scholarly journals The SR Approach: A New Estimation Method for Non-Linear and Non-Gaussian Dynamic Term Structure Models

2011 ◽  
Author(s):  
Martin M. Andreasen ◽  
Bent Jesper Christensen
Keyword(s):  
Term Structure ◽  
Estimation Method ◽  
Term Structure Models ◽  
Non Linear ◽  
Non Gaussian

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

2021 ◽  
Author(s):  
Marcello Pericoli ◽  
Marco Taboga
Keyword(s):  
Bayesian Estimation ◽  
Term Structure ◽  
Broad Class ◽  
Model Parameters ◽  
State Variables ◽  
Computationally Efficient ◽  
Macroeconomic Factors ◽  
No Arbitrage ◽  
Term Structure Models ◽  
Non Linear

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.

Approximation of Non-Linear Term Structure Models

2001 ◽  
Vol 8 (3) ◽  
pp. 44-51 ◽  
Author(s):  
Hideyuki Takamizawa ◽  
Isao Shoji
Keyword(s):  
Term Structure ◽  
Linear Term ◽  
Term Structure Models ◽  
Non Linear
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