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No Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates

Author

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  • Marcellino, Massimiliano
  • Carriero, Andrea
  • Clark, Todd
Abstract
We propose a method to produce density forecasts of the term structure of government bond yields that accounts for (i) the possible mispecification of an underlying Gaussian Affine Term Structure Model (GATSM) and (ii) the time varying volatility of interest rates. For this, we derive a Bayesian prior from a GATSM and use it to estimate the coefficients of a BVAR for the term structure, specifying a common, multiplicative, time varying volatility for the VAR disturbances. Results based on U.S. data show that this method significantly improves the precision of point and density forecasts of the term structure. While this paper focuses on term structure modelling, the proposed method can be applied for a wide range of alternative models, including DSGE models, and is a generalization of the method of Del Negro and Schorfheide (2004) to VARs featuring drifting volatilities. The method also generalizes the model of Giannone et al. (2012), by specifying hierarchically not only the prior variance but also the prior mean of the VAR coefficients. Our results show that both time variation in volatilities, and a hierarchical specification for the prior means, improve model fit and forecasting performance.

Suggested Citation

  • Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2014. "No Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," CEPR Discussion Papers 9848, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:9848
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    1. Shin, Minchul & Zhong, Molin, 2017. "Does realized volatility help bond yield density prediction?," International Journal of Forecasting, Elsevier, vol. 33(2), pages 373-389.
    2. Florian Huber & Tamás Krisztin & Philipp Piribauer, 2017. "Forecasting Global Equity Indices Using Large Bayesian Vars," Bulletin of Economic Research, Wiley Blackwell, vol. 69(3), pages 288-308, July.
    3. Andrea Renzetti, 2023. "Theory coherent shrinkage of Time-Varying Parameters in VARs," Papers 2311.11858, arXiv.org.
    4. Gregor Bäurle & Daniel Kaufmann, 2018. "Measuring Exchange Rate, Price, and Output Dynamics at the Effective Lower Bound," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(6), pages 1243-1266, December.

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    More about this item

    Keywords

    Density forecasting; No arbitrage; stochastic volatility; Term structure;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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