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Forecasting copper prices with dynamic averaging and selection models

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  • Buncic, Daniel
  • Moretto, Carlo

Abstract

We use data from the London Metal Exchange (LME) to forecast monthly copper returns using the recently proposed dynamic model averaging and selection (DMA/DMS) framework, which incorporates time varying parameters as well as model averaging and selection into one unifying framework. Using a total of 18 predictor variables that include traditional fundamental indicators such as excess demand, inventories and the convenience yield, as well as indicators related to global risk appetite, momentum, the term spread, and various other financial series, we show that there exists a considerable predictive component in copper returns. Covering an out-of-sample period from May 2002 to June 2014 and employing standard statistical evaluation criteria we show that the out-of-sample R2 (relative to a random walk benchmark) can be as high as 18.5 percent for the DMA framework. Time series plots of the cumulative mean squared forecast errors and time varying coefficients show further that firstly, a large part of the improvement in the forecasts is realised during the peak of the financial crisis period at the end of 2008, and secondly that the importance of the most relevant predictor variables has changed substantially over the out-of-sample period. The coefficients of the SP500, the VIX, the yield spread, the TED spread, industrial production and the convenience yield predictors are most heavily affected, with the TED spread and yield spread coefficients even changing signs over this period. Our predictability results remain valid for forecast horizons up to 6 months ahead, but are weaker and smaller than at the one month horizon.

Suggested Citation

  • Buncic, Daniel & Moretto, Carlo, 2015. "Forecasting copper prices with dynamic averaging and selection models," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 1-38.
  • Handle: RePEc:eee:ecofin:v:33:y:2015:i:c:p:1-38
    DOI: 10.1016/j.najef.2015.03.002
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    Cited by:

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    6. Buncic, Daniel & Piras, Gion Donat, 2016. "Heterogeneous agents, the financial crisis and exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 60(C), pages 313-359.
    7. Boriss Siliverstovs, 2015. "Dissecting Models' Forecasting Performance," KOF Working papers 15-397, KOF Swiss Economic Institute, ETH Zurich.
    8. Mark Cummins & Brian M. Lucey & Michael M. Dowling, 2014. "Behavioral Influences in Non-Ferrous Metals Prices," The Institute for International Integration Studies Discussion Paper Series iiisdp459, IIIS.
    9. repec:gam:jeners:v:11:y:2018:i:5:p:1207-:d:145404 is not listed on IDEAS
    10. Buncic, Daniel & Lentner, Philipp, 2016. "The term structure of interest rates in an estimated New Keynesian policy model," Journal of Macroeconomics, Elsevier, vol. 50(C), pages 126-150.
    11. Cummins, Mark & Dowling, Michael & Lucey, Brian M., 2015. "Behavioral influences in non-ferrous metals prices," Resources Policy, Elsevier, vol. 45(C), pages 9-22.
    12. Buncic, Daniel & Tischhauser, Martin, 2017. "Macroeconomic factors and equity premium predictability," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 621-644.
    13. Pincheira, Pablo & Hardy, Nicolas, 2018. "Forecasting Base Metal Prices with Commodity Currencies," MPRA Paper 83564, University Library of Munich, Germany.

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    Keywords

    Copper forecasting; Time varying parameter model; State-space modelling; Dynamic averaging and selection models;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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