Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 오만숙 | * |
dc.contributor.author | 신동완 | * |
dc.date.accessioned | 2018-11-22T16:30:34Z | - |
dc.date.available | 2018-11-22T16:30:34Z | - |
dc.date.issued | 2017 | * |
dc.identifier.issn | 2287-7843 | * |
dc.identifier.other | OAK-23826 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/246974 | - |
dc.description.abstract | Volatility plays a crucial role in theory and applications of asset pricing, optimal portfolio allocation, and risk management. This paper proposes a combined model of autoregressive moving average (ARFIMA), generalized autoregressive conditional heteroscedasticity (GRACH), and skewed-t error distribution to accommodate important features of volatility data; long memory, heteroscedasticity, and asymmetric error distribution. A fully Bayesian approach is proposed to estimate the parameters of the model simultaneously, which yields parameter estimates satisfying necessary constraints in the model. The approach can be easily implemented using a free and user-friendly software JAGS to generate Markov chain Monte Carlo samples from the joint posterior distribution of the parameters. The method is illustrated by using a daily volatility index from Chicago Board Options Exchange (CBOE). JAGS codes for model specification is provided in the Appendix. © 2017 The Korean Statistical Society, and Korean International Statistical Society. | * |
dc.description.sponsorship | Ministry of Education, Science and Technology | * |
dc.language | English | * |
dc.publisher | Korean Statistical Society | * |
dc.subject | ARFIMA | * |
dc.subject | Bayesian | * |
dc.subject | GARCH | * |
dc.subject | JAGS | * |
dc.subject | Markov chain Monte Carlo | * |
dc.subject | Skewed-t | * |
dc.title | Bayesian analysis of financial volatilities addressing long-memory, conditional heteroscedasticity and skewed error distribution | * |
dc.type | Article | * |
dc.relation.issue | 5 | * |
dc.relation.volume | 24 | * |
dc.relation.index | SCOPUS | * |
dc.relation.index | KCI | * |
dc.relation.startpage | 507 | * |
dc.relation.lastpage | 518 | * |
dc.relation.journaltitle | Communications for Statistical Applications and Methods | * |
dc.identifier.doi | 10.5351/CSAM.2017.24.5.507 | * |
dc.identifier.scopusid | 2-s2.0-85044066457 | * |
dc.author.google | Oh R. | * |
dc.author.google | Shin D.W. | * |
dc.author.google | Oh M.-S. | * |
dc.contributor.scopusid | 오만숙(7201600334) | * |
dc.contributor.scopusid | 신동완(7403352539) | * |
dc.date.modifydate | 20240116115756 | * |