RCIN and OZwRCIN projects

Object

Title: Price Volatility Spillovers among Agricultural and Energy Commodity Markets: The Perspective of European Markets During the COVID-19 Pandemic and the Russia-Ukraine War

Creator:

Just, Małgorzata ORCID ; Echaust, Krzysztof ORCID

Date issued/created:

2023

Resource type:

Text

Publisher:

Instytut Rozwoju Wsi i Rolnictwa Polskiej Akademii Nauk

Place of publishing:

Warszawa

Description:

25 cm

Type of object:

Journal/Article

Abstract:

The study aims to assess the price volatility connectedness across agricultural and energy futures markets, and in particular, to identify the markets that are the main sources of price volatility among the markets considered. We analysed volatility spillovers among wheat, maize, rapeseed, Brent oil and natural gas on the Euronext and ICE exchange in the period from January 2017 to January 2023. We used the spillover index of Diebold and Yilmaz based on a generalised forecast error variance decomposition and its frequency extension of Barunik and Křehlík. The period from the outbreak of the COVID-19 pandemic to the beginning of 2023 brings an increase in price volatility in the food and energy markets. In the COVID-19 pandemic, the volatility spillover effect among markets was twice as strong as in 2017–2019, and three times stronger than during the Russia–Ukraine war. The main source of market shocks during the spread of the SARS-CoV-2 virus was the rapeseed market, while during the war in Ukraine this role was taken over by the wheat market. The volatility was not immediately transferred, thus providing an opportunity to implement risk management procedures to mitigate the impact of shocks from one market to another.

References:

