IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v12y2024i5p73-d1380791.html
   My bibliography  Save this article

Estimation and Prediction of Commodity Returns Using Long Memory Volatility Models

Author

Listed:
  • Kisswell Basira

    (Department of Mathematics and Computational Sciences, University of Zimbabwe, Harare P.O. Box MP167, Zimbabwe)

  • Lawrence Dhliwayo

    (Department of Mathematics and Computational Sciences, University of Zimbabwe, Harare P.O. Box MP167, Zimbabwe)

  • Knowledge Chinhamu

    (School of Mathematics, Statistics and Computer Science, University of KwaZulu Natal, Durban 3630, South Africa)

  • Retius Chifurira

    (School of Mathematics, Statistics and Computer Science, University of KwaZulu Natal, Durban 3630, South Africa)

  • Florence Matarise

    (Department of Mathematics and Computational Sciences, University of Zimbabwe, Harare P.O. Box MP167, Zimbabwe)

Abstract

Modelling the volatility of commodity prices and creating more reliable models for estimating and forecasting commodity price returns are crucial. The body of research on statistical models that can fully reflect the empirical characteristics of commodity price returns is lacking. The main aim of this research was to develop a modelling framework that could be used to accurately estimate and forecast commodity price returns by combining long memory models with heavy-tailed distributions. This study employed dual hybrid long-memory generalised autoregressive conditionally heteroscedasticity (GARCH) models with heavy-tailed innovations, namely, the Student- t distribution (S t D), skewed-Student- t distribution (SS t D), and the generalised error distribution (GED). Based on the smallest forecasting metrics values for mean absolute error (MAE) and mean squared error (MSE) values, the best performing LM-GARCH-type model for lithium is the ARFIMA (1, o , 1)- FIAPARCH (1, ξ , 1) with normal innovations. For tobacco, the best model is ARFIMA (1, o , 1) -FIGARCH (1, ξ , 1) with SS t D innovations. The robust performing model for gold is the ARFIMA (1, o , 1)-FIGARCH (1, ξ , 1)-GED model. The best performing forecasting model for crude oil and cotton returns are the F I A P A R C H 1 , ξ , 1 − S S t D model and H Y G A R C H 1 , ξ , 1 − S t D model, respectively. The results obtained from this study would be beneficial to those concerned with financial market modelling techniques, such as derivative pricing, risk management, asset allocation, and valuation.

Suggested Citation

  • Kisswell Basira & Lawrence Dhliwayo & Knowledge Chinhamu & Retius Chifurira & Florence Matarise, 2024. "Estimation and Prediction of Commodity Returns Using Long Memory Volatility Models," Risks, MDPI, vol. 12(5), pages 1-20, April.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:5:p:73-:d:1380791
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/12/5/73/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/12/5/73/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Christian Conrad & Berthold R. Haag, 2006. "Inequality Constraints in the Fractionally Integrated GARCH Model," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 413-449.
    2. Moosa, Imad A. & Al-Loughani, Nabeel E., 1994. "Unbiasedness and time varying risk premia in the crude oil futures market," Energy Economics, Elsevier, vol. 16(2), pages 99-105, April.
    3. Purczyński Jan & Bednarz-Okrzyńska Kamila, 2014. "Estimation of the Shape Parameter of Ged Distribution for a Small Sample Size," Folia Oeconomica Stetinensia, Sciendo, vol. 14(1), pages 35-46, June.
    4. Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
    5. Mert URAL & C. Coskun KUCUKOZMEN, 2011. "Analyzing the Dual Long Memory in Stock Market Returns," Ege Academic Review, Ege University Faculty of Economics and Administrative Sciences, vol. 11(Special I), pages 19-28.
    6. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    7. Brenner, Robin J. & Kroner, Kenneth F., 1995. "Arbitrage, Cointegration, and Testing the Unbiasedness Hypothesis in Financial Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 30(1), pages 23-42, March.
    8. Y. K. Tse, 1998. "The conditional heteroscedasticity of the yen-dollar exchange rate," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(1), pages 49-55.
    Full references (including those not matched with items on IDEAS)

