Dynamic return and volatility connectedness for dominant agricultural commodity markets during the COVID-19 pandemic era

ABSTRACT

This paper explores the dynamic return and volatility connectedness for the three most relevant agricultural and livestock commodity indexes (Softs, Grains and Livestock) and a media sentiment index as the Coronavirus Media Coverage Index (MCI). To that purpose, we apply the fresh time-varying parameter vector autoregression methodology during the sample period between 1 January 2020 and 30 April 2021, that is, covering the three waves of the COVID-19 pandemic crisis. Interesting results are found in this research. First, dynamic total return and volatility connectedness fluctuate over time, reaching a peak during both the first and the third waves of the global pandemic crisis. Second, in the dynamic connectedness TO the system, we observe significant differences between markets at the level of the return connectedness measure. However, in the dynamic volatility connectedness TO, there are very few differences between some elements of the system. The Coronavirus MCI appears as the less relevant receiver FROM the system, not only in terms of dynamic return connectedness but also in volatility. Finally, regarding the net dynamic total connectedness, the Coronavirus MCI shows the highest values in return and volatility, during most of the sample period analysed.

KEYWORDS:

• Agricultural commodity markets
• Coronavirus Media Coverage Index (MCI)
• connectedness
• COVID-19 pandemic crisis

JEL CLASSIFICATION:

• C22
• C51
• Q02
• Q14

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ The COVID-19 pandemic crisis declared by the World Health Organization (WHO) on 11 March 2020 represents the major episode of worldwide turmoil since the Global Financial Crisis of 2007–2008. Goodell (2020) defines the COVID-19 pandemic as an unprecedented episode of global crisis with a destructive economic damage never seen before.

² A small size of the rolling window could involve an overreaction of the connectedness measure, while a large size could lead to smooth the effects.

³ This methodology has been also used in Gabauer and Gupta (2018) to analyse the economic policy uncertainty spillovers between the U.S.A. and Japan and in Antonakakis, Chatziantoniou, and Gabauer (2020) to study dynamic connectedness measures of the four most traded foreign exchange rates against the U.S. dollar.

⁴ Data series have been retrieved from Datastream.

⁵ RavenPack is a data analytics provider. They supply insights generated automatically from real-time news provided from over 22,000 sources of news and social media. Source: https://www.ravenpack.com/.

⁶ See https://covidtracking.com/ for detailed information about the evolution of the pandemic in the US.

⁷ The results using alternative volatility and sentiment index measures are available upon request. The results are virtually the same.

⁸ Most of the results obtained with the alternative measures are not included in the paper for reasons of space but are available on request.

Additional information

Funding

This work was supported by the Ministerio de Economia, Industria y Competitividad, Gobierno de Espana [ECO2017-89715-P]; Universidad de Castilla-La Mancha [2021-GRIN-31019].