IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v66y2024ics1544612324007025.html
   My bibliography  Save this article

Incorporating weather information into commodity portfolio optimization

Author

Listed:
  • Zhang, Dongna
  • Dai, Xingyu
  • Xue, Jianhao

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.

Suggested Citation

  • Zhang, Dongna & Dai, Xingyu & Xue, Jianhao, 2024. "Incorporating weather information into commodity portfolio optimization," Finance Research Letters, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:finlet:v:66:y:2024:i:c:s1544612324007025
    DOI: 10.1016/j.frl.2024.105672
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612324007025
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2024.105672?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2014. "Specification Sensitivity in Right-Tailed Unit Root Testing for Explosive Behaviour," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(3), pages 315-333, June.
    2. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    3. Yu, Deshui & Chen, Li & Li, Luyang, 2023. "Nonparametric modeling for the time-varying persistence of inflation," Economics Letters, Elsevier, vol. 225(C).
    4. Lwin, Khin T. & Qu, Rong & MacCarthy, Bart L., 2017. "Mean-VaR portfolio optimization: A nonparametric approach," European Journal of Operational Research, Elsevier, vol. 260(2), pages 751-766.
    5. Zhao, Yi & Dai, Xingyu & Zhang, Dongna & Wang, Qunwei & Cao, Yaru, 2023. "Do weather conditions drive China's carbon-coal-electricity markets systemic risk? A multi-timescale analysis," Finance Research Letters, Elsevier, vol. 51(C).
    6. Jasmien De Winne & Gert Peersman, 2021. "The adverse consequences of global harvest and weather disruptions on economic activity," Nature Climate Change, Nature, vol. 11(8), pages 665-672, August.
    7. Lu Wang & Ferhana Ahmad & Gong-li Luo & Muhammad Umar & Dervis Kirikkaleli, 2022. "Portfolio optimization of financial commodities with energy futures," Annals of Operations Research, Springer, vol. 313(1), pages 401-439, June.
    8. Shuping Shi & Peter C. B. Phillips & Stan Hurn, 2018. "Change Detection and the Causal Impact of the Yield Curve," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(6), pages 966-987, November.
    9. Brauneis, Alexander & Mestel, Roland, 2019. "Cryptocurrency-portfolios in a mean-variance framework," Finance Research Letters, Elsevier, vol. 28(C), pages 259-264.
    10. Namwon Hyung & Casper G. de Vries, 2005. "Portfolio Diversification Effects of Downside Risk," Tinbergen Institute Discussion Papers 05-008/2, Tinbergen Institute.
    11. Wu, Dan & Dai, Xingyu & Zhao, Ruikun & Cao, Yaru & Wang, Qunwei, 2023. "Pass-through from temperature intervals to China's commodity futures’ interval-valued returns: Evidence from the varying-coefficient ITS model," Finance Research Letters, Elsevier, vol. 58(PA).
    12. Peter C. B. Phillips & Shu-Ping Shi & Jun Yu, 2011. "Specification Sensitivity in Right-Tailed Unit Root Testing for Explosive Behavior," Working Papers 15-2011, Singapore Management University, School of Economics.
    13. Pesaran, M. Hashem & Pick, Andreas, 2011. "Forecast Combination Across Estimation Windows," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 307-318.
    14. Xingyu Dai & Dongna Zhang & Chi Keung Marco Lau & Qunwei Wang, 2023. "Multiobjective portfolio optimization: Forecasting and evaluation under investment horizon heterogeneity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2167-2196, December.
    15. Namwon Hyung, 2005. "Portfolio Diversification Effects of Downside Risk," Journal of Financial Econometrics, Oxford University Press, vol. 3(1), pages 107-125.
    16. Zhang, Yue-Jun & Chen, Ming-Ying, 2018. "Evaluating the dynamic performance of energy portfolios: Empirical evidence from the DEA directional distance function," European Journal of Operational Research, Elsevier, vol. 269(1), pages 64-78.
