A Bayesian inference for time series via copula-based Markov chain models

Li Hsien Sun; Chang Shang Lee; Takeshi Emura

doi:10.1080/03610918.2018.1529241

A Bayesian inference for time series via copula-based Markov chain models

Li Hsien Sun^*, Chang Shang Lee, Takeshi Emura

^*Corresponding author for this work

Department of Information Management

National Central University

Research output: Contribution to journal › Journal Article › peer-review

7 Scopus citations

Abstract

This paper studies the nonstandardized Student’s t-distribution for fitting serially correlated observations where serial dependence is described by the copula-based Markov chain. Due to the computational difficulty of obtaining maximum likelihood estimates, alternatively, we develop Bayesian inference using the empirical Bayes method through the resampling procedure. We provide the simulations to examine the performance and also analyze the stock price data in empirical studies for illustration.

Original language	English
Pages (from-to)	2897-2913
Number of pages	17
Journal	Communications in Statistics: Simulation and Computation
Volume	49
Issue number	11
DOIs	https://doi.org/10.1080/03610918.2018.1529241
State	Published - 2020

Bibliographical note

Publisher Copyright:
© 2018 Taylor & Francis Group, LLC.

Keywords

Bayesian inference
Clayton copula
Markov chain Monte Carlo
Metropolis-Hastings algorithm
Nonstandardized Student’s t-distribution

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10.1080/03610918.2018.1529241

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title = "A Bayesian inference for time series via copula-based Markov chain models",

abstract = "This paper studies the nonstandardized Student{\textquoteright}s t-distribution for fitting serially correlated observations where serial dependence is described by the copula-based Markov chain. Due to the computational difficulty of obtaining maximum likelihood estimates, alternatively, we develop Bayesian inference using the empirical Bayes method through the resampling procedure. We provide the simulations to examine the performance and also analyze the stock price data in empirical studies for illustration.",

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AU - Emura, Takeshi

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AB - This paper studies the nonstandardized Student’s t-distribution for fitting serially correlated observations where serial dependence is described by the copula-based Markov chain. Due to the computational difficulty of obtaining maximum likelihood estimates, alternatively, we develop Bayesian inference using the empirical Bayes method through the resampling procedure. We provide the simulations to examine the performance and also analyze the stock price data in empirical studies for illustration.

KW - Bayesian inference

KW - Clayton copula

KW - Markov chain Monte Carlo

KW - Metropolis-Hastings algorithm

KW - Nonstandardized Student’s t-distribution

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A Bayesian inference for time series via copula-based Markov chain models

Abstract

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Keywords

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