High Frequency Realized Skewness and Downside Volatility for Sector Rotation

Summary

This Chinese research summary examines whether intraday data can support sector rotation signals, focusing on realized skewness and the share of volatility attributable to downside moves. It describes constructing industry level factors inspired by high frequency stock selection research, then testing their monthly rotation ability across primary and secondary industry indices. The stated results show negative information coefficients for realized skewness and positive coefficients for downside volatility share in primary industries, with similar claimed signal ability in secondary industries.

The summary reports parameter sensitivity comparisons across one, two, five, and ten minute data intervals, and across holding or rebalance intervals ranging from weekly to two months. It says higher frequency inputs performed better and that roughly monthly rebalancing was stronger. It also says selected long industries were shown from 2017 onward, but those selections and the underlying report are not provided here. The evidence is limited to a summary: it omits detailed formulas, sample construction, transaction costs, robustness checks, and full backtest results, so the findings should not be treated as proof of live performance.

Key ideas

• The study tests realized skewness and downside volatility share as industry rotation factors.
• Realized skewness is reported to have negative information coefficients for primary industry indices.
• Downside volatility share is reported to have positive information coefficients for primary industry indices.
• The summary says higher frequency intraday inputs improved the factors' rotation ability.
• It reports stronger performance with a rebalance interval of about one month, but omits full test details.

Tags

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