On the Surprising Explanatory Power of Higher Realized Moments in Practice
Keren Shen, Jianfeng Yao, Wai Keung Li

TL;DR
This paper empirically examines the predictive power of higher realized moments, specifically skewness and kurtosis, for individual stock returns and variances on a daily scale, revealing that kurtosis forecasts future variance while skewness does not predict daily returns.
Contribution
It provides new empirical evidence on the forecasting abilities of realized skewness and kurtosis at the individual stock level on a daily basis, contrasting with prior market-wide studies.
Findings
Realized kurtosis significantly forecasts future stock variance.
Realized skewness lacks predictive power for short-term daily returns.
The study focuses on daily scale, individual stocks, contrasting with previous market-wide analyses.
Abstract
Realized moments of higher order computed from intraday returns are introduced in recent years. The literature indicates that realized skewness is an important factor in explaining future asset returns. However, the literature mainly focuses on the whole market and on the monthly or weekly scale. In this paper, we conduct an extensive empirical analysis to investigate the forecasting abilities of realized skewness and realized kurtosis towards individual stock's future return and variance in the daily scale. It is found that realized kurtosis possesses significant forecasting power for the stock's future variance. In the meanwhile, realized skewness is lack of explanatory power for the future daily return for individual stocks with a short horizon, in contrast with the existing literature.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Systems and Time Series Analysis · Financial Risk and Volatility Modeling · Stochastic processes and financial applications
