{"title":"具有动态潜变量的ARCH-M模型的贝叶斯分析","authors":"Zefang Song , Xinyuan Song , Yuan Li","doi":"10.1016/j.ecosta.2021.10.001","DOIUrl":null,"url":null,"abstract":"<div><p>A time-varying coefficient ARCH-in-mean (ARCH-M) model with a dynamic latent variable that follows an AR process<span> is considered. The joint model extends the existing ARCH-M model by considering a dynamic structure of latent variable for examining a latent effect on the time-varying risk–return relationship. A Bayesian<span> approach coped with Markov Chain Monte Carlo algorithm is developed to perform the joint estimation of model parameters and the latent variable. Simulation results show that the proposed inference procedure performs satisfactorily. An application of the proposed method to a financial study of the Chinese stock market is presented.</span></span></p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"28 ","pages":"Pages 47-62"},"PeriodicalIF":2.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Bayesian Analysis of ARCH-M model with a dynamic latent variable\",\"authors\":\"Zefang Song , Xinyuan Song , Yuan Li\",\"doi\":\"10.1016/j.ecosta.2021.10.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A time-varying coefficient ARCH-in-mean (ARCH-M) model with a dynamic latent variable that follows an AR process<span> is considered. The joint model extends the existing ARCH-M model by considering a dynamic structure of latent variable for examining a latent effect on the time-varying risk–return relationship. A Bayesian<span> approach coped with Markov Chain Monte Carlo algorithm is developed to perform the joint estimation of model parameters and the latent variable. Simulation results show that the proposed inference procedure performs satisfactorily. An application of the proposed method to a financial study of the Chinese stock market is presented.</span></span></p></div>\",\"PeriodicalId\":54125,\"journal\":{\"name\":\"Econometrics and Statistics\",\"volume\":\"28 \",\"pages\":\"Pages 47-62\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2023-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Econometrics and Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2452306221001167\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometrics and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2452306221001167","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
Bayesian Analysis of ARCH-M model with a dynamic latent variable
A time-varying coefficient ARCH-in-mean (ARCH-M) model with a dynamic latent variable that follows an AR process is considered. The joint model extends the existing ARCH-M model by considering a dynamic structure of latent variable for examining a latent effect on the time-varying risk–return relationship. A Bayesian approach coped with Markov Chain Monte Carlo algorithm is developed to perform the joint estimation of model parameters and the latