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Asymmetric GARCH models on price volatility of agricultural commodities

Author

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  • Tirngo Dinku

    (Bahir Dar University)

  • Gardachew Worku

    (Injibara University)

Abstract

This study aimed to estimate the best-fit volatility model. To meet this objective, data on the retail prices of agricultural commodities recorded from 2010 to 2020 from three regions and one city administration were collected from the central statistics agency (CSA). The researcher used asymmetric generalized autoregressive conditional heteroskedasticity models for estimating the volatility of price returns of agricultural commodity prices in Ethiopia. Asymmetric GARCH family models, specifically threshold GARCH, and exponential GARCH were applied to analyze the time-varying volatility of price returns of cereals, pulses, oilseeds, species, and root crops. The data analysis results revealed that the EGARCH model with normal distribution assumption of residuals was a best-fitted model for “teff”, “maize”, niger, “onion”, “potato”, and “red pepper”, and the TGARCH was a better-fitted model for the price volatility of “sorghum”, “barley”, and “beans”. However, the finding showed that no model was found to be the best fit for wheat price return in the sampled periods. In general, the study established the presence of time-varying conditional volatility, in which the effect of today’s shock remains in the forecast of variance for many periods in the future. It also specified the existence of a leverage effect, wherein the “bad” news and the “good” news of the same magnitude could have a different effect.

Suggested Citation

  • Tirngo Dinku & Gardachew Worku, 2022. "Asymmetric GARCH models on price volatility of agricultural commodities," SN Business & Economics, Springer, vol. 2(11), pages 1-17, November.
    • Handle: RePEc:spr:snbeco:v:2:y:2022:i:11:d:10.1007_s43546-022-00355-7
    DOI: 10.1007/s43546-022-00355-7
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    References listed on IDEAS

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    1. Hong, Yongmiao, 1996. "Consistent Testing for Serial Correlation of Unknown Form," Econometrica, Econometric Society, vol. 64(4), pages 837-864, July.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Lama, A. & Jha, G.K. & Paul, R.K. & Gurung, B., 2015. "Modelling and Forecasting of Price Volatility: An Application of GARCH and EGARCH Models," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 28(1).
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    6. Zakoian, Jean-Michel, 1994. "Threshold heteroskedastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 18(5), pages 931-955, September.
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    Cited by:

    1. Mutaju Isaack Marobhe & Jonathan Mukiza Peter Kansheba, 2023. "High frequency volatility spillover between oil and non-energy commodities during crisis and tranquil periods," SN Business & Economics, Springer, vol. 3(4), pages 1-27, April.

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    More about this item

    Keywords

    Volatility; Agricultural commodities; EGARCH; TGARCH; Leverage effect;
    All these keywords.

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • B26 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Financial Economics
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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