Index tracking strategy based on mixed-frequency financial data.
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- Record sourced from PubMed, PMID 33822827.
- Also identified by DOI 10.1371/journal.pone.0249665 and PMC identifier 8023492.
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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.