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Bayesian extensions to Diebold-Li term structure model

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  • Laurini, Márcio Poletti
  • Hotta, Luiz Koodi

Abstract

This paper proposes a statistical model to adjust, interpolate, and forecast the term structure of interest rates. The model is based on the extensions for the term structure model of interest rates proposed by Diebold and Li (2006), through a Bayesian estimation using Markov Chain Monte Carlo (MCMC). The proposed extensions involve the use of a more flexible parametric form for the yield curve, allowing all the parameters to vary in time using a structure of latent factors, and the addition of a stochastic volatility structure to control the presence of conditional heteroskedasticity observed in the interest rates. The Bayesian estimation enables the exact distribution of the estimators in finite samples, and as a by-product, the estimation enables obtaining the distribution of forecasts of the term structure of interest rates. Unlike some econometric models of term structure, the methodology developed does not require a pre-interpolation of the yield curve. The model is fitted to the daily data of the term structure of interest rates implicit in SWAP DI-PRÉ contracts traded in the Mercantile and Futures Exchange (BM&F) in Brazil. The results are compared with the other models in terms of fitting and forecasts.

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  • Laurini, Márcio Poletti & Hotta, Luiz Koodi, 2010. "Bayesian extensions to Diebold-Li term structure model," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 342-350, December.
    • Handle: RePEc:eee:finana:v:19:y:2010:i:5:p:342-350
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    1. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    2. Vadim Kaushanskiy & Victor Lapshin, 2016. "A nonparametric method for term structure fitting with automatic smoothing," Applied Economics, Taylor & Francis Journals, vol. 48(58), pages 5654-5666, December.
    3. 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.
    4. 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.
    5. Tunaru, Diana, 2017. "Gaussian estimation and forecasting of the U.K. yield curve with multi-factor continuous-time models," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 119-129.
    6. Daniel R. Kowal & Antonio Canale, 2021. "Semiparametric Functional Factor Models with Bayesian Rank Selection," Papers 2108.02151, arXiv.org, revised May 2022.
    7. Caldeira, João F. & Laurini, Márcio P. & Portugal, Marcelo S., 2010. "Bayesian Inference Applied to Dynamic Nelson-Siegel Model with Stochastic Volatility," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 30(1), October.
    8. Vahidin Jeleskovic & Anastasios Demertzidis, 2018. "Comparing different methods for the estimation of interbank intraday yield curves," MAGKS Papers on Economics 201839, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    9. Aryo Sasongko & Cynthia Afriani Utama & Buddi Wibowo & Zaäfri Ananto Husodo, 2019. "Modifying Hybrid Optimisation Algorithms to Construct Spot Term Structure of Interest Rates and Proposing a Standardised Assessment," Computational Economics, Springer;Society for Computational Economics, vol. 54(3), pages 957-1003, October.
    10. Renata Tavanielli & Márcio Laurini, 2023. "Yield Curve Models with Regime Changes: An Analysis for the Brazilian Interest Rate Market," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
    11. repec:erh:journl:v:6:y:2014:i:2:p:78-100 is not listed on IDEAS
    12. Sourish Das, 2018. "Modeling Nelson-Siegel Yield Curve using Bayesian Approach," Papers 1809.06077, arXiv.org, revised Oct 2018.
    13. Tunaru, Radu & Zheng, Teng, 2017. "Parameter estimation risk in asset pricing and risk management: A Bayesian approach," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 80-93.
    14. Dang-Nguyen, Stéphane & Le Caillec, Jean-Marc & Hillion, Alain, 2014. "The deterministic shift extension and the affine dynamic Nelson–Siegel model," The North American Journal of Economics and Finance, Elsevier, vol. 29(C), pages 402-417.

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