IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/201911.html
   My bibliography  Save this paper

Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis

Author

Listed:
  • Joao F. Caldeira

    (Department of Economics, Universidade Federal do Rio Grande do Sul and CNPq, Brazil)

  • Rangan Gupta

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

  • Tahir Suleman

    (School of Economics and Finance, Victoria University of Wellington & School of Business, Wellington Institute of Technology, New Zealand)

  • Hudson S. Torrent

    (Department of Statistics, Universidade Federal do Rio Grande do Sul, Brazil)

Abstract

In this paper, we develop a non-parametric functional data analysis (NP-FDA) model to forecast the term-structure of Brazil, Russia, India, China and South Africa (BRICS). We use daily data over the period of January 1, 2010 to December 31, 2016. We find that, while it is in general difficult to beat the random-walk model in the shorter-horizons, at longer-runs our proposed NP-FDA approach outperforms not only the random-walk model, but also other popular competitors used in term-structure forecasting literature. Our results have important implications for both policymakers aiming to stabilize the economy, and for optimal portfolio allocation decisions of financial market agents.

Suggested Citation

  • Joao F. Caldeira & Rangan Gupta & Tahir Suleman & Hudson S. Torrent, 2019. "Forecasting the Term Structure of Interest Rates of the BRICS: Evidence from a Nonparametric Functional Data Analysis," Working Papers 201911, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201911
    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. Hui-Chu Shu & Jung-Hsien Chang & Ting-Ya Lo, 2018. "Forecasting the Term Structure of South African Government Bond Yields," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(1), pages 41-53, January.
    2. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
    3. Moench, Emanuel, 2008. "Forecasting the yield curve in a data-rich environment: A no-arbitrage factor-augmented VAR approach," Journal of Econometrics, Elsevier, vol. 146(1), pages 26-43, September.
    4. Christensen, Jens H.E. & Diebold, Francis X. & Rudebusch, Glenn D., 2011. "The affine arbitrage-free class of Nelson-Siegel term structure models," Journal of Econometrics, Elsevier, vol. 164(1), pages 4-20, September.
    5. 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.
    6. Doh, Taeyoung, 2011. "Yield curve in an estimated nonlinear macro model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(8), pages 1229-1244, August.
    7. Sowmya, Subramaniam & Prasanna, Krishna & Bhaduri, Saumitra, 2016. "Linkages in the term structure of interest rates across sovereign bond markets," Emerging Markets Review, Elsevier, vol. 27(C), pages 118-139.
    8. Vieira, Fausto & Fernandes, Marcelo & Chague, Fernando, 2017. "Forecasting the Brazilian yield curve using forward-looking variables," International Journal of Forecasting, Elsevier, vol. 33(1), pages 121-131.
    9. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Bond portfolio optimization using dynamic factor models," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 128-158.
    10. Ahmad, Wasim & Mishra, Anil V. & Daly, Kevin J., 2018. "Financial connectedness of BRICS and global sovereign bond markets," Emerging Markets Review, Elsevier, vol. 37(C), pages 1-16.
    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. Shang, Yuhuang & Zheng, Tingguo, 2018. "Fitting and forecasting yield curves with a mixed-frequency affine model: Evidence from China," Economic Modelling, Elsevier, vol. 68(C), pages 145-154.
    13. Vasilios Plakandaras & Rangan Gupta & Luis A. Gil-Alana & Mark E. Wohar, 2019. "Are BRICS exchange rates chaotic?," Applied Economics Letters, Taylor & Francis Journals, vol. 26(13), pages 1104-1110, July.
    14. Borus Jungbacker & Siem Jan Koopman, 2015. "Likelihood‐based dynamic factor analysis for measurement and forecasting," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 1-21, June.
    15. Balcilar, Mehmet & Bonato, Matteo & Demirer, Riza & Gupta, Rangan, 2018. "Geopolitical risks and stock market dynamics of the BRICS," Economic Systems, Elsevier, vol. 42(2), pages 295-306.
    16. Gregory R. Duffee, 2002. "Term Premia and Interest Rate Forecasts in Affine Models," Journal of Finance, American Finance Association, vol. 57(1), pages 405-443, February.
    17. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2017. "Forecasting the term structure of government bond yields in unstable environments," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 209-225.
    18. Vasilios Plakandaras & Juncal Cunado & Rangan Gupta & Mark E. Wohar, 2017. "Do leading indicators forecast U.S. recessions? A nonlinear re†evaluation using historical data," International Finance, Wiley Blackwell, vol. 20(3), pages 289-316, December.
    19. Rangan Gupta & Hylton Hollander & Rudi Steinbach, 2020. "Forecasting output growth using a DSGE-based decomposition of the South African yield curve," Empirical Economics, Springer, vol. 58(1), pages 351-378, January.
    20. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2016. "The Term Premium as a Leading Macroeconomic Indicator," Working Papers 201613, University of Pretoria, Department of Economics.
    21. Pan Feng & Junhui Qian, 2018. "Analyzing and forecasting the Chinese term structure of interest rates using functional principal component analysis," China Finance Review International, Emerald Group Publishing, vol. 8(3), pages 275-296, August.
    22. Goodness C. Aye & Christina Christou & Luis A. Gil‐Alana & Rangan Gupta, 2019. "Forecasting the Probability of Recessions in South Africa: the Role of Decomposed Term Spread and Economic Policy Uncertainty," Journal of International Development, John Wiley & Sons, Ltd., vol. 31(1), pages 101-116, January.
    23. 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.
    24. JoÃo Caldeira & Hudson Torrent, 2017. "Forecasting the US Term Structure of Interest Rates Using Nonparametric Functional Data Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(1), pages 56-73, January.
    25. João F. Caldeira & Guilherme V. Moura & André A. P. Santos, 2018. "Yield curve forecast combinations based on bond portfolio performance," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 64-82, January.
    26. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
    27. Krishna Prasanna & Subramaniam Sowmya, 2017. "Yield curve in India and its interactions with the US bond market," International Economics and Economic Policy, Springer, vol. 14(2), pages 353-375, April.
    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. Rangan Gupta & Syed Jawad Hussain Shahzad & Xin Sheng & Sowmya Subramaniam, 2020. "The Role of Oil and Risk Shocks in the High-Frequency Movements of the Term Structure of Interest Rates of the United States," Working Papers 202063, University of Pretoria, Department of Economics.
    2. 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.

