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Performance replication of the Spot Energy Index with optimal equity portfolio selection: Evidence from the UK, US and Brazilian markets

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  • Andriosopoulos, Kostas
  • Nomikos, Nikos

Abstract

This paper reproduces the performance of a geometric average Spot Energy Index by investing only in a subset of stocks from the Dow Jones Composite Average, the FTSE 100 and Bovespa Composite indexes, and in two pools that include only energy-sector stocks from the US and the UK respectively. Daily data are used and the index-tracking problem for passive investment is addressed with two evolutionary algorithms – the differential evolution algorithm and the genetic algorithm. The performance of the suggested investment strategy is tested under three different scenarios: buy-and-hold, quarterly and monthly rebalancing, accounting for transaction costs where necessary.

Suggested Citation

  • Andriosopoulos, Kostas & Nomikos, Nikos, 2014. "Performance replication of the Spot Energy Index with optimal equity portfolio selection: Evidence from the UK, US and Brazilian markets," European Journal of Operational Research, Elsevier, vol. 234(2), pages 571-582.
    • Handle: RePEc:eee:ejores:v:234:y:2014:i:2:p:571-582
    DOI: 10.1016/j.ejor.2013.09.006
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    2. Joëts, Marc, 2015. "Heterogeneous beliefs, regret, and uncertainty: The role of speculation in energy price dynamics," European Journal of Operational Research, Elsevier, vol. 247(1), pages 204-215.
    3. Xianhua Peng & Chenyin Gong & Xue Dong He, 2023. "Reinforcement Learning for Financial Index Tracking," Papers 2308.02820, arXiv.org, revised Nov 2024.
    4. Strub, O. & Baumann, P., 2018. "Optimal construction and rebalancing of index-tracking portfolios," European Journal of Operational Research, Elsevier, vol. 264(1), pages 370-387.
    5. Nguyen, Quynh Nga & Aboura, Sofiane & Chevallier, Julien & Zhang, Lyuyuan & Zhu, Bangzhu, 2020. "Local Gaussian correlations in financial and commodity markets," European Journal of Operational Research, Elsevier, vol. 285(1), pages 306-323.
    6. Mellios, Constantin & Six, Pierre & Lai, Anh Ngoc, 2016. "Dynamic speculation and hedging in commodity futures markets with a stochastic convenience yield," European Journal of Operational Research, Elsevier, vol. 250(2), pages 493-504.
    7. Zhang, Yue-Jun & Chen, Ming-Ying, 2018. "Evaluating the dynamic performance of energy portfolios: Empirical evidence from the DEA directional distance function," European Journal of Operational Research, Elsevier, vol. 269(1), pages 64-78.
    8. Spiridon Penev & Pavel Shevchenko & Wei Wu, 2019. "Myopic robust index tracking with Bregman divergence," Papers 1908.07659, arXiv.org, revised Jul 2021.
    9. Gnägi, M. & Strub, O., 2020. "Tracking and outperforming large stock-market indices," Omega, Elsevier, vol. 90(C).
    10. Jebabli, Ikram & Roubaud, David, 2018. "Time-varying efficiency in food and energy markets: Evidence and implications," Economic Modelling, Elsevier, vol. 70(C), pages 97-114.
    11. Zhang, Yue-Jun & Lin, Jia-Juan, 2019. "Can the VAR model outperform MRS model for asset allocation in commodity market under different risk preferences of investors?," International Review of Financial Analysis, Elsevier, vol. 66(C).
    12. Sant’Anna, Leonardo Riegel & Caldeira, João Frois & Filomena, Tiago Pascoal, 2020. "Lasso-based index tracking and statistical arbitrage long-short strategies," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    13. Julio Cezar Soares Silva & Adiel Teixeira de Almeida Filho, 2023. "A systematic literature review on solution approaches for the index tracking problem in the last decade," Papers 2306.01660, arXiv.org, revised Jun 2023.

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