IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v94y2021icp981-994.html
   My bibliography  Save this article

Macroeconomic forecasts and commodity futures volatility

Author

Listed:
  • Ye, Wuyi
  • Guo, Ranran
  • Deschamps, Bruno
  • Jiang, Ying
  • Liu, Xiaoquan

Abstract

We examine the impact of macroeconomic expectations on the volatility of Chinese commodity futures. As commodity futures are forward-looking, we expect them to be influenced by market expectations of the future economic situation, which we capture using a data set of professional macroeconomic forecasts. We analyze 15 commodity futures contracts using a GARCH-MIDAS model that contains daily price volatility and monthly macroeconomic forecasts. We find that the volatility of commodity futures is impacted more strongly by macroeconomic forecasts than by concurrent economic conditions. Furthermore, augmenting the volatility model with the macroeconomic forecasts improves the model ability to predict future volatility. These volatility predictions also offer economic gains to a mean-variance utility investor in a portfolio setting. Finally, the impact of macroeconomic forecasts is dependent on the state of the economy.

Suggested Citation

  • Ye, Wuyi & Guo, Ranran & Deschamps, Bruno & Jiang, Ying & Liu, Xiaoquan, 2021. "Macroeconomic forecasts and commodity futures volatility," Economic Modelling, Elsevier, vol. 94(C), pages 981-994.
  • Handle: RePEc:eee:ecmode:v:94:y:2021:i:c:p:981-994
    DOI: 10.1016/j.econmod.2020.02.038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999319312088
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econmod.2020.02.038?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. Jennifer N. Carpenter & Fangzhou Lu & Robert F. Whitelaw, 2015. "The Real Value of China's Stock Market," NBER Working Papers 20957, National Bureau of Economic Research, Inc.
    2. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    3. Smales, L.A., 2017. "Commodity market volatility in the presence of U.S. and Chinese macroeconomic news," Journal of Commodity Markets, Elsevier, vol. 7(C), pages 15-27.
    4. Browne, Frank & Cronin, David, 2010. "Commodity prices, money and inflation," Journal of Economics and Business, Elsevier, vol. 62(4), pages 331-345, July.
    5. Leeper, Eric M. & Zha, Tao, 2003. "Modest policy interventions," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1673-1700, November.
    6. Hammoudeh, Shawkat & Nguyen, Duc Khuong & Sousa, Ricardo M., 2015. "US monetary policy and sectoral commodity prices," Journal of International Money and Finance, Elsevier, vol. 57(C), pages 61-85.
    7. Karali, Berna & Ramirez, Octavio A., 2014. "Macro determinants of volatility and volatility spillover in energy markets," Energy Economics, Elsevier, vol. 46(C), pages 413-421.
    8. Libing Fang & Baizhu Chen & Honghai Yu & Yichuo Qian, 2018. "The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH‐MIDAS approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 413-422, March.
    9. Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
    10. Jonas Dovern & Ulrich Fritsche & Jiri Slacalek, 2012. "Disagreement Among Forecasters in G7 Countries," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1081-1096, November.
    11. Tobias Adrian & Joshua Rosenberg, 2008. "Stock Returns and Volatility: Pricing the Short‐Run and Long‐Run Components of Market Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2997-3030, December.
    12. Jeffrey Frankel & Ben Smit & Federico Sturzenegger, 2008. "Fiscal and monetary policy in a commodity‐based economy1," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 16(4), pages 679-713, October.
    13. Elder, John & Miao, Hong & Ramchander, Sanjay, 2012. "Impact of macroeconomic news on metal futures," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 51-65.
    14. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    15. Ji, Qiang & Fan, Ying, 2016. "How do China's oil markets affect other commodity markets both domestically and internationally?," Finance Research Letters, Elsevier, vol. 19(C), pages 247-254.
