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Efficient Dynamic Yield Curve Estimation in Emerging Financial Markets

Author

Listed:
  • Makram El-Shagi

    () (Center for Financial Development and Stability at Henan University, and School of Economics at Henan University, Kaifeng, Henan)

  • Lunan Jiang

    (Center for Financial Development and Stability at Henan University, and School of Economics at Henan University, Kaifeng, Henan)

Abstract

The current state-of-the-art estimation of yield curves relies on the dynamic state space version of the Nelson and Siegel (1987) model proposed in the seminal paper by Diebold et al. (2006). However, things become difficult when applying their approach to emerging economies with less frequently bond issuance and more sparse maturity available. Therefore, the traditional state space representation, which requires dense and fixed grids of maturities, may not be possible. One remedy is to use the traditional Nelson and Siegel (1987) OLS estimation instead, though it sacrifices efficiency by ignoring the time dimension. We propose a simple augmentation of the Diebold et al. (2006) framework, which is more efficient than OLS estimation as it allows exploiting information from all available bonds and the time dependency of yields. We demonstrate the efficiency gains generated by our method in five case studies for major emerging economies including four of the BRICS.

Suggested Citation

  • Makram El-Shagi & Lunan Jiang, 2019. "Efficient Dynamic Yield Curve Estimation in Emerging Financial Markets," CFDS Discussion Paper Series 2019/4, Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China.
  • Handle: RePEc:fds:dpaper:201904
    as

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    File URL: http://cfds.henuecon.education/images/dpaper/WP_4_2019_YC_Estimation.pdf
    File Function: First version, 2019
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    References listed on IDEAS

    as
    1. Vicente, José & Tabak, Benjamin M., 2008. "Forecasting bond yields in the Brazilian fixed income market," International Journal of Forecasting, Elsevier, vol. 24(3), pages 490-497.
    2. Melvin Muzi Khomo & Meshach Jesse Aziakpono, 2007. "Forecasting Recession In South Africa: A Comparison Of The Yield Curve And Other Economic Indicators," South African Journal of Economics, Economic Society of South Africa, vol. 75(2), pages 194-212, June.
    3. Hugo Nel*, 1996. "The Term Structure of Interest Rates and Economic Activity in South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 64(3), pages 151-157, September.
    4. Rafael B. Rezende & Mauro S. Ferreira, 2013. "Modeling and Forecasting the Yield Curve by an Extended Nelson‐Siegel Class of Models: A Quantile Autoregression Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(2), pages 111-123, March.
    5. 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.
    6. Diebold, Francis X. & Rudebusch, Glenn D. & Borag[caron]an Aruoba, S., 2006. "The macroeconomy and the yield curve: a dynamic latent factor approach," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 309-338.
    7. Makram El-Shagi & Lunan Jiang, 2017. "China Monetary Policy Transmission in China: Dual Shocks with Dual Bond Markets," CFDS Discussion Paper Series 2017/2, Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Yield curve; dynamic modeling; state space model; efficiency; BRICS;

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects

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