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Constrained Smoothing Splines for the Term Structure of Interest Rates

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  • Laurini, Márcio P.
  • Moura, Marcelo

Abstract

The constrained smoothing B-splines (COBS) is proposed as a nonparametric approach to estimate the term structure of interest rate. Compared to the existing methods in the literature, COBS' main innovation lies in its incorporation of important constraints imposed by no-arbitrage, such as monotonically decreasing and boundary conditions for the discount function, positive forward and spot rates. In addition, by estimating the conditional median function, COBS is less sensible to outliers in reduced samples than other common methods in the literature. Estimation for high and low liquidity markets together with simulation exercises puts COBS in an intermediate position between usual parametric and nonparametric methods in the literature. It has more flexibility than parametric methods and, compared to other nonparametric methods, satisfies no-arbitrage constraints and generates parsimonious shapes of the term structure of interest rates.
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  • Laurini, Márcio P. & Moura, Marcelo, 2007. "Constrained Smoothing Splines for the Term Structure of Interest Rates," Insper Working Papers wpe_100, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
  • Handle: RePEc:ibm:ibmecp:wpe_100
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    Cited by:

    1. M�rcio Poletti Laurini, 2014. "Dynamic functional data analysis with non-parametric state space models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 142-163, January.
    2. Márcio Poletti Laurini & Armênio Westin Neto, 2014. "Arbitrage in the Term Structure of Interest Rates: a Bayesian Approach," International Econometric Review (IER), Econometric Research Association, vol. 6(2), pages 77-99, September.
    3. Victor Lapshin, 2019. "A Nonparametric Approach to Bond Portfolio Immunization," Mathematics, MDPI, vol. 7(11), pages 1-12, November.
    4. Luo, Sirong & Kong, Xiao & Nie, Tingting, 2016. "Spline based survival model for credit risk modeling," European Journal of Operational Research, Elsevier, vol. 253(3), pages 869-879.
    5. Cousin, Areski & Maatouk, Hassan & Rullière, Didier, 2016. "Kriging of financial term-structures," European Journal of Operational Research, Elsevier, vol. 255(2), pages 631-648.
    6. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "A simple and general approach to fitting the discount curve under no-arbitrage constraints," Finance Research Letters, Elsevier, vol. 15(C), pages 78-84.
    7. Varga, Gyorgy, 2009. "Teste de Modelos Estatísticos para a Estrutura a Termo no Brasil [Test of Term Structure Models for Brazil]," MPRA Paper 20832, University Library of Munich, Germany.
    8. Yallup, Peter J., 2012. "Models of the yield curve and the curvature of the implied forward rate function," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 121-135.
    9. repec:erh:journl:v:6:y:2014:i:2:p:78-100 is not listed on IDEAS
    10. Eduardo Mineo & Airlane Pereira Alencar & Marcelo Moura & Antonio Elias Fabris, 2020. "Forecasting the Term Structure of Interest Rates with Dynamic Constrained Smoothing B-Splines," JRFM, MDPI, vol. 13(4), pages 1-14, April.
    11. Varga, Gyorgy, 2009. "Teste de Modelos Estatísticos para a Estrutura a Termo no Brasil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 63(4), December.
    12. Damir Filipović & Sander Willems, 2016. "Exact Smooth Term Structure Estimation," Swiss Finance Institute Research Paper Series 16-38, Swiss Finance Institute.

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