IDEAS home Printed from https://ideas.repec.org/a/kap/revdev/v25y2022i1d10.1007_s11147-021-09182-8.html
   My bibliography  Save this article

Economic policy uncertainty and volatility of treasury futures

Author

Listed:
  • Maojun Zhang

    (Suzhou University of Science and Technology)

  • Yang Zhao

    (Suzhou University of Science and Technology)

  • Jiangxia Nan

    (Suzhou University of Science and Technology)

Abstract

This paper investigates the relation between Treasury futures market volatility and economic policy uncertainty using GARCH-MIDAS. We formulated models with the realized volatility of Treasury futures, the level and volatility of economic policy uncertainty. We find that the realized volatility of Treasury futures and economic policy uncertainty play a significant role in the dynamics of long-run volatility in Treasury futures markets in China and United States.

Suggested Citation

  • Maojun Zhang & Yang Zhao & Jiangxia Nan, 2022. "Economic policy uncertainty and volatility of treasury futures," Review of Derivatives Research, Springer, vol. 25(1), pages 93-107, April.
  • Handle: RePEc:kap:revdev:v:25:y:2022:i:1:d:10.1007_s11147-021-09182-8
    DOI: 10.1007/s11147-021-09182-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11147-021-09182-8
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11147-021-09182-8?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. 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.
    2. Michael W. Brandt & Kenneth A. Kavajecz & Shane E. Underwood, 2007. "Price discovery in the treasury futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(11), pages 1021-1051, November.
    3. Weihua Shi & Larry Eisenberg & Cheng-few Lee, 2009. "Intraday Patterns, Announcement Effects, and Volatility Persistence in the Japanese Government Bond Futures Market," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 63-85.
    4. Ederington, Louis H & Lee, Jae Ha, 1993. "How Markets Process Information: News Releases and Volatility," Journal of Finance, American Finance Association, vol. 48(4), pages 1161-1191, September.
    5. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    6. Fang, Libing & Yu, Honghai & Li, Lei, 2017. "The effect of economic policy uncertainty on the long-term correlation between U.S. stock and bond markets," Economic Modelling, Elsevier, vol. 66(C), pages 139-145.
    7. Kasing Man & Junbo Wang & Chunchi Wu, 2013. "Price Discovery in the U.S. Treasury Market: Automation vs. Intermediation," Management Science, INFORMS, vol. 59(3), pages 695-714, September.
    8. 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.
    9. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2006. "Predicting volatility: getting the most out of return data sampled at different frequencies," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 59-95.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. 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.
    12. Orlowski, Lucjan T., 2015. "From pit to electronic trading: Impact on price volatility of U.S. Treasury futures," Review of Financial Economics, Elsevier, vol. 25(C), pages 3-9.
    13. repec:bla:jfinan:v:59:y:2004:i:6:p:2623-2654 is not listed on IDEAS
    14. Mao‐Wei Hung & Hua Zhang, 1995. "Price movements and price discovery in the municipal bond index and the index futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(4), pages 489-506, June.
    15. Wisniewski, Tomasz Piotr & Lambe, Brendan John, 2015. "Does economic policy uncertainty drive CDS spreads?," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 447-458.
    16. Huseyin Gulen & Mihai Ion, 2016. "Editor's Choice Policy Uncertainty and Corporate Investment," The Review of Financial Studies, Society for Financial Studies, vol. 29(3), pages 523-564.
    17. 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. Mei, Dexiang & Zhao, Chenchen & Luo, Qin & Li, Yan, 2022. "Forecasting the Chinese low-carbon index volatility," Resources Policy, Elsevier, vol. 77(C).
    2. Fang, Tong & Lee, Tae-Hwy & Su, Zhi, 2020. "Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection," Journal of Empirical Finance, Elsevier, vol. 58(C), pages 36-49.
    3. Lu Wang & Feng Ma & Guoshan Liu & Qiaoqi Lang, 2023. "Do extreme shocks help forecast oil price volatility? The augmented GARCH‐MIDAS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 2056-2073, April.
    4. Jon Danielsson & Marcela Valenzuela & Ilknur Zer, 2018. "Learning from History: Volatility and Financial Crises," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2774-2805.
    5. Belcaid, Karim & El Ghini, Ahmed, 2019. "U.S., European, Chinese economic policy uncertainty and Moroccan stock market volatility," The Journal of Economic Asymmetries, Elsevier, vol. 20(C).
    6. Amendola, A. & Candila, V. & Cipollini, F. & Gallo, G.M., 2024. "Doubly multiplicative error models with long- and short-run components," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    7. Christian Conrad & Melanie Schienle, 2020. "Testing for an Omitted Multiplicative Long-Term Component in GARCH Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 229-242, April.
    8. Yun-Shi Dai & Peng-Fei Dai & Wei-Xing Zhou, 2024. "The impact of geopolitical risk on the international agricultural market: Empirical analysis based on the GJR-GARCH-MIDAS model," Papers 2404.01641, arXiv.org.
    9. 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.
    10. Yu, Xiaoling & Huang, Yirong, 2021. "The impact of economic policy uncertainty on stock volatility: Evidence from GARCH–MIDAS approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    11. Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti, 2019. "Macro-Financial Linkages in the High-Frequency Domain: The Effects of Uncertainty on Realized Volatility," CESifo Working Paper Series 8000, CESifo.
    12. Yu, Honghai & Fang, Libing & Sun, Wencong, 2018. "Forecasting performance of global economic policy uncertainty for volatility of Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 931-940.
    13. Kaminska, Iryna & Roberts-Sklar, Matt, 2018. "Volatility in equity markets and monetary policy rate uncertainty," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 68-83.
    14. 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.
    15. Vincenzo Candila & Oguzhan Cepni & Giampiero M. Gallo & Rangan Gupta, 2024. "Influence of Local and Global Economic Policy Uncertainty on the Volatility of US State-Level Equity Returns: Evidence from a GARCH-MIDAS Approach with Shrinkage and Cluster Analysis," Working Papers 202437, University of Pretoria, Department of Economics.
    16. Julien Chevallier & Bilel Sanhaji, 2023. "Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices," Stats, MDPI, vol. 6(4), pages 1-32, December.
    17. Emiliano Magrini & Ayca Donmez, 2013. "Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach," JRC Research Reports JRC84138, Joint Research Centre.
    18. Vincenzo Candila & Oguzhan Cepni & Giampiero M. Gallo & Rangan Gupta, 2024. "Influence of Local and Global Economic Policy Uncertainty on the Volatility of US State-Level Equity Returns: Evidence from a GARCH-MIDAS Approach with Shrinkage and Cluster Analysis," Working Papers 202437, University of Pretoria, Department of Economics.
    19. Segnon, Mawuli & Gupta, Rangan & Wilfling, Bernd, 2024. "Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks," International Journal of Forecasting, Elsevier, vol. 40(1), pages 29-43.
    20. Walther, Thomas & Klein, Tony & Bouri, Elie, 2019. "Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).

    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:kap:revdev:v:25:y:2022:i:1:d:10.1007_s11147-021-09182-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.