IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/202098.html
   My bibliography  Save this paper

Time-Varying Risk Aversion and Forecastability of the US Term Structure of Interest Rates

Author

Listed:
  • Elie Bouri

    (Adnan Kassar School of Business, Lebanese American University, Lebanon)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Anandamayee Majumdar

    (Department of Physical Sciences, School of Engineering, Technology & Sciences, Independent University, Bangladesh, Dhaka 1229, Bangladesh)

  • Sowmya Subramaniam

    (Indian Institute of Management Lucknow, Prabandh Nagar off Sitapur Road, Lucknow, Uttar Pradesh 226013, India)

Abstract

In this paper, we analyse the forecasting ability of a time-varying metric of daily risk aversion for the entire term structure of interest rates of Treasury securities of the United States (US) as reflected by the three latent factors, level, slope and curvature. Using daily data covering the out-of-sample period 22nd June, 1988 to 3rd September, 2020 (given the in-sample period 30th May, 1986 to 21st June, 1988) within a quantiles-based framework, the results show statistically significant forecasting gains emanating from risk aversion for the tails of the conditional distributions of the level, slope and curvature factors at horizons of one-day, one-week, and one-month-ahead. Interestingly, a conditional mean-based model fails to detect any evidence of out-of-sample predictability. Our findings have important implications for academics, bond investors, and policymakers in their quest to better understand the evolution of future movement in US Treasury securities.

Suggested Citation

  • Elie Bouri & Rangan Gupta & Anandamayee Majumdar & Sowmya Subramaniam, 2020. "Time-Varying Risk Aversion and Forecastability of the US Term Structure of Interest Rates," Working Papers 202098, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202098
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Maurizio Michael Habib & Livio Stracca, 2015. "Is There a Global Safe Haven?," International Finance, Wiley Blackwell, vol. 18(3), pages 281-298, December.
    2. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).
    3. Diebold, Francis X. & Li, Canlin, 2006. "Forecasting the term structure of government bond yields," Journal of Econometrics, Elsevier, vol. 130(2), pages 337-364, February.
    4. Geert Bekaert & Eric C. Engstrom & Nancy R. Xu, 2022. "The Time Variation in Risk Appetite and Uncertainty," Management Science, INFORMS, vol. 68(6), pages 3975-4004, June.
    5. John Y. Campbell, 2008. "Viewpoint: Estimating the equity premium," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 41(1), pages 1-21, February.
    6. Eric Hillebrand & Huiyu Huang & Tae-Hwy Lee & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    7. Gurkaynak, Refet S. & Sack, Brian & Wright, Jonathan H., 2007. "The U.S. Treasury yield curve: 1961 to the present," Journal of Monetary Economics, Elsevier, vol. 54(8), pages 2291-2304, November.
    8. John Y. Campbell, 2007. "Estimating the Equity Premium," NBER Working Papers 13423, National Bureau of Economic Research, Inc.
    9. Laborda, Ricardo & Olmo, Jose, 2014. "Investor sentiment and bond risk premia," Journal of Financial Markets, Elsevier, vol. 18(C), pages 206-233.
    10. Çepni, Oğguzhan & Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian, 2020. "Time-varying risk aversion and the predictability of bond premia," Finance Research Letters, Elsevier, vol. 34(C).
    11. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    12. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    13. Kim, Hwagyun & Park, Hail, 2013. "Term structure dynamics with macro-factors using high frequency data," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 78-93.
    14. Demirer, Riza & Omay, Tolga & Yuksel, Asli & Yuksel, Aydin, 2018. "Global risk aversion and emerging market return comovements," Economics Letters, Elsevier, vol. 173(C), pages 118-121.
    15. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    16. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    17. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    18. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    19. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    20. Kateryna Anatoliyevna Kopyl & John Byong-Tek Lee, 2016. "How safe are the safe haven assets?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 30(4), pages 453-482, November.
    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. Xu, Xin & Xu, Xiaoguang, 2023. "Monetary policy transmission modeling and policy responses," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).

