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Transmission of risks between energy and agricultural commodities: Frequency time-varying VAR, asymmetry and portfolio management
Resources Policy ; 81:103339, 2023.
Article in English | ScienceDirect | ID: covidwho-2221301
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.
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Full text: Available Collection: Databases of international organizations Database: ScienceDirect Type of study: Prognostic study Language: English Journal: Resources Policy Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ScienceDirect Type of study: Prognostic study Language: English Journal: Resources Policy Year: 2023 Document Type: Article