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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
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    References listed on IDEAS

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