Improving Time-Series Momentum Strategies: The Role of Trading Signals and Volatility Estimators
UBS AG; Imperial College Business School
Imperial College Business School; University of Oxford, Oxford-Man Institute of Quantitative Finance
August 30, 2012
Constructing a time-series momentum strategy involves the volatility-adjusted aggregation of uni- variate strategies and therefore relies heavily on the efficiency of the volatility estimator and on the quality of the momentum trading signal. Using a dataset with intra-day quotes of 12 futures contracts from November 1999 to October 2009, we investigate these dependencies and their relation to time-series momentum profitability and reach a number of novel findings. Momentum trading signals generated by fitting a linear trend on the asset price path maximise the out-of-sample performance while minimizing the portfolio turnover, hence dominating the ordinary momentum trading signal in literature, the sign of past return. Regarding the volatility-adjusted aggregation of univariate strategies, the Yang-Zhang range estimator constitutes the optimal choice for volatility estimation in terms of maximizing efficiency and minimizing the bias and the ex-post portfolio turnover.
Number of Pages in PDF File: 48
Keywords: Trend-following, Momentum, Managed Futures, Volatility Estimation, Trading Signal, Transaction Costs
JEL Classification: D23, E3, G14working papers series
Date posted: September 2, 2012
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