IDEAS home Printed from https://ideas.repec.org/p/mil/wpdepa/2014-03.html
   My bibliography  Save this paper

Water, Food, Energy: Searching for the Economic Nexus

Author

Listed:
  • Massimo PERI
  • Daniela VANDONE
  • Lucia BALDI

Abstract

Water, food and energy (WFE) are strongly interconnected: each depends on the other for a lot of concerns, spanning from guaranteeing access to services, to environmental, social and ethical impact issues, to price relations. Using daily data spanning from November 2001 to May 2013 we empirically analyze the volatility spillovers and the dynamic conditional correlation between the WFE prices using a multivariate GARCH method. We then apply a multifactor market model based on the theory of Capital Asset Pricing Model (CAPM) with the aim to analyze the impact of agriculture and energy price trends on the share price value of exchange-listed companies that derive a substantial portion of their revenues from the potable and wastewater industry. Results highlight the existences of a financial nexus between WFE that is particular exacerbate during finance turbulence. Understanding price dynamics is relevant both to water, agriculture and energy policy makers and to investors, since it influences information dissemination, price discovery, efficient allocation of resources, hedging and portfolio optimization.

Suggested Citation

  • Massimo PERI & Daniela VANDONE & Lucia BALDI, 2014. "Water, Food, Energy: Searching for the Economic Nexus," Departmental Working Papers 2014-03, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  • Handle: RePEc:mil:wpdepa:2014-03
    as

    Download full text from publisher

    File URL: http://wp.demm.unimi.it/files/wp/2014/DEMM-2014_03wp.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(2), pages 280-310, April.
    2. Silvennoinen, Annastiina & Teräsvirta, Timo, 2007. "Multivariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 669, Stockholm School of Economics, revised 18 Jan 2008.
    3. Gulnur Muradog Lu & Kivilcim Metin & Reha Argac, 2001. "Is there a long run relationship between stock returns and monetary variables: evidence from an emerging market," Applied Financial Economics, Taylor & Francis Journals, vol. 11(6), pages 641-649.
    4. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    5. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(4), pages 537-572.
    6. Bazilian, Morgan & Rogner, Holger & Howells, Mark & Hermann, Sebastian & Arent, Douglas & Gielen, Dolf & Steduto, Pasquale & Mueller, Alexander & Komor, Paul & Tol, Richard S.J. & Yumkella, Kandeh K., 2011. "Considering the energy, water and food nexus: Towards an integrated modelling approach," Energy Policy, Elsevier, vol. 39(12), pages 7896-7906.
    7. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    8. Chen, Shiu-Sheng, 2009. "Predicting the bear stock market: Macroeconomic variables as leading indicators," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 211-223, February.
    9. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    10. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    11. Fama, Eugene F & French, Kenneth R, 1996. "Multifactor Explanations of Asset Pricing Anomalies," Journal of Finance, American Finance Association, vol. 51(1), pages 55-84, March.
    12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    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. Peri, Massimo & Vandone, Daniela & Baldi, Lucia, 2014. "Worldwide Evidences in the Relationships between Agriculture, Energy and Water Sectors," 2014 International European Forum, February 17-21, 2014, Innsbruck-Igls, Austria 199346, International European Forum on System Dynamics and Innovation in Food Networks.
    2. Silvennoinen, Annastiina & Teräsvirta, Timo, 2007. "Multivariate GARCH models," SSE/EFI Working Paper Series in Economics and Finance 669, Stockholm School of Economics, revised 18 Jan 2008.
    3. Dahiru A. Balaa & Taro Takimotob, 2017. "Stock markets volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 17(1), pages 25-48, March.
    4. Annastiina Silvennoinen & Timo Ter�svirta, 2015. "Modeling Conditional Correlations of Asset Returns: A Smooth Transition Approach," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 174-197, February.
    5. R. Khalfaoui & M. Boutahar, 2012. "Portfolio Risk Evaluation: An Approach Based on Dynamic Conditional Correlations Models and Wavelet Multi-Resolution Analysis," Working Papers halshs-00793068, HAL.
    6. Peri, M. & Vandone, D. & Baldi, L., 2015. "Volatility Spillover between Water, Food and Energy," 2015 Conference, August 9-14, 2015, Milan, Italy 212627, International Association of Agricultural Economists.
    7. de Almeida, Daniel & Hotta, Luiz K. & Ruiz, Esther, 2018. "MGARCH models: Trade-off between feasibility and flexibility," International Journal of Forecasting, Elsevier, vol. 34(1), pages 45-63.
    8. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," LIDAM Discussion Papers CORE 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
      • Bauwens, L. & Hafner, C. & Laurent, S., 2012. "Volatility Models," LIDAM Reprints ISBA 2012028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
      • Bauwens, L. & Hafner C. & Laurent, S., 2011. "Volatility Models," LIDAM Discussion Papers ISBA 2011044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. M. Hashem Pesaran & Paolo Zaffaroni, 2004. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management," CESifo Working Paper Series 1358, CESifo.
    10. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    11. Lucia BALDI & Massimo PERI & Daniela VANDONE, 2013. "Clean Energy Industries and Rare Earth Materials: Economic and Financial Issues," Departmental Working Papers 2013-07, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    12. Paul Catani & Timo Teräsvirta & Meiqun Yin, 2017. "A Lagrange multiplier test for testing the adequacy of constant conditional correlation GARCH model," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 599-621, October.
    13. Aboura, Sofiane & Chevallier, Julien, 2015. "Volatility returns with vengeance: Financial markets vs. commodities," Research in International Business and Finance, Elsevier, vol. 33(C), pages 334-354.
    14. Audrone Virbickaite & M. Concepción Ausín & Pedro Galeano, 2015. "Bayesian Inference Methods For Univariate And Multivariate Garch Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 76-96, February.
    15. Caldeira, João F & Moura, Guilherme Valle & Santos, André Alves Portela, 2013. "Seleção de carteiras utilizando o modelo Fama-French-Carhart," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 67(1), April.
    16. Carlo Drago & Andrea Scozzari, 2022. "Evaluating conditional covariance estimates via a new targeting approach and a networks-based analysis," Papers 2202.02197, arXiv.org.
    17. Baldi, Lucia & Peri, Massimo & Vandone, Daniela, 2014. "Clean energy industries and rare earth materials: Economic and financial issues," Energy Policy, Elsevier, vol. 66(C), pages 53-61.
    18. Mensi, Walid & Nekhili, Ramzi & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Oil and precious metals: Volatility transmission, hedging, and safe haven analysis from the Asian crisis to the COVID-19 crisis," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 73-96.
    19. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    20. repec:dau:papers:123456789/13359 is not listed on IDEAS
    21. Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2011. "Crude oil hedging strategies using dynamic multivariate GARCH," Energy Economics, Elsevier, vol. 33(5), pages 912-923, September.

    More about this item

    Keywords

    WFE nexus; volatility spillover; Multifactor Market Model; stock prices;
    All these keywords.

    JEL classification:

    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water
    • Q21 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Demand and Supply; Prices
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • E39 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Other

    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:mil:wpdepa:2014-03. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/damilit.html .

    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: DEMM Working Papers The email address of this maintainer does not seem to be valid anymore. Please ask DEMM Working Papers to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/damilit.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.