The Low-Risk Anomaly Revisited on High-Frequency Data

Kris Boudt*, Giang Nguyen, Benedict Peeters

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Abstract

Under the capital asset pricing model assumptions, the market capitalization-weighted portfolio is mean-variance efficient. In real-world applications, it has been shown by various authors that low-risk portfolios outperform the market capitalization-weighted portfolio. We revisit this anomaly using high-frequency data to construct low-risk portfolios for the S&P 500 constituents over the period 2007-2012. The portfolios that we consider are invested in the 100 lowest risk stocks and apply equal weighting, market capitalization weighting, or inverse risk weighting. We find that the low-risk anomaly is also present when using high-frequency data, and for downside risk measures such as semivariance and Cornish-Fisher value at risk. For the portfolios considered, there does not seem to be any statistically or economically significant gain of using high-frequency data.

Original languageEnglish
Title of host publicationHandbook of High Frequency Trading
PublisherElsevier Inc
Pages397-424
Number of pages28
ISBN (Electronic)9780128023624
ISBN (Print)9780128022054
DOIs
Publication statusPublished - 4 Feb 2015
Externally publishedYes

Keywords

  • Downside risk
  • High-frequency data
  • Low-risk investing
  • Portfolio allocation
  • Realized volatility

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