    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. Gupta, Rangan & Subramaniam, Sowmya & Bouri, Elie & Ji, Qiang, 2021. "Infectious disease-related uncertainty and the safe-haven characteristic of US treasury securities," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 289-298.
    2. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).
    3. Elie Bouri & Rangan Gupta & Clement Kweku Kyei & Sowmya Subramaniam, 2020. "High-Frequency Movements of the Term Structure of Interest Rates of the United States: The Role of Oil Market Uncertainty," Working Papers 202085, University of Pretoria, Department of Economics.
    4. Rangan Gupta & Anandamayee Majumdar & Mark E. Wohar, 2017. "The Role of Current Account Balance in Forecasting the US Equity Premium: Evidence From a Quantile Predictive Regression Approach," Open Economies Review, Springer, vol. 28(1), pages 47-59, February.
    5. Balcilar, Mehmet & Gupta, Rangan & Wang, Shixuan & Wohar, Mark E., 2020. "Oil price uncertainty and movements in the US government bond risk premia," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    6. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, vol. 62(C), pages 181-186.
    7. Mehmet Balcilar & Rangan Gupta & Christian Pierdzioch, 2022. "Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data," Energies, MDPI, vol. 15(22), pages 1-26, November.
    8. Gupta, Rangan & Ji, Qiang & Pierdzioch, Christian & Plakandaras, Vasilios, 2023. "Forecasting the conditional distribution of realized volatility of oil price returns: The role of skewness over 1859 to 2023," Finance Research Letters, Elsevier, vol. 58(PC).
    9. Gupta, Rangan & Mwamba, John W. Muteba & Wohar, Mark E., 2018. "The role of partisan conflict in forecasting the U.S. equity premium: A nonparametric approach," Finance Research Letters, Elsevier, vol. 25(C), pages 131-136.
    10. Christou, Christina & Gupta, Rangan, 2020. "Forecasting equity premium in a panel of OECD countries: The role of economic policy uncertainty," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 243-248.
    11. Christou, Christina & Gupta, Rangan & Jawadi, Fredj, 2021. "Does inequality help in forecasting equity premium in a panel of G7 countries?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    12. Plakandaras, Vasilios & Gupta, Rangan & Wong, Wing-Keung, 2019. "Point and density forecasts of oil returns: The role of geopolitical risks," Resources Policy, Elsevier, vol. 62(C), pages 580-587.
    13. Cepni, Oguzhan & Gupta, Rangan & Karahan, Cenk C. & Lucey, Brian, 2022. "Oil price shocks and yield curve dynamics in emerging markets," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 613-623.
    14. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2021. "Variants of consumption‐wealth ratios and predictability of U.S. government bond risk premia," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 661-674, June.
    15. Edmond Berisha & David Gabauer & Rangan Gupta & Chi Keung Marco Lau, 2021. "Time-varying influence of household debt on inequality in United Kingdom," Empirical Economics, Springer, vol. 61(4), pages 1917-1933, October.
    16. Oguzhan Cepni & Rangan Gupta & Mark E. Wohar, 2019. "Variants of Consumption-Wealth Ratios and Predictability of U.S. Government Bond Risk Premia: Old is still Gold," Working Papers 201912, University of Pretoria, Department of Economics.
    17. Rangan Gupta & Christian Pierdzioch & Wing-Keung Wong, 2021. "A Note on Forecasting the Historical Realized Variance of Oil-Price Movements: The Role of Gold-to-Silver and Gold-to-Platinum Price Ratios," Energies, MDPI, vol. 14(20), pages 1-12, October.
    18. Çepni, Oğguzhan & Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian, 2020. "Time-varying risk aversion and the predictability of bond premia," Finance Research Letters, Elsevier, vol. 34(C).
    19. Narayan, Paresh Kumar & Gupta, Rangan, 2015. "Has oil price predicted stock returns for over a century?," Energy Economics, Elsevier, vol. 48(C), pages 18-23.
    20. Oguzhan Cepni & Rangan Gupta & I. Ethem Güney & M. Yilmaz, 2020. "Forecasting local currency bond risk premia of emerging markets: The role of cross‐country macrofinancial linkages," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 966-985, September.

    More about this item

    Keywords

    Yield Curve Factors; Risk Aversion; Out-of-Sample Forecasts;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pre:wpaper:202098. 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: Rangan Gupta (email available below). General contact details of provider: https://edirc.repec.org/data/decupza.html .

    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.