IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/117003.html
   My bibliography  Save this paper

Transmission of risks between energy and agricultural commodities: Frequency time-varying VAR, asymmetry and portfolio management

Author

Listed:
  • Furuoka, Fumitaka
  • Yaya, OlaOluwa S
  • Ling, Piu Kiew
  • Al-Faryan, Mamdouh Abdulaziz Saleh
  • Islam, M. Nazmul

Abstract

This paper examines energy and agricultural commodities' short-run and long-run connectedness by using the Time-varying parameter vector autoregressions (TVP-VAR). It applies the frequency version of the TVP-VAR model, which is a modified version of the dynamic TVP-VAR model. The frequency decomposition definition also decomposes into short-run and long-run connectedness. We further the analysis by investigating the effect of asymmetry in returns on connectedness. It also examines how portfolio management strategies would lead to a maximization of profits with minimal risks. Empirical evidence indicates that only 32.52% and 31.38% of connectedness in oil and gas, respectively, are transmitted to agricultural commodities, which suggests their weak tendencies in influencing agricultural commodities; the total connectedness index hovers around 40-60% in the 2018-2019 period; however, it dropped below 40% in 2020-2021 when the COVID-19 pandemic contributed to disintegrate the connectedness between energy and agricultural commodities but increased further during the 2022 Russia-Ukraine saga. The findings also indicate that corn, wheat, and flour are net transmitters of risks to oil and natural gas in the long and short-run, and wheat-flour pairwise connectedness is the strongest in the connectedness. Asymmetry is also pronounced in the network of connectedness. Portfolio analyses indicate that investors require a low proportion of energy in a portfolio of energy-agricultural commodities to achieve an optimum profit. The findings will offer exciting insights into the connectedness of agricultural and energy commodities, particularly during periods of high price uncertainty.

