Integrating Time Series and Cross-Sectional Signals for Optimal Commodity Portfolios

45 Pages Posted: 1 Jan 2020 Last revised: 26 Oct 2020

See all articles by Regina Hammerschmid

Regina Hammerschmid

University of Zurich; Swiss Finance Institute

Harald Lohre

Robeco Quantitative Investments; Lancaster University Management School

Date Written: October 24, 2020


We study the optimal combination of different commodity signals in a dynamic portfolio theoretic framework. Following Brandt et al. (2006, 2009) we parameterize the portfolio weights of a risk-averse mean-variance investor to integrate information from time series predictors and cross-sectional characteristics of commodity assets. We show that the long end of the futures curve as well as open interest have significant timing power, whereas the short end of the futures curve and past returns are relevant characteristics for tilting commodities. Combining timing and tilting strategies outperforms all factor benchmarks out-of-sample and after transaction costs. Moreover, the optimal commodity allocation is not priced by common risk factors, such as commodity carry or momentum.

Keywords: optimal commodity strategies, parametric portfolio policies, timing and tilting

JEL Classification: G11, G12

Suggested Citation

Hammerschmid, Regina and Lohre, Harald, Integrating Time Series and Cross-Sectional Signals for Optimal Commodity Portfolios (October 24, 2020). Available at SSRN: or

Regina Hammerschmid (Contact Author)

University of Zurich ( email )

Schönberggasse 1
Zürich, 8001

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4

Harald Lohre

Robeco Quantitative Investments ( email )

Weena 850
Rotterdam, 3011 AG

Lancaster University Management School

Lancaster LA1 4YX
United Kingdom


Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics