Nearly Exact Bayesian Estimation of Non-linear No-Arbitrage Term-Structure Models*

Pricing the Term Structure with Linear Regressions

Marcello Pericoli and Marco Taboga

Journal of Financial Econometrics, 2022, vol. 20, issue 5, 807-838

Abstract: We propose a general method for the Bayesian estimation of a very broad class of non-linear no-arbitrage term-structure models. The main innovation we introduce is a computationally efficient method, based on deep learning techniques, for approximating no-arbitrage model-implied bond yields to any desired degree of accuracy. Once the pricing function is approximated, the posterior distribution of model parameters and unobservable state variables can be estimated by standard Markov Chain Monte Carlo methods. As an illustrative example, we apply the proposed techniques to the estimation of a shadow-rate model with a time-varying lower bound and unspanned macroeconomic factors.

Keywords: approximation methods; deep learning; shadow rate models; yield curve (search for similar items in EconPapers)
JEL-codes: C32 E43 G12 (search for similar items in EconPapers)
Date: 2022
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Working Paper: Nearly exact Bayesian estimation of non-linear no-arbitrage term structure models (2018)
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