Suggested Citation

  • Furuoka, Fumitaka & Yaya, OlaOluwa S & Ling, Piu Kiew & Al-Faryan, Mamdouh Abdulaziz Saleh & Islam, M. Nazmul, 2023. "Transmission of risks between energy and agricultural commodities: Frequency time-varying VAR, asymmetry and portfolio management," MPRA Paper 117003, University Library of Munich, Germany, revised 04 Dec 2022.
    • Handle: RePEc:pra:mprapa:117003
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/117003/1/MPRA_paper_117003.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Luis Alberiko & OlaOluwa S. Yaya & Olarenwaju I. Shittu, 2015. "Fractional integration and asymmetric volatility in european, asian and american bull and bear markets. Applications to high frequency stock data," NCID Working Papers 07/2015, Navarra Center for International Development, University of Navarra.
    2. Antonakakis, Nikolaos & Gabauer, David & Gupta, Rangan, 2019. "International monetary policy spillovers: Evidence from a time-varying parameter vector autoregression," International Review of Financial Analysis, Elsevier, vol. 65(C).
    3. Adekoya, Oluwasegun B. & Akinseye, Ademola B. & Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Gabauer, David & Oliyide, Johnson, 2022. "Crude oil and Islamic sectoral stocks: Asymmetric TVP-VAR connectedness and investment strategies," Resources Policy, Elsevier, vol. 78(C).
    4. Malik, Farooq & Umar, Zaghum, 2019. "Dynamic connectedness of oil price shocks and exchange rates," Energy Economics, Elsevier, vol. 84(C).
    5. Gabauer, David, 2021. "Dynamic measures of asymmetric & pairwise connectedness within an optimal currency area: Evidence from the ERM I system," Journal of Multinational Financial Management, Elsevier, vol. 60(C).
    6. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    7. Taghizadeh-Hesary, Farhad & Rasoulinezhad, Ehsan & Yoshino, Naoyuki, 2018. "Volatility Linkages between Energy and Food Prices: Case of Selected Asian Countries," ADBI Working Papers 829, Asian Development Bank Institute.
    8. Gil-Alana, Luis A. & Gupta, Rangan & Olubusoye, Olusanya E. & Yaya, OlaOluwa S., 2016. "Time series analysis of persistence in crude oil price volatility across bull and bear regimes," Energy, Elsevier, vol. 109(C), pages 29-37.
    9. Ederington, Louis H, 1979. "The Hedging Performance of the New Futures Markets," Journal of Finance, American Finance Association, vol. 34(1), pages 157-170, March.
    10. Mo, Bin & Meng, Juan & Zheng, Liping, 2022. "Time and frequency dynamics of connectedness between cryptocurrencies and commodity markets," Resources Policy, Elsevier, vol. 77(C).
    11. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    12. Awartani, Basel & Maghyereh, Aktham I. & Shiab, Mohammad Al, 2013. "Directional spillovers from the U.S. and the Saudi market to equities in the Gulf Cooperation Council countries," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 27(C), pages 224-242.
    13. Awartani, Basel & Maghyereh, Aktham Issa, 2013. "Dynamic spillovers between oil and stock markets in the Gulf Cooperation Council Countries," Energy Economics, Elsevier, vol. 36(C), pages 28-42.
    14. Parker, Miles, 2018. "How global is “global inflation”?," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 174-197.
    15. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    16. Gil-Alana, Luis A. & Yaya, OlaOluwa S., 2014. "The relationship between oil prices and the Nigerian stock market. An analysis based on fractional integration and cointegration," Energy Economics, Elsevier, vol. 46(C), pages 328-333.
    17. Ola Oluwa S. Yaya & Luis A. Gil‐Alana & Olanrewaju I. Shittu, 2015. "Fractional Integration and Asymmetric Volatility in European, American and Asian Bull and Bear Markets: Application to High‐frequency Stock Data," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 20(3), pages 276-290, July.
    18. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & Perez de Gracia, Fernando, 2018. "Oil volatility, oil and gas firms and portfolio diversification," Energy Economics, Elsevier, vol. 70(C), pages 499-515.
    19. Jozef Baruník & Tomáš Křehlík, 2018. "Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 271-296.
    20. Awe, O. O. & Akinlana, D. M. & Yaya, O. S. & Aromolaran, O., 2018. "Time Series Analysis of the Behaviour of Import and Export of Agricultural and Non-Agricultural Goods in West Africa: A Case Study of Nigeria," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 10(2).
    21. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & de Gracia, Fernando Perez, 2020. "Oil and asset classes implied volatilities: Investment strategies and hedging effectiveness," Energy Economics, Elsevier, vol. 91(C).
    22. Kroner, Kenneth F. & Sultan, Jahangir, 1993. "Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(4), pages 535-551, December.
    23. Adekoya, Oluwasegun B. & Oliyide, Johnson A., 2020. "The hedging effectiveness of industrial metals against different oil shocks: Evidence from the four newly developed oil shocks datasets," Resources Policy, Elsevier, vol. 69(C).
    24. Ioannis Chatziantoniou & David Gabauer & Rangan Gupta, 2021. "Integration and Risk Transmission in the Market for Crude Oil: A Time-Varying Parameter Frequency Connectedness Approach," Working Papers 202147, University of Pretoria, Department of Economics.
    25. Duncan, Andrew S. & Kabundi, Alain, 2013. "Domestic and foreign sources of volatility spillover to South African asset classes," Economic Modelling, Elsevier, vol. 31(C), pages 566-573.
    26. Adrian R. Pagan & Kirill A. Sossounov, 2003. "A simple framework for analysing bull and bear markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 23-46.
    27. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    28. Cronin, David, 2014. "The interaction between money and asset markets: A spillover index approach," Journal of Macroeconomics, Elsevier, vol. 39(PA), pages 185-202.
    29. Battistini, Niccolò & Grapow, Helen & Hahn, Elke & Soudan, Michel, 2022. "Wage share dynamics and second-round effects on inflation after energy price surges in the 1970s and today," Economic Bulletin Boxes, European Central Bank, vol. 5.
    30. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," JRFM, MDPI, vol. 13(4), pages 1-23, April.