    16. Margaret E. Slade & Henry Thille, 2006. "Commodity Spot Prices: An Exploratory Assessment of Market Structure and Forward‐Trading Effects," Economica, London School of Economics and Political Science, vol. 73(290), pages 229-256, May.
    17. (Jeremy) Chiu, Ching-wai & Harris, Richard D.F. & Stoja, Evarist & Chin, Michael, 2018. "Financial market Volatility, macroeconomic fundamentals and investor Sentiment," Journal of Banking & Finance, Elsevier, vol. 92(C), pages 130-145.
    18. Martin T. Bohl, Pierre Siklos, Claudia Wellenreuther, 2018. "Speculative Activity and Returns to Volatility of Chinese Major Agricultural Commodity Futures," LCERPA Working Papers 0111, Laurier Centre for Economic Research and Policy Analysis, revised 30 Jan 2018.
    19. Luis E. Arango & Fernando Arias & Adriana Flórez, 2012. "Determinants of commodity prices," Applied Economics, Taylor & Francis Journals, vol. 44(2), pages 135-145, January.
    20. Hayo, Bernd & Kutan, Ali M. & Neuenkirch, Matthias, 2012. "Communication matters: US monetary policy and commodity price volatility," Economics Letters, Elsevier, vol. 117(1), pages 247-249.
    21. Qian Chen & Xin Weng, 2018. "Information Flows Between the US and China’s Agricultural Commodity Futures Markets—Based on VAR–BEKK–Skew-t Model," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(1), pages 71-87, January.
    22. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    23. Bohl, Martin T. & Siklos, Pierre L. & Wellenreuther, Claudia, 2018. "Speculative activity and returns volatility of Chinese agricultural commodity futures," Journal of Asian Economics, Elsevier, vol. 54(C), pages 69-91.
    24. Mallick, Sushanta K. & Sousa, Ricardo M., 2012. "Real Effects Of Monetary Policy In Large Emerging Economies," Macroeconomic Dynamics, Cambridge University Press, vol. 16(S2), pages 190-212, September.
    25. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    26. Chen, Qiwei & Costantini, Mauro & Deschamps, Bruno, 2016. "How accurate are professional forecasts in Asia? Evidence from ten countries," International Journal of Forecasting, Elsevier, vol. 32(1), pages 154-167.
    27. Chernov, Mikhail & Ronald Gallant, A. & Ghysels, Eric & Tauchen, George, 2003. "Alternative models for stock price dynamics," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 225-257.
    28. Bruno Deschamps & Paolo Bianchi, 2012. "An evaluation of Chinese macroeconomic forecasts," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 10(3), pages 229-246, December.
    29. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    30. Liu, Qingfu & Hua, Renhai & An, Yunbi, 2016. "Determinants and information content of intraday bid-ask spreads: Evidence from Chinese commodity futures markets," Pacific-Basin Finance Journal, Elsevier, vol. 38(C), pages 135-148.
    31. Jeffrey A. Frankel, 2008. "The Effect of Monetary Policy on Real Commodity Prices," NBER Chapters, in: Asset Prices and Monetary Policy, pages 291-333, National Bureau of Economic Research, Inc.
    32. Ye, Wuyi & Guo, Ranran & Jiang, Ying & Liu, Xiaoquan & Deschamps, Bruno, 2019. "Professional macroeconomic forecasts and Chinese commodity futures prices," Finance Research Letters, Elsevier, vol. 28(C), pages 130-136.
    33. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M., 2010. "The macroeconomic determinants of volatility in precious metals markets," Resources Policy, Elsevier, vol. 35(2), pages 65-71, June.
    34. Hossein Asgharian & Ai Jun Hou & Farrukh Javed, 2013. "The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 600-612, November.
    35. Dieter Hess & He Huang & Alexandra Niessen, 2008. "How do commodity futures respond to macroeconomic news?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 22(2), pages 127-146, June.
    36. Huayun Jiang & Neda Todorova & Eduardo Roca & Jen-Je Su, 2017. "Dynamics of volatility transmission between the U.S. and the Chinese agricultural futures markets," Applied Economics, Taylor & Francis Journals, vol. 49(34), pages 3435-3452, July.
