Demystifying Time‐Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations

By N. Baltas, Robert Kosowski. 2020

46 citations recorded by the source corpus.

Motivated by the recent asset pricing literature that examines the effect of frictions on asset prices, we examine the effect of volatility estimation error, trading rule and pairwise correlations on turnover and performance of time-series momentum strategies from 1974 until 2013. Volatility estimators with desirable theoretical properties, such as range-based estimators, improve the performance of the strategies after transaction costs. Price trend-based momentum trading rules lead to the highest out-of-sample performance, because they reduce portfolio turnover significantly. A weighting scheme that incorporates pairwise correlations sheds light on recent performance drivers and improves performance during the post 2008 financial crisis period.

Subfields: Economics and Econometrics, Finance

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1. Bryan Lim
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1. Bryan Lim
2. Stephen J. Roberts
3. Stefan Zohren

Citations in the corpus

• Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019, methodology): “Although the intuition underpinning the strategy is clear, specific implementation details can vary widely between signals with a plethora of methods available to estimate the magnitude of price trends [5, 6, 4] and map them to actual traded positions [7, 8, 9].”