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Index tracking strategy based on mixed-frequency financial data

Author

Listed:
  • Xiangyu Cui
  • Xuan Zhang

Abstract

To obtain market average return, investment managers need to construct index tracking portfolio to replicate target index. Currently, most literatures use financial data that has homogenous frequency when constructing the index tracking portfolio. To make up for this limitation, we propose a methodology based on mixed-frequency financial data, called FACTOR-MIDAS-POET model. The proposed model can utilize the intraday return data, daily risk factors data and monthly or quarterly macro economy data, simultaneously. Meanwhile, the out-of-sample analysis demonstrates that our model can improve the tracking accuracy.

Suggested Citation

  • Xiangyu Cui & Xuan Zhang, 2021. "Index tracking strategy based on mixed-frequency financial data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-15, April.
    • Handle: RePEc:plo:pone00:0249665
    DOI: 10.1371/journal.pone.0249665
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    References listed on IDEAS

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