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Can Mixed-Frequency Data Improve the Higher-Order Moments Portfolio Performance?

Author

Listed:
  • Shuang Zhao
  • Wanbo Lu
  • Muhammad Wajid Raza
  • Dong Yang

Abstract

In the presence of non-normally distributed asset returns, an optimal portfolio selection should consider higher-order (co-)moments when no sampling errors exist. However, the curse of dimensionality has already been a serious concern in mean-variance analyses; higher-order (co-)moments also face the same dilemma. This study uses mixed-frequency (MF) data under the assumption that stock returns are generated by a mixed-data sampling (MIDAS) regression model, which shows to provide well-improved estimates for the covariance, co-skewness, and co-kurtosis matrices for higher dimension. We discover that the new, improved estimates used in higher-order (co-)moment portfolio selections can dominate the other existing structured estimates from an out-of-sample perspective in most instances.

Suggested Citation

  • Shuang Zhao & Wanbo Lu & Muhammad Wajid Raza & Dong Yang, 2021. "Can Mixed-Frequency Data Improve the Higher-Order Moments Portfolio Performance?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(15), pages 4473-4493, December.
    • Handle: RePEc:mes:emfitr:v:57:y:2021:i:15:p:4473-4493
    DOI: 10.1080/1540496X.2020.1785862
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