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REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market

Author

Listed:
  • Paweł Jakubowski

    (University of Warsaw, Faculty of Economic Sciences)

  • Robert Ślepaczuk

    (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)

  • Franciszek Windorbski

    (University of Warsaw, Faculty of Management)

Abstract

This paper presents the results of investment strategies based on predictions from an ARIMA with exogenous variables (ARIMAX/ARIMAX-Garch) model, using the prices of selected commodities and companies from the DJIA index as explanatory variables. The explained variables are four Invesco ETF funds (DBE, DBA, DBP, DBB) corresponding to baskets of energy, agricultural, precious, and industrial metals. The models are optimized using the Walk-Forward technique, and the selection of exogenous variables is based on Granger causality tests. By analyzing the results, we conclude that ARIMAX/ARIMAX-Garch models are not useful tools for making buy or sell decisions for the selected commodity baskets. Out of the 80 estimated models, 44 outperform the Buy & Hold strategy, however, none achieved statistically significant results. Combining individual models into an investment portfolio reduced the risk without significantly reducing the profit, enabling us to consistently beat the benchmark. We also observe that using returns of commodities listed on stock exchanges is more effective than using stock returns. Sensitivity analysis shows instability in results with changes in the length of the training and testing windows. The highest annual return rate of 15.37% from 02.01.2008 to 01.12.2022 was characterized by an ARIMAX model with one commodity exogenous variable.

Suggested Citation

  • Paweł Jakubowski & Robert Ślepaczuk & Franciszek Windorbski, 2023. "REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market," Working Papers 2023-20, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2023-20
    as

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    References listed on IDEAS

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    1. Christopher K. Ma & Jeffrey M. Mercer & Matthew A. Walker, 1992. "Rolling over futures contracts: A note," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 12(2), pages 203-217, April.
    2. James M. Poterba & John B. Shoven, 2002. "Exchange-Traded Funds: A New Investment Option for Taxable Investors," American Economic Review, American Economic Association, vol. 92(2), pages 422-427, May.
    3. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    4. Chen, Haojun & Maher, Daniela, 2013. "On the predictive role of large futures trades for S&P500 index returns: An analysis of COT data as an informative trading signal," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 27(C), pages 177-201.
    5. Zhang, Yongjie & Chu, Gang & Shen, Dehua, 2021. "The role of investor attention in predicting stock prices: The long short-term memory networks perspective," Finance Research Letters, Elsevier, vol. 38(C).
    6. Latoszek Michał & Ślepaczuk Robert, 2020. "Does the inclusion of exposure to volatility into diversified portfolio improve the investment results? Portfolio construction from the perspective of a Polish investor," Economics and Business Review, Sciendo, vol. 6(1), pages 46-81, March.
    7. Bui, Quynh & Ślepaczuk, Robert, 2022. "Applying Hurst Exponent in pair trading strategies on Nasdaq 100 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    8. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    9. Beck Alexander & Kim Young Shin Aaron & Rachev Svetlozar & Feindt Michael & Fabozzi Frank, 2013. "Empirical analysis of ARMA-GARCH models in market risk estimation on high-frequency US data," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 167-177, April.
    10. Adcock, Robert & Gradojevic, Nikola, 2019. "Non-fundamental, non-parametric Bitcoin forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
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    More about this item

    Keywords

    ARIMA(X); GARCH; ARIMA(X)/GARCH; Algorithmic Investment Strategies; Granger Causality; Investment Performance Evaluation; Trading Systems; Forecasting Models;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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