    17. Zhang, Dongna & Dai, Xingyu & Wang, Qunwei & Lau, Chi Keung Marco, 2023. "Impacts of weather conditions on the US commodity markets systemic interdependence across multi-timescales," Energy Economics, Elsevier, vol. 123(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Dan & Dai, Xingyu & Zhao, Ruikun & Cao, Yaru & Wang, Qunwei, 2023. "Pass-through from temperature intervals to China's commodity futures’ interval-valued returns: Evidence from the varying-coefficient ITS model," Finance Research Letters, Elsevier, vol. 58(PA).
    2. Lennart Ante & Aman Saggu, 2025. "Time-Varying Bidirectional Causal Relationships Between Transaction Fees and Economic Activity of Subsystems Utilizing the Ethereum Blockchain Network," Papers 2501.05299, arXiv.org.
    3. Christie Smith & Aaron Kumar, 2018. "Crypto‐Currencies – An Introduction To Not‐So‐Funny Moneys," Journal of Economic Surveys, Wiley Blackwell, vol. 32(5), pages 1531-1559, December.
    4. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    5. KIRKPINAR, Aysegul & ERER, Elif & ERER, Deniz, 2019. "Is There A Rational Bubble In Bist 100 And Sector Indices?," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 23(3), pages 21-33, September.
    6. Lui, Yiu Lim & Phillips, Peter C.B. & Yu, Jun, 2024. "Robust testing for explosive behavior with strongly dependent errors," Journal of Econometrics, Elsevier, vol. 238(2).
    7. Peter C. B. Phillips & Shuping Shi, 2019. "Detecting Financial Collapse and Ballooning Sovereign Risk," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(6), pages 1336-1361, December.
    8. Tee, Kai-Hong, 2009. "The effect of downside risk reduction on UK equity portfolios included with Managed Futures Funds," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 303-310, December.
    9. Verena Monschang & Bernd Wilfling, 2021. "Sup-ADF-style bubble-detection methods under test," Empirical Economics, Springer, vol. 61(1), pages 145-172, July.
    10. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(4), pages 1043-1078, November.
    11. Xi-Xi Zhang & Lu Liu & Chi-Wei Su & Ran Tao & Oana-Ramona Lobonţ & Nicoleta-Claudia Moldovan, 2019. "Bubbles in Agricultural Commodity Markets of China," Complexity, Hindawi, vol. 2019, pages 1-7, December.
    12. Funke, Michael & Tsang, Andrew & Zhu, Linxu, 2018. "Not all cities are alike: House price heterogeneity and the design of macro-prudential policies in China," BOFIT Discussion Papers 18/2018, Bank of Finland Institute for Emerging Economies (BOFIT).
    13. Su-Ling TSAI & Hsien-Hung KUNG & Kai-yin Allison HAGA, 2015. "Testing for Multiple Bubbles in the 35 Large and Medium Cities of Real Estate Price in China," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(605), W), pages 275-290, Winter.
    14. Moore, Kyle & Sun, Pengfei & de Vries, Casper G. & Zhou, Chen, 2013. "The cross-section of tail risks in stock returns," MPRA Paper 45592, University Library of Munich, Germany.
    15. Zhao, Yanping & Chang, Hsu-Ling & Su, Chi-Wei & Nian, Rui, 2015. "Gold bubbles: When are they most likely to occur?," Japan and the World Economy, Elsevier, vol. 34, pages 17-23.
    16. DiTraglia, Francis J. & Gerlach, Jeffrey R., 2013. "Portfolio selection: An extreme value approach," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 305-323.
    17. Gomis-Porqueras, Pedro & Shi, Shuping & Tan, David, 2022. "Gold as a financial instrument," Journal of Commodity Markets, Elsevier, vol. 27(C).
    18. Adam Goliński & Peter Spencer, 2021. "Modeling the Covid‐19 epidemic using time series econometrics," Health Economics, John Wiley & Sons, Ltd., vol. 30(11), pages 2808-2828, November.
    19. Peter C.B. Phillips & Ye Chen, "undated". "Restricted Likelihood Ratio Tests in Predictive Regression," Cowles Foundation Discussion Papers 1968, Cowles Foundation for Research in Economics, Yale University.
    20. Peter C. B. Phillips & Shuping Shi & Jun Yu, 2015. "Testing For Multiple Bubbles: Limit Theory Of Real‐Time Detectors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56(4), pages 1079-1134, November.

    More about this item

    Keywords

    Weather information; Energy commodity; Agricultural commodity; Portfolio optimization;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:66:y:2024:i:c:s1544612324007025. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.