    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. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Ronald Ravinesh Kumar & Peter Josef Stauvermann & Hang Thi Thu Vu, 2021. "The Relationship between Yield Curve and Economic Activity: An Analysis of G7 Countries," JRFM, MDPI, vol. 14(2), pages 1-23, February.
    3. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Switching Nelson-Siegel Models," BAFFI CAREFIN Working Papers 19106, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    4. Guidolin, Massimo & Pedio, Manuela, 2019. "Forecasting and trading monetary policy effects on the riskless yield curve with regime switching Nelson–Siegel models," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    5. Caio Almeida & Kym Ardison & Daniela Kubudi & Axel Simonsen & José Vicente, 2018. "Forecasting Bond Yields with Segmented Term Structure Models," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 16(1), pages 1-33.
    6. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
    7. Stona, Filipe & Caldeira, João F., 2019. "Do U.S. factors impact the Brazilian yield curve? Evidence from a dynamic factor model," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 76-89.
    8. Wali ULLAH & Khadija Malik BARI, 2018. "The Term Structure of Government Bond Yields in an Emerging Market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-28, September.
    9. Fausto Vieira & Fernando Chague, Marcelo Fernandes, 2016. "A dynamic Nelson-Siegel model with forward-looking indicators for the yield curve in the US," Working Papers, Department of Economics 2016_31, University of São Paulo (FEA-USP).
    10. P. Byrne, Joseph & Cao, Shuo & Korobilis, Dimitris, 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," SIRE Discussion Papers 2015-71, Scottish Institute for Research in Economics (SIRE).
    11. Christensen, Jens H.E. & Diebold, Francis X. & Rudebusch, Glenn D., 2011. "The affine arbitrage-free class of Nelson-Siegel term structure models," Journal of Econometrics, Elsevier, vol. 164(1), pages 4-20, September.
    12. Giuseppe Arbia & Michele Di Marcantonio, 2015. "Forecasting Interest Rates Using Geostatistical Techniques," Econometrics, MDPI, vol. 3(4), pages 1-28, November.
    13. Carlo A. Favero & Linlin Niu & Luca Sala, 2012. "Term Structure Forecasting: No‐Arbitrage Restrictions versus Large Information Set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(2), pages 124-156, March.
    14. Leo Krippner & Michelle Lewis, 2018. "Real-time forecasting with macro-finance models in the presence of a zero lower bound," Reserve Bank of New Zealand Discussion Paper Series DP2018/04, Reserve Bank of New Zealand.
    15. Erhard RESCHENHOFER & Thomas STARK, 2019. "Forecasting the Yield Curve with Dynamic Factors," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 101-113, March.
    16. Michal Dvorák & Zlatuše Komárková & Adam Kucera, 2019. "The Czech Government Yield Curve Decomposition at the Lower Bound," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 69(1), pages 2-36, February.
    17. Fernandes, Marcelo & Vieira, Fausto, 2019. "A dynamic Nelson–Siegel model with forward-looking macroeconomic factors for the yield curve in the US," Journal of Economic Dynamics and Control, Elsevier, vol. 106(C), pages 1-1.
    18. Tu, Anthony H. & Chen, Cathy Yi-Hsuan, 2018. "A factor-based approach of bond portfolio value-at-risk: The informational roles of macroeconomic and financial stress factors," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 243-268.
    19. Gaus, Eric & Sinha, Arunima, 2018. "What does the yield curve imply about investor expectations?," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 248-265.
    20. Wali Ullah & Yasumasa Matsuda, 2014. "Generalized Nelson-Siegel Term Structure Model : Do the second slope and curvature factors improve the in-sample fit and out-of-sample forecast?," TERG Discussion Papers 312, Graduate School of Economics and Management, Tohoku University.

    More about this item

    Keywords

    Functional data analysis; yield curve forecasting; performance evaluation; BRICS;
    All these keywords.

    JEL classification:

    • 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
    • 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:201911. 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/decupza.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: 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 hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.