Abbott P.C., Hurt C., Tyner W.E. (2009). What’s Driving Food Prices? Farm Foundation Issue Report. Oak Brook, IL., 58–64. (dostęp: 28.03.2023).
Ang A., Bekaert G. (2002). International asset allocation with regime shifts. Review of Financial Studies, 15 (4), 1137–1187. DOI
Antonakakis N., Chatziantoniou I., Gabauer D. (2020). Refined measures of dynamic connectedness based on time-varying parameter vector autoregressions. Journal of Risk and Financial Management, 13 (4), 84. DOI
Baffes J. (2007). Oil spills on other commodities. Resources Policy, 32 (3), 126–134. DOI
Balcilar M., Bekun F.V. (2020). Do oil prices and exchange rates account for agricultural commodity market spillovers? Evidence from the Diebold and Yilmaz Index. Agrekon, 59 (3), 366–385.. DOI
Barbaglia L., Croux C., Wilms I. (2020). Volatility spillovers in commodity markets: A large t-vector autoregressive approach. Energy Economics, 85, 104555. DOI
Baruník J., Křehlík T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16 (2), 271–296. DOI
Bouri E., Demirer R., Gupta R., Pierdzioch C. (2020). Infectious diseases, market uncertainty and oil market volatility. Energies, 13 (16), 4090. DOI
Chang C.-L., McAleer M., Wong W.-K. (2020). Risk and financial management of COVID-19 in business, economics and fi nance. Journal of Risk and Financial Management, 13 (5), 102. DOI
Chang T.-H., Su H.-M. (2010). The substitutive effect of biofuels on fossil fuels in the lower and higher crude oil price periods. Energy, 35 (7), 2807–2813. . DOI
Czech K., Górska A., Kozioł-Kaczorek D. (2019). Związki cenowe towarów w warunkach finansjeryzacji gospodarki na przykładzie cen ropy naftowej, złota i pszenicy. Warszawa: Wydawnictwo SGGW.
Diebold F.X., Yilmaz K. (2015). Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring. New York: Oxford University Press.
Diebold F.X., Yilmaz K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28 (1), 57–66. DOI
Diebold F.X., Yilmaz K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119 (534), 158–171. DOI
Du X., Yu C.L., Hayes D.J. (2011). Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis. Energy Economics, 33, 497–503. DOI
El Montasser G., Malek Belhoula M., Charfeddine L. (2023). Co-explosivity versus leading effects: Evidence from crude oil and agricultural commodities. Resources Policy, 81, 103331. DOI
Euronext (2023). Commitments of Traders Report. (dostęp: 9.02.2023).
Farid S., Naeem M.A., Paltrinieri A., Nepal R. (2022). Impact of COVID-19 on the quantile con nectedness between energy, metals and agriculture commodities. Energy Economics, 109, 10596. DOI
Gong X., Liu Y., Wang X. (2021). Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method. International Review of Financial Analysis, 76, 101790. DOI
Hamulczuk M., Klimkowski C. (2011). Powiązania między cenami ropy a cenami pszenicy w Polsce. Roczniki Nauk Rolniczych, seria G, 98 (3), 176–190.
Hassen T.B., Bilali H.E. (2022). Impacts of the Russia-Ukraine war on global food security: Towards more sustainable and resilient food systems? Foods, 11 (15), 2301. DOI
Hung N.T. (2021). Oil prices and agricultural commodity markets: Evidence from pre and during CO VID-19 outbreak. Resources Policy, 73, 102236. DOI
IGC [International Grains Council] (2022a). Grain Market Report: Russia-Ukraine conflict. (dostęp: 27.01.2023).
IGC (2022b). Databank: Ukraine production and trade (main grains & oilseeds/products). (dostęp: 27.01.2023).
Irwin S.H., Sanders D.R. (2012). Financialization and structural change in commodity futures markets. Journal of Agricultural and Applied Economics, 44 (3), 371–396. DOI
Ji Q., Bouri E., Roubaud D., Shahzad S.J.H. (2018). Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model. Energy Economics, 75, 14–27. DOI
Just M., Echaust K. (2022). Dynamic spillover transmission in agricultural commodity markets: What has changed after the COVID-19 threat? Economics Letters, 217, 110671. DOI
Just M., Echaust K. (2020). Stock market returns, volatility, correlation and liquidity during the COVID-19 crisis: Evidence from the Markov switching approach. Finance Research Letters, 37, 101775. DOI
Kumar D. (2017). On volatility transmission from crude oil to agricultural commodities. Theoretical Economics Letters, 7, 87–101. DOI
Le Z., Su Y. (2020). Dynamic spillovers between international crude oil market and China’s commodity sectors: Evidence from time-frequency perspective of stochastic volatility. Frontiers in Energy Research, 8. DOI
Liu W. (2009). Analysis of co-integration and volatility spillover effects between Chinese and international agricultural products futures markets. 2009 International Conference on Management and Service Science, 10953556. (dostęp: 31.01.2023)
Nyga-Łukaszewska H., Aruga K. (2020). Energy prices and COVID-immunity: The case of crude oil and natural gas prices in the US and Japan. Energies, 13 (23), 6300. DOI
Pal D., Mitra S.K. (2020). Time-frequency dynamics of return spillover from crude oil to agricultural commodities. Applied Economics, 52 (49), 5426–5445 DOI
Paris A. (2018). On the link between oil and agricultural commodity prices: Do biofuels matter? International Economics, 155, 48–60. DOI
Parkinson M. (1980). The extreme value method for estimating the variance of the rate of return. Journal of Business, 53 (1), 61–6 5.
Pesaran H.H., Shin Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58 (1), 17–29. DOI
Rokicki T., Perkowska A., Klepacki B., Bórawski P., Bełdycka-Bórawska A., Michalski K., (2021). Changes in energy consumption in agriculture in the EU countries. Energies, 14 (6), 1570. DOI
Rosiak E. (red.) (2021). Rynek rzepaku. Stan i perspektywy. Analizy Rynkowe, 59.
Shah A.A., Dar A.B. (2022). Asymmetric, time and frequency-based spillover transmission in financial and commodity markets. Journal of Economic Asymmetries, 25, e00241. DOI
Śmiech S., Papież M., Fijorek K., Dąbrowski M.A. (2019). What drives food price volatility? Evidence based on a generalized VAR approach applied to the food, financial and energy markets. Economics, 13 (1), 20190014. DOI
Taghizadeh-Hesary F., Rasouline zhad E., Yoshino N. (2019). Energy and food security: Linkages through price volatility. Energy Policy, 128, 796–806. DOI
Tang K., Xiong W. (2012). Index investment and financialization of commodities. Financial Analysts Journal, 68 (6), 54–74. DOI
Tibshirani R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58 (1), 267–288.
Tiwari A.K., Abakah E.J.A., Adewuyi A.O., Lee C.-C. (2022). Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak. Energy Economics, 113, 106235. DOI
Tomaszewski J. (2015). Finansjalizacja a zmiany strukturalne na rynku towarów rolnych w pierwszych latach XXI w. Annales Universitatis Mariae Curie-Skłodowska. Sectio H. Oeconomia, 49 (4), 601–610. DOI
Wei C.C., Ch en S.M. (2016). Examining the relationship of crude oil future price return and agricultural future price return in US. International Journal of Energy Economics and Policy, 6 (1), 58–64.
Wheeler C.M., Baffes J., Kabundi A.N., Kindberg-Hanlon G., Nagle P.S.O., Ohnsorge F.L. (2020). Adding fuel to the fire: Cheap oil during the COVID-19 pandemic. Policy Research Working Paper Series 9320, The World Bank. (dostęp: 23.06.2021).
World Bank Group (2022). Commodity Markets Outlook: The Impact of the War in Ukraine on Commodity Markets, April 2022. A World Bank Report. Washington, DC: World Bank. (dostęp: 27.01.2023).
Xiarchos I.M., Burnett J.W. (2018). Dynamic volatility spillovers between agricultural and energy commodities. Journal of Agricultural and Applied Economics, 50 (3), 291–318. DOI
Yang J., Li Z., Miao H. (2021). Volatility spillovers in commodity futures markets: A network approach. Journal of Futures Markets, 41 (12), 1959–1987. DOI
Yang J., Qiu H., Huang J., Rozelle S. (2008). Fighting global food price rises in the developing world: The response of China and its effect on domestic and world markets. Agricultural Economics, 39 (Suppl. 1), 453–464. DOI

Relation:

Village & Agriculture

Volume:

2023

Issue:

2 (199)

Start page:

41

End page:

66

Detailed Resource Type:

Article

Resource Identifier:

oai:rcin.org.pl:241225

Source:

click here to follow the link

Language:

pl

Digitizing institution:

Institute of Rural and Agricultural Development of the Polish Academy of Sciences

Original in:

Institute of Rural and Agricultural Development of the Polish Academy of Sciences

Access:

Open

Object collections:

Last modified:

Jun 5, 2024

In our library since:

May 22, 2024

Number of object content downloads / hits:

1

All available object's versions:

https://www.rcin.org.pl/publication/277653

Show description in RDF format:

RDF

Show description in RDFa format:

RDFa

Show description in OAI-PMH format:

OAI-PMH

×

Citation

Citation style:

This page uses 'cookies'. More information