    31. Zhang, Bing & Wang, Peijie, 2014. "Return and volatility spillovers between china and world oil markets," Economic Modelling, Elsevier, vol. 42(C), pages 413-420.
    32. Zhang, Dayong, 2017. "Oil shocks and stock markets revisited: Measuring connectedness from a global perspective," Energy Economics, Elsevier, vol. 62(C), pages 323-333.
    33. Ji, Qiang & Bouri, Elie & Lau, Chi Keung Marco & Roubaud, David, 2019. "Dynamic connectedness and integration in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 257-272.
    34. Gong, Xu & Xu, Jun, 2022. "Geopolitical risk and dynamic connectedness between commodity markets," Energy Economics, Elsevier, vol. 110(C).
    35. Maghyereh, Aktham I. & Awartani, Basel & Bouri, Elie, 2016. "The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes," Energy Economics, Elsevier, vol. 57(C), pages 78-93.
    36. Thomas J. Fisher & Colin M. Gallagher, 2012. "New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 777-787, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chishti, Muhammad Zubair & Khalid, Ali Awais & Sana, Moniba, 2023. "Conflict vs sustainability of global energy, agricultural and metal markets: A lesson from Ukraine-Russia war," Resources Policy, Elsevier, vol. 84(C).
    2. Wu, You & Ren, Wenting & Wan, Jieru & Liu, Xiaoxue, 2023. "Time-frequency volatility connectedness between fossil energy and agricultural commodities: Comparing the COVID-19 pandemic with the Russia-Ukraine conflict," Finance Research Letters, Elsevier, vol. 55(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stenfors, Alexis & Chatziantoniou, Ioannis & Gabauer, David, 2022. "Independent policy, dependent outcomes: A game of cross-country dominoes across European yield curves," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    2. Balcilar, Mehmet & Gabauer, David & Umar, Zaghum, 2021. "Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach," Resources Policy, Elsevier, vol. 73(C).
    3. Evrim Mandacı, Pınar & Cagli, Efe Çaglar & Taşkın, Dilvin, 2020. "Dynamic connectedness and portfolio strategies: Energy and metal markets," Resources Policy, Elsevier, vol. 68(C).
    4. Tiwari, Aviral Kumar & Aikins Abakah, Emmanuel Joel & Gabauer, David & Dwumfour, Richard Adjei, 2022. "Dynamic spillover effects among green bond, renewable energy stocks and carbon markets during COVID-19 pandemic: Implications for hedging and investments strategies," Global Finance Journal, Elsevier, vol. 51(C).
    5. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & de Gracia, Fernando Perez, 2023. "Dynamic connectedness among the implied volatilities of oil prices and financial assets: New evidence of the COVID-19 pandemic," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 114-123.
    6. Adekoya, Oluwasegun B. & Oliyide, Johnson A., 2021. "How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques," Resources Policy, Elsevier, vol. 70(C).
    7. Juncal Cunado & David Gabauer & Rangan Gupta & Chien-Chiang Lee, 2022. "On the Propagation Mechanism of International Real Interest Rate Spillovers: Evidence from More than 200 Years of Data," Working Papers 202212, University of Pretoria, Department of Economics.
    8. Karol Szafranek & Michał Rubaszek & Gazi Salah Uddin, 2023. "The role of uncertainty and sentiment for intraday volatility connectedness between oil and financial markets," KAE Working Papers 2023-095, Warsaw School of Economics, Collegium of Economic Analysis.
    9. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2021. "Volatility spillovers during market supply shocks: The case of negative oil prices," Resources Policy, Elsevier, vol. 74(C).
    10. Chatziantoniou, Ioannis & Elsayed, Ahmed H. & Gabauer, David & Gozgor, Giray, 2023. "Oil price shocks and exchange rate dynamics: Evidence from decomposed and partial connectedness measures for oil importing and exporting economies," Energy Economics, Elsevier, vol. 120(C).
    11. Çelik, İsmail & Sak, Ahmet Furkan & Höl, Arife Özdemir & Vergili, Gizem, 2022. "The dynamic connectedness and hedging opportunities of implied and realized volatility: Evidence from clean energy ETFs," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    12. Evžen Kočenda & Michala Moravcová & Evžen Kocenda, 2024. "Frequency Volatility Connectedness and Portfolio Hedging of U.S. Energy Commodities," CESifo Working Paper Series 10889, CESifo.
    13. Gabauer, David & Chatziantoniou, Ioannis & Stenfors, Alexis, 2023. "Model-free connectedness measures," Finance Research Letters, Elsevier, vol. 54(C).
    14. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2022. "Long-memory and volatility spillovers across petroleum futures," Energy, Elsevier, vol. 243(C).
    15. Lee, Chien-Chiang & Zhou, Hegang & Xu, Chao & Zhang, Xiaoming, 2023. "Dynamic spillover effects among international crude oil markets from the time-frequency perspective," Resources Policy, Elsevier, vol. 80(C).
    16. Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022. "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, vol. 77(C).
    17. Wen, Fenghua & Cao, Jiahui & Liu, Zhen & Wang, Xiong, 2021. "Dynamic volatility spillovers and investment strategies between the Chinese stock market and commodity markets," International Review of Financial Analysis, Elsevier, vol. 76(C).
    18. Coskun, Merve & Taspinar, Nigar, 2022. "Volatility spillovers between Turkish energy stocks and fossil fuel energy commodities based on time and frequency domain approaches," Resources Policy, Elsevier, vol. 79(C).
    19. Cui, Jinxin & Maghyereh, Aktham, 2023. "Higher-order moment risk connectedness and optimal investment strategies between international oil and commodity futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict," International Review of Financial Analysis, Elsevier, vol. 86(C).
    20. Samia Nasreen & Aviral Kumar Tiwari & Seong-Min Yoon, 2021. "Dynamic Connectedness and Portfolio Diversification during the Coronavirus Disease 2019 Pandemic: Evidence from the Cryptocurrency Market," Sustainability, MDPI, vol. 13(14), pages 1-14, July.

    More about this item

    Keywords

    Agricultural commodity; Asymmetry; Frequency TVP-VAR; Optimal weight; Risk;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:117003. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.