    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. repec:ipg:wpaper:2013-009 is not listed on IDEAS
    2. Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Energy Economics, Elsevier, vol. 41(C), pages 1-18.
    3. Carl Lönnbark, 2016. "Asymmetry with respect to the memory in stock market volatilities," Empirical Economics, Springer, vol. 50(4), pages 1409-1419, June.
    4. Klein, Tony & Walther, Thomas, 2017. "Fast fractional differencing in modeling long memory of conditional variance for high-frequency data," Finance Research Letters, Elsevier, vol. 22(C), pages 274-279.
    5. Morten Ørregaard Nielsen & Antoine L. Noël, 2020. "To infinity and beyond: Efficient computation of ARCH(1) models," CREATES Research Papers 2020-13, Department of Economics and Business Economics, Aarhus University.
    6. Adnen Ben Nasr & Ahdi Noomen Ajmi & Rangan Gupta, 2014. "Modelling the volatility of the Dow Jones Islamic Market World Index using a fractionally integrated time-varying GARCH (FITVGARCH) model," Applied Financial Economics, Taylor & Francis Journals, vol. 24(14), pages 993-1004, July.
    7. Dark, Jonathan, 2018. "Multivariate models with long memory dependence in conditional correlation and volatility," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 162-180.
    8. repec:ipg:wpaper:9 is not listed on IDEAS
    9. repec:ipg:wpaper:201409 is not listed on IDEAS
    10. Sang Hoon Kang & SEONG-MIN YOON, 2008. "Asymmetry and Long Memory Features in Volatility: Evidence From Korean Stock Market," Korean Economic Review, Korean Economic Association, vol. 24, pages 383-412.
    11. Walid Chkili & Shawkat Hammoudeh & Duc Khuong Nguyen, 2013. "Long memory and asymmetry in the volatility of commodity markets and Basel Accord: choosing between models," Working Papers 2013-9, Department of Research, Ipag Business School.
    12. Demiralay, Sercan & Ulusoy, Veysel, 2014. "Value-at-risk Predictions of Precious Metals with Long Memory Volatility Models," MPRA Paper 53229, University Library of Munich, Germany.
    13. Mohamed Chikhi & Anne Péguin-Feissolle & Michel Terraza, 2013. "SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence," Computational Economics, Springer;Society for Computational Economics, vol. 41(2), pages 249-265, February.
    14. CHIA-LIN CHANG & MICHAEL McALEER & ROENGCHAI TANSUCHAT, 2012. "Modelling Long Memory Volatility In Agricultural Commodity Futures Returns," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 1-27.
    15. Stavros Stavroyiannis, 2017. "A note on the Nelson Cao inequality constraints in the GJR-GARCH model: Is there a leverage effect?," Papers 1705.00535, arXiv.org.
    16. Bhaskar Bagchi & Biswajit Paul, 2023. "Effects of Crude Oil Price Shocks on Stock Markets and Currency Exchange Rates in the Context of Russia-Ukraine Conflict: Evidence from G7 Countries," JRFM, MDPI, vol. 16(2), pages 1-18, January.
    17. Jeffrey A Frankel & Andrew K Rose, 2010. "Determinants of Agricultural and Mineral Commodity Prices," RBA Annual Conference Volume (Discontinued), in: Renée Fry & Callum Jones & Christopher Kent (ed.),Inflation in an Era of Relative Price Shocks, Reserve Bank of Australia.
    18. Aloui, Chaker & Mabrouk, Samir, 2010. "Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models," Energy Policy, Elsevier, vol. 38(5), pages 2326-2339, May.
    19. Trino-Manuel Ñíguez, 2008. "Volatility and VaR forecasting in the Madrid Stock Exchange," Spanish Economic Review, Springer;Spanish Economic Association, vol. 10(3), pages 169-196, September.
    20. HAFNER, Christian & PREMINGER, Arie, 2016. "On Asymptotic Theory for ARCH(infinite) Models," LIDAM Discussion Papers CORE 2016030, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    21. Abderrazak Ben Maatoug & Rim Lamouchi & Russell Davidson & Ibrahim Fatnassi, 2018. "Modelling Foreign Exchange Realized Volatility Using High Frequency Data: Long Memory versus Structural Breaks," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 10(1), pages 1-25, March.
    22. Lee, Oesook, 2018. "Stationarity and functional central limit theorem for ARCH(∞) models," Economics Letters, Elsevier, vol. 162(C), pages 107-111.
    23. Stavroyiannis, S. & Makris, I. & Nikolaidis, V. & Zarangas, L., 2012. "Econometric modeling and value-at-risk using the Pearson type-IV distribution," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 10-17.

    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:gam:jrisks:v:12:y:2024:i:5:p:73-:d:1380791. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.