Nearly Exact Bayesian Estimation of Non-linear No-Arbitrage Term-Structure Models
Marcello Pericoli, Marco Taboga
Abstract
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.
Topics & Concepts
UnobservableMarkov chain Monte CarloYield curveAffine term structure modelTerm (time)Bayesian probabilityEconometricsArbitrageHeath–Jarrow–Morton frameworkInterest rateComputer scienceMathematicsStatisticsEconomicsFinanceVolatility (finance)PhysicsQuantum mechanicsMarkov Chains and Monte Carlo MethodsMonetary Policy and Economic ImpactNuclear reactor physics and engineering