Nearly exact Bayesian estimation of non-linear no-arbitrage term structure models

Marcello Pericoli and Marco Taboga (marco.taboga@bancaditalia.it)
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Marco Taboga: Bank of Italy

No 1189, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: We propose a general method for the Bayesian estimation of nonlinear no-arbitrage term structure models. The main innovations we introduce are: 1) a computationally efficient method, based on deep learning techniques, for approximating no-arbitrage model-implied bond yields to any desired degree of accuracy; and 2) computational graph optimizations for accelerating the MCMC sampling of the model parameters and of the unobservable state variables that drive the short-term interest rate. We apply the proposed techniques for estimating a shadow rate model with a time-varying lower bound, in which the shadow rate can be driven by both spanned unobservable factors and unspanned macroeconomic factors.

Keywords: yield curve; shadow rate; deep learning; artificial intelligence (search for similar items in EconPapers)
JEL-codes: C32 E43 G12 (search for similar items in EconPapers)
Date: 2018-09
New Economics Papers: this item is included in nep-big, nep-ecm, nep-ets, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Nearly Exact Bayesian Estimation of Non-linear No-Arbitrage Term-Structure Models* (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_1189_18

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