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. 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.
    4. Yu, Deshui & Chen, Li & Li, Luyang, 2023. "Nonparametric modeling for the time-varying persistence of inflation," Economics Letters, Elsevier, vol. 225(C).
    5. 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.
    6. Brauneis, Alexander & Mestel, Roland, 2019. "Cryptocurrency-portfolios in a mean-variance framework," Finance Research Letters, Elsevier, vol. 28(C), pages 259-264.
    7. 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).
    8. Namwon Hyung & Casper G. de Vries, 2005. "Portfolio Diversification Effects of Downside Risk," Tinbergen Institute Discussion Papers 05-008/2, Tinbergen Institute.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    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. Yang Hu, 2023. "A review of Phillips‐type right‐tailed unit root bubble detection tests," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 141-158, February.
    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. Yongheng Deng & Eric Girardin & Roselyne Joyeux & Shuping Shi, 2017. "Did bubbles migrate from the stock to the housing market in China between 2005 and 2010?," Pacific Economic Review, Wiley Blackwell, vol. 22(3), pages 276-292, August.
    6. 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.
    7. Tavakoli Baghdadabad, Mohammad Reza, 2014. "Average drawdown risk reduction and risk tolerances," Research in Economics, Elsevier, vol. 68(3), pages 264-276.
    8. Janusz Sobieraj & Dominik Metelski, 2021. "Testing Housing Markets for Episodes of Exuberance: Evidence from Different Polish Cities," JRFM, MDPI, vol. 14(9), pages 1-29, September.
    9. Phillips, Peter C.B. & Lee, Ji Hyung, 2016. "Robust econometric inference with mixed integrated and mildly explosive regressors," Journal of Econometrics, Elsevier, vol. 192(2), pages 433-450.
    10. 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.
    11. 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).
    12. 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.
    13. Jesús Otero & Theodore Panagiotidis & Georgios Papapanagiotou, 2021. "Testing for exuberance in house prices using data sampled at different frequencies," Working Paper series 21-13, Rimini Centre for Economic Analysis.
    14. 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.
    15. Verena Monschang & Bernd Wilfling, 2021. "Sup-ADF-style bubble-detection methods under test," Empirical Economics, Springer, vol. 61(1), pages 145-172, July.
    16. 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.
    17. 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.
    18. Antonio Di Cesare & Philip A. Stork & Casper G. de Vries, 2015. "Risk Measures for Autocorrelated Hedge Fund Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(4), pages 868-895.
    19. Caravello, Tomas E. & Psaradakis, Zacharias & Sola, Martin, 2023. "Rational bubbles: Too many to be true?," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    20. Smith, L. Vanessa & Tarui, Nori & Yamagata, Takashi, 2021. "Assessing the impact of COVID-19 on global fossil fuel consumption and CO2 emissions," Energy Economics, Elsevier, vol. 97(C).

    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.