    37. Harris, Richard D.F. & Stoja, Evarist & Yilmaz, Fatih, 2011. "A cyclical model of exchange rate volatility," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3055-3064, November.
    38. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836.
    39. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    40. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range‐Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
    41. Shawkat Hammoudeh & Duc Khuong Nguyen & Ricardo M. Sousa, 2014. "China’s Monetary Policy and Commodity Prices," Working Papers 2014-298, Department of Research, Ipag Business School.
    42. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    43. Roache, Shaun K. & Rossi, Marco, 2010. "The effects of economic news on commodity prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 377-385, August.
    44. Mo, Di & Gupta, Rakesh & Li, Bin & Singh, Tarlok, 2018. "The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets," Economic Modelling, Elsevier, vol. 70(C), pages 543-560.
    45. Jun Cai & Yan‐Leung Cheung & Michael C. S. Wong, 2001. "What moves the gold market?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 21(3), pages 257-278, March.
    46. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    47. Wang, Yudong & Ma, Feng & Wei, Yu & Wu, Chongfeng, 2016. "Forecasting realized volatility in a changing world: A dynamic model averaging approach," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 136-149.
    48. Christie-David, Rohan & Chaudhry, Mukesh & Koch, Timothy W., 2000. "Do macroeconomics news releases affect gold and silver prices?," Journal of Economics and Business, Elsevier, vol. 52(5), pages 405-421.
    49. Ying Jiang & Shamim Ahmed & Xiaoquan Liu, 2017. "Volatility forecasting in the Chinese commodity futures market with intraday data," Review of Quantitative Finance and Accounting, Springer, vol. 48(4), pages 1123-1173, May.
    50. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, vol. 8(4), pages 323-361, December.
    51. Gargano, Antonio & Timmermann, Allan, 2014. "Forecasting commodity price indexes using macroeconomic and financial predictors," International Journal of Forecasting, Elsevier, vol. 30(3), pages 825-843.
    52. Michael P. Clements, 2015. "Are Professional Macroeconomic Forecasters Able To Do Better Than Forecasting Trends?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(2-3), pages 349-382, March.
    53. Claudia Wellenreuther & Jan Voelzke, 2019. "Speculation and volatility—A time‐varying approach applied on Chinese commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(4), pages 405-417, April.
    54. Shang, Hua & Yuan, Ping & Huang, Lin, 2016. "Macroeconomic factors and the cross-section of commodity futures returns," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 316-332.
    55. Hess, Dieter E. & Huang, He & Niessen-Ruenzi, Alexandra, 2008. "How do commodity futures respond to macroeconomic news?," CFR Working Papers 08-03, University of Cologne, Centre for Financial Research (CFR).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Algirdas Justinas Staugaitis & Bernardas Vaznonis, 2022. "Financial Speculation Impact on Agricultural and Other Commodity Return Volatility: Implications for Sustainable Development and Food Security," Agriculture, MDPI, vol. 12(11), pages 1-27, November.
    2. Zhang, Wei & He, Jie & Ge, Chanyuan & Xue, Rui, 2022. "Real-time macroeconomic monitoring using mixed frequency data: Evidence from China," Economic Modelling, Elsevier, vol. 117(C).
    3. Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024. "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, vol. 92(C).
    4. Zhu, Bo & Lin, Renda & Deng, Yuanyue & Chen, Pingshe & Chevallier, Julien, 2021. "Intersectoral systemic risk spillovers between energy and agriculture under the financial and COVID-19 crises," Economic Modelling, Elsevier, vol. 105(C).
    5. Sara Boni & Massimiliano Caporin & Francesco Ravazzolo, 2024. "Nowcasting Inflation at Quantiles: Causality from Commodities," BEMPS - Bozen Economics & Management Paper Series BEMPS102, Faculty of Economics and Management at the Free University of Bozen.

