Nearly exact Bayesian estimation of non-linear no-arbitrage term structure models
Marcello Pericoli () and
Marco Taboga ()
No 1189, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area
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)
New Economics Papers: this item is included in nep-big, nep-ecm, nep-ets, nep-mac and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_1189_18
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