Application of MCMC algorithm in interest rate modeling

Xiaoxia Feng; Dejun Xie

Application of MCMC algorithm in interest rate modeling

Xiaoxia Feng^*, Dejun Xie

^*Corresponding author for this work

Department of Financial and Actuarial Mathematics

Xi'an Jiaotong University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

Interest rate modeling is a challenging but important problem in financial econometrics. This work is concerned with the parameter estimation of the short term interest models. In light of a recent development in Markov Chain Monte Carlo simulation techniques based on Gibbs sampling, numerical experimentations are carried out for finding an effective and convergent Beyesian estimation scheme. The optimal degree of data augmentation is probed on basis of sensitivity analysis in searching of maximum A-posteriori probability density. Our method is calibrated with both US Treasury bills and basic loan rates from Japanese market.

Original language	English
Title of host publication	IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011
Pages	1495-1500
Number of pages	6
Publication status	Published - 2011
Event	International MultiConference of Engineers and Computer Scientists 2011, IMECS 2011 - Kowloon, Hong Kong Duration: 16 Mar 2011 → 18 Mar 2011

Publication series

Name	IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011
Volume	2

Conference

Conference	International MultiConference of Engineers and Computer Scientists 2011, IMECS 2011
Country/Territory	Hong Kong
City	Kowloon
Period	16/03/11 → 18/03/11

Keywords

Bayesian estimation
Data augumenation
Gibbs sampler
MAP estimation
MCMC method

Cite this

@inproceedings{a8b08cf96c164cd4bb3344fb47c35fea,

title = "Application of MCMC algorithm in interest rate modeling",

abstract = "Interest rate modeling is a challenging but important problem in financial econometrics. This work is concerned with the parameter estimation of the short term interest models. In light of a recent development in Markov Chain Monte Carlo simulation techniques based on Gibbs sampling, numerical experimentations are carried out for finding an effective and convergent Beyesian estimation scheme. The optimal degree of data augmentation is probed on basis of sensitivity analysis in searching of maximum A-posteriori probability density. Our method is calibrated with both US Treasury bills and basic loan rates from Japanese market.",

keywords = "Bayesian estimation, Data augumenation, Gibbs sampler, MAP estimation, MCMC method",

author = "Xiaoxia Feng and Dejun Xie",

year = "2011",

language = "English",

isbn = "9789881925121",

series = "IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011",

pages = "1495--1500",

booktitle = "IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011",

note = "International MultiConference of Engineers and Computer Scientists 2011, IMECS 2011 ; Conference date: 16-03-2011 Through 18-03-2011",

}

Feng, X & Xie, D 2011, Application of MCMC algorithm in interest rate modeling. in IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011. IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011, vol. 2, pp. 1495-1500, International MultiConference of Engineers and Computer Scientists 2011, IMECS 2011, Kowloon, Hong Kong, 16/03/11.

Application of MCMC algorithm in interest rate modeling. / Feng, Xiaoxia; Xie, Dejun.
IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011. 2011. p. 1495-1500 (IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011; Vol. 2).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Xie, Dejun

PY - 2011

Y1 - 2011

N2 - Interest rate modeling is a challenging but important problem in financial econometrics. This work is concerned with the parameter estimation of the short term interest models. In light of a recent development in Markov Chain Monte Carlo simulation techniques based on Gibbs sampling, numerical experimentations are carried out for finding an effective and convergent Beyesian estimation scheme. The optimal degree of data augmentation is probed on basis of sensitivity analysis in searching of maximum A-posteriori probability density. Our method is calibrated with both US Treasury bills and basic loan rates from Japanese market.

AB - Interest rate modeling is a challenging but important problem in financial econometrics. This work is concerned with the parameter estimation of the short term interest models. In light of a recent development in Markov Chain Monte Carlo simulation techniques based on Gibbs sampling, numerical experimentations are carried out for finding an effective and convergent Beyesian estimation scheme. The optimal degree of data augmentation is probed on basis of sensitivity analysis in searching of maximum A-posteriori probability density. Our method is calibrated with both US Treasury bills and basic loan rates from Japanese market.

KW - Bayesian estimation

KW - Data augumenation

KW - Gibbs sampler

KW - MAP estimation

KW - MCMC method

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M3 - Conference Proceeding

AN - SCOPUS:79960612376

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T3 - IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011

SP - 1495

EP - 1500

BT - IMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011

T2 - International MultiConference of Engineers and Computer Scientists 2011, IMECS 2011

Y2 - 16 March 2011 through 18 March 2011

ER -

Application of MCMC algorithm in interest rate modeling

Abstract

Publication series

Conference

Keywords

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