    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. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
    2. Ye, Wuyi & Guo, Ranran & Jiang, Ying & Liu, Xiaoquan & Deschamps, Bruno, 2019. "Professional macroeconomic forecasts and Chinese commodity futures prices," Finance Research Letters, Elsevier, vol. 28(C), pages 130-136.
    3. Bruno Deschamps & Tianlun Fei & Ying Jiang & Xiaoquan Liu, 2022. "Procyclical volatility in Chinese stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 58(3), pages 1117-1144, April.
    4. Mo, Di & Gupta, Rakesh & Li, Bin & Singh, Tarlok, 2018. "The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets," Economic Modelling, Elsevier, vol. 70(C), pages 543-560.
    5. Tarek Chebbi, 2021. "The response of precious metal futures markets to unconventional monetary surprises in the presence of uncertainty," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1897-1916, April.
    6. Alessandra Amendola & Vincenzo Candila & Antonio Scognamillo, 2017. "On the influence of US monetary policy on crude oil price volatility," Empirical Economics, Springer, vol. 52(1), pages 155-178, February.
    7. (Jeremy) Chiu, Ching-wai & Harris, Richard D.F. & Stoja, Evarist & Chin, Michael, 2018. "Financial market Volatility, macroeconomic fundamentals and investor Sentiment," Journal of Banking & Finance, Elsevier, vol. 92(C), pages 130-145.
    8. Smales, L.A. & Lucey, B.M., 2019. "The influence of investor sentiment on the monetary policy announcement liquidity response in precious metal markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 60(C), pages 19-38.
    9. Xiangyu Chen & Jittima Tongurai, 2021. "The Relationship Between China’s Real Estate Market and Industrial Metals Futures Market: Evidence from Non-price Measures of the Real Estate Market," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(4), pages 527-561, December.
    10. Dinh, Theu & Goutte, Stéphane & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Economic drivers of volatility and correlation in precious metal markets," Journal of Commodity Markets, Elsevier, vol. 28(C).
    11. Bjoern Schulte-Tillman & Mawuli Segnon & Bernd Wilfling, 2022. "Financial-market volatility prediction with multiplicative Markov-switching MIDAS components," CQE Working Papers 9922, Center for Quantitative Economics (CQE), University of Muenster.
    12. Amendola, Alessandra & Candila, Vincenzo & Gallo, Giampiero M., 2019. "On the asymmetric impact of macro–variables on volatility," Economic Modelling, Elsevier, vol. 76(C), pages 135-152.
    13. Li, Tao & Ma, Feng & Zhang, Xuehua & Zhang, Yaojie, 2020. "Economic policy uncertainty and the Chinese stock market volatility: Novel evidence," Economic Modelling, Elsevier, vol. 87(C), pages 24-33.
    14. Wei, Yu & Liu, Jing & Lai, Xiaodong & Hu, Yang, 2017. "Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 141-150.
    15. Ruobing Liu & Jianhui Yang & Chuan-Yang Ruan, 2019. "The Impact of Macroeconomic News on Chinese Futures," IJFS, MDPI, vol. 7(4), pages 1-14, October.
    16. Xu Gong & Mingchao Wang & Liuguo Shao, 2022. "The impact of macro economy on the oil price volatility from the perspective of mixing frequency," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4487-4514, October.
    17. Fang, Libing & Yu, Honghai & Xiao, Wen, 2018. "Forecasting gold futures market volatility using macroeconomic variables in the United States," Economic Modelling, Elsevier, vol. 72(C), pages 249-259.
    18. Charlot, Philippe & Marimoutou, Vêlayoudom, 2014. "On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree," Energy Economics, Elsevier, vol. 44(C), pages 456-467.
    19. Emiliano Magrini & Ayca Donmez, 2013. "Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach," JRC Research Reports JRC84138, Joint Research Centre.
    20. Smales, Lee A. & Yang, Yi, 2015. "The importance of belief dispersion in the response of gold futures to macroeconomic announcements," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 292-302.

    More about this item

    Keywords

    Commodity futures; Volatility; GARCH-MIDAS model; Macroeconomic forecasts;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    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:ecmode:v:94:y:2021:i:c:p:981-994. 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/inca/30411 .

    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.