (Int J Forecast 28:57–66, 2012) spillover model to investigate volatility spillover effects between crude oil and soft agricultural commodities. The results indicate a significant increase in systemic risk following the global financial crisis (GFC) of 2008–09, highlighting the importance of understanding the drivers of risk transmission. The findings underscore the need for strategic portfolio selection to mitigate extreme market fluctuations. Despite offering higher returns, crude oil exhibits substantial volatility in terms of spillover reception and transmission in the postcrisis period. Additionally, commodities such as rubber, sugar, and coffee are identified as major contributors to spillover intensification after the crisis. To assist in asset selection, this study proposes an asset allocation strategy based on a connectedness network derived from a spillover matrix designed to minimize systemic risk. The approach is applied to three crisis periods: the GFC (2008–09), the global commodity crisis (2014–15), and the COVID-19 pandemic (2020–21). In each case, a portfolio of four assets is selected, with the following commodities chosen: GFC (2008–09): wheat, rapeseed, and corn; the global commodity crisis (2014–15): cotton, rapeseed, and rice; and the COVID-19 pandemic (2020–21): wheat, rubber, and rice. The proposed approach provides valuable insights for policymakers, facilitating informed decision-making to address inflationary pressures and improve portfolio resilience during periods of market volatility.

Financial InnovationVol. 12(1)
Shiv Nadar University (IN), Dublin Business School (IE), Dublin City University (IE)
Openalex Percentile: Top 61%
Market Dynamics and Volatility
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.