A Nonparametric Bayesian Approach to Term Structure Fitting
The article shows how the Bayesian approach to income adjustment can be implemented in a non-parametric structure with automatic smoothing obtained from data. It also briefly illustrates the benefits of this approach using real data.
The article uses an infinite-dimensional (functional space) approach to reverse problems. Numerical calculations are performed using the Markov-Monte Carlo chain algorithm with several settings to ensure good performance. The model clearly uses spreads between queries and sentences to account for observation errors and provides automatic smoothing based on them.
The non-parametric structure captures the complex forms of the zero-coupon curves of emerging markets. The Bayesian approach assesses the accuracy of estimates, which is crucial for some applications. Examples of valuation results are given for three different bond markets: liquid (German), medium liquid (Chinese) and illiquid (Russian).
The result shows that an infinite-dimensional Bayesian approach to evaluating the structure of the term is possible. Market practices can use this approach to better understand the timing of interest rates. For example, they could now supplement their non-parametric estimates of the timing structure with Bayesian confidence intervals to enable them to assess the statistical significance of their results.
The model does not require parameters to be set during the evaluation. It has its own parameters, but they must be selected during the model configuration.