Tactical Allocation in Commodity Futures Markets: Combining Momentum and Term Structure Signals
By Ana‐Maria Fuertes, Joëlle Miffre, G. Rallis. 2010
208 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/2c295d9b1219451bfa48ffd9f6019f6b4f0a07b4
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Citations in the corpus
- Should Investors Include Commodities in Their Portfolios After All? New Evidence (2011, unknown): “…returns can be predicted by a number of variables (see e.g., Hong and Yogo, 2010, and the references therein), and profitable momentum and term structure strategies can be constructed (Erb and Harvey, 2006, Gorton et al., 2007, Miffre and Rallis, 2007, Basu and Miffre, 2010, Fuertes et al., 2010).”
- Conditional Correlation and Volatility in CommodityFutures and Traditional Asset Markets (2009, background): “By far the most successful volatility forecasting model is the generalized autoregressive conditional heteroscedasticity model, GARCH(1,1) (Hansen and Lunde [2005]), developed by Bollerslev [1986].”
- Demystifying Time‐Series Momentum Strategies: Volatility Estimators, Trading Rules and Pairwise Correlations (2020, unknown): “A multitude of range estimators have been suggested in the literature by Parkinson (1980), Garman and Klass (1980), Rogers and Satchell (1991), and Yang and Zhang (2000), which have been empirically shown (see for example Brandt and Kinlay 2005, Shu and Zhang 2006) to reduce the estimation error of ”
- Capturing the Risk Premium of Commodity Futures: The Role of Hedging Pressure (2013, methodology): “The double-sort strategy that combines the momentum and term structure signals follows from Fuertes et al. (2010). The available cross section is first split into winner and loser portfolios based on mean returns over the previous R weeks using the 50% breakpoint.”