Bayesian inference for the hazard term structure with functional predictors using Bayesian predictive information criteria

Tomohiro Ando

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A Bayesian method for estimation of a hazard term structure is presented in a functional data analysis framework. The hazard terms structure is designed to include the effects of changes in economic conditions, as well as trends in stock prices and accounting variables from financial statements. The hazard function contains time-varying parameters that are modelled using splines. To estimate the model parameters, a Markov-chain Monte Carlo sampling algorithm is developed. The Bayesian predictive information criterion is employed to assess the default predictive power of the estimated model. The method is then applied to a Japanese firm's default data listed on the Japanese Stock Exchange. The results demonstrate that the proposed method performs well.

Original languageEnglish
Pages (from-to)1925-1939
Number of pages15
JournalComputational Statistics and Data Analysis
Volume53
Issue number6
DOIs
Publication statusPublished - 2009 Apr 15

Fingerprint

Term Structure
Information Criterion
Bayesian inference
Hazard
Predictors
Hazards
Functional Data Analysis
Time-varying Parameters
Monte Carlo Sampling
Hazard Function
Stock Prices
Bayesian Methods
Markov Chain Monte Carlo
Spline
Economics
Splines
Markov processes
Model
Estimate
Sampling

ASJC Scopus subject areas

  • Computational Mathematics
  • Computational Theory and Mathematics
  • Statistics and Probability
  • Applied Mathematics

Cite this

Bayesian inference for the hazard term structure with functional predictors using Bayesian predictive information criteria. / Ando, Tomohiro.

In: Computational Statistics and Data Analysis, Vol. 53, No. 6, 15.04.2009, p. 1925-1939.

Research output: Contribution to journalArticle

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