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Corvinus

Modelling time-varying volatility spillovers across crises : evidence from major commodity futures and the US stock market

Ramesh, Shietal ORCID: https://orcid.org/0009-0000-6519-0018, Low, Rand Kwong Yew and Faff, Robert (2025) Modelling time-varying volatility spillovers across crises : evidence from major commodity futures and the US stock market. Energy Economics, 143 . DOI 10.1016/j.eneco.2025.108225

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Official URL: https://doi.org/10.1016/j.eneco.2025.108225


Abstract

Effective risk management requires discernment of volatility interaction patterns across assets. Our study examines the level of interconnectedness amongst nine major commodity futures across precious metals, energy, industrial and agricultural sectors and the US S&P 500 index from 1990 to 2022. Spillover indices are constructed by combining the Time-Varying Parameter (TVP)-Vector Autoregression (VAR)-Stochastic Volatility (SV) model with the DY- spillover index. We analyse the fluctuating dynamics of the extent and directionality of the volatility transmissions across various crises. Our results indicate that SPX is the largest net transmitter of volatility information, predominantly affecting crude oil, heating oil, and gold futures, with spillovers intensifying during crises. Gold futures receive heightened volatility transmissions during crises, alluding to the ‘‘flight to quality’’ characteristic displayed by investors. The COVID-19 crisis and the consequent supply chain disruptions uniquely heightened volatility transmissions from lumber to natural gas futures, unseen in previous economic crises. We posit that natural gas futures could be a viable asset for risk diversification as they show limited interaction with SPX and minimal within-sector transmissions with crude and heating oil futures. We substantiate our findings on potential hedge assets by constructing dynamic portfolio weights based on minimising pairwise volatility interactions between assets in the portfolio.

Item Type:Article
Uncontrolled Keywords:Volatility spillover index Bayesian sampling technique Crisis period Risk diversification
JEL classification:C50 - Econometric Modeling: General
C60 - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling: General
C80 - Data Collection and Data Estimation Methodology; Computer Programs: General
G10 - General Financial Markets: General (includes Measurement and Data)
Divisions:Institute of Finance
Subjects:Finance
DOI:10.1016/j.eneco.2025.108225
ID Code:10894
Deposited By: MTMT SWORD
Deposited On:07 Feb 2025 13:45
Last Modified:11 Feb 2025 15:40

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