Skewness and Term Structure in Momentum Trading Strategies

Summary

The document analyzes the third-moment, or skewness, properties of linear and nonlinear momentum trading strategies. It treats nonlinear strategies as transformations of a normalized moving-average filter, specifically an exponential moving average (EMA). The focus is on explaining why these strategies generally have positive skewness and why that skewness varies across the term structure.

The paper synthesizes two earlier works by the author, with updates and commentary. The excerpt identifies the strategy family and the statistical property under study, but does not provide formulas, empirical data, parameter choices, or evidence comparing strategies. It therefore serves as an outline of the analysis rather than a complete basis for estimating skewness or selecting a momentum strategy.

Key ideas

• The analysis characterizes skewness in both linear and nonlinear momentum strategies.
• Nonlinear strategies are modeled as transformations of a normalized EMA filter.
• The document explains why momentum strategies generally exhibit positive skewness.
• Skewness varies across the strategy term structure, though the excerpt provides no numerical estimates.

Tags

# Design and analysis of momentum trading strategies


# Design and analysis of momentum trading strategies









We give a complete description of the third-moment (skewness) characteristics of both linear and nonlinear momentum trading strategies, the latter being understood as transformations of a normalised moving-average filter (EMA). We explain in detail why the skewness is generally positive and has a term structure. This paper is a synthesis of two papers published by the author in RISK in 2012, with some updates and comments.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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