Using High-Frequency Upside Volatility Share as a Stock Factor

Summary

This research summary tests whether splitting stock volatility into components yields useful selection signals from high-frequency returns. The reported system-versus-idiosyncratic volatility split performs poorly at the one-minute sampling interval; its results improve as the data interval grows. By contrast, the study reports that upside volatility and, especially, upside volatility as a share of total volatility help distinguish subsequent stock returns. Higher prior upside volatility is associated with weaker returns over the following month.

The summary reports negative information coefficients and long-short returns for the one-minute upside-share factor, including after orthogonalization, and says adding it improves a multi-factor model’s reported metrics. It also notes that favorable cross-sectional results in 2017 did not translate into positive returns for a standalone factor portfolio. These findings are summaries of a 2017 report, not evidence of current performance; the underlying study’s full methods, costs, universe construction, and robustness checks are not included in the supplied text.

Key ideas

• The study compares high-frequency volatility decompositions as stock-selection factors.
• The system and idiosyncratic volatility split has weak results at one-minute frequency, with reported improvement at wider intervals.
• A higher share of upside volatility is associated with lower subsequent one-month stock returns in the study.
• The upside-share factor retains reported selection power after orthogonalization and improves the stated multi-factor model metrics.
• The summary cautions that favorable cross-sectional results did not produce positive standalone portfolio returns in 2017.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.