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

Shuang Zhao, Wanbo Lu, Muhammad Wajid Raza and Dong Yang

Emerging Markets Finance and Trade, 2021, vol. 57, issue 15, 4473-4493

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.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://hdl.handle.net/10.1080/1540496X.2020.1785862 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mes:emfitr:v:57:y:2021:i:15:p:4473-4493

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/MREE20

DOI: 10.1080/1540496X.2020.1785862

Access Statistics for this article

More articles in Emerging Markets Finance and Trade from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().