Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak
Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Energy Economics
Abstract
The spillover effect is a significant factor impacting the volatility of commodity prices. Unlike earlier studies, this
research uses the rolling window-based Quantile VAR (QVAR) model to describe conditional volatility
spillover between energy, biofuel and agricultural commodity markets. Since the magnitude of connectedness
and spillover effects may switch between bearish and bullish market states over time, a QVAR model is a
relatively realistic and appropriate approach to capture connectedness as compared to the mean-based approaches of Diebold and Yilmaz (DY; 2009, 2012, & 2014), which are mostly used in the literature. To this end,
we employ volatility estimates by using the realized variance advanced by Parkinson (1980). Specifically, we
investigate the time-varying volatility spillovers and connectedness among agricultural markets (wheat, corn,
sugar, soyabean, coffee, and cotton), energy markets (gasoline, crude oil, and natural gas) and biofuel (ethanol)
markets from January 12, 2012, to May 10, 2021. By comparing our empirical analysis with results from the DY
spillover model, we establish that connectedness is stronger in the left and right quantiles than those in the mean
and median of the conditional distribution, emphasizing the importance of systematic risk spillovers during
extreme market movements. Furthermore, results find that volatility spillovers and connectedness in the right tail
is higher than in the left tail. In particular, we document significant volatility spillovers from agricultural markets
to energy markets during extreme markets conditions and observe the dominance of agricultural markets over
energy markets. To ascertain the impact of COVID-19 on the volatility of markets examined, we divide our
sample into sub-samples and observe significant variation in the level of volatility spillovers and connectedness
across the markets before and during the outbreak of COVID-19. Finally, some useful implications are summarized for investors’ portfolios and risk avoidance.
Description
Research Article
Keywords
Volatility spillovers, Quantile VAR model, COVID-19