Incorporating weather information into commodity portfolio optimization

Dongna Zhang, Xingyu Dai and Jianhao Xue

Finance Research Letters, 2024, vol. 66, issue C

Abstract: This study investigates the out-of-sample performance of commodity portfolios by incorporating weather information within the Black-Litterman framework. The inclusion of weather information increases returns, reduces downside risk for energy and agricultural portfolios, and diminishes volatility in agricultural portfolios. We find significant enhancement in the efficiency of energy and agricultural portfolios with weather information. Notably, portfolios integrating low-temperature weather information outperform their counterparts across most performance measures. Our findings underscore the benefits of incorporating weather information in the optimization of commodity portfolios.

Keywords: Weather information; Energy commodity; Agricultural commodity; Portfolio optimization (search for similar items in EconPapers)
JEL-codes: G11 Q02 Q54 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612324007025
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:66:y:2024:i:c:s1544612324007025

DOI: 10.1016/j.frl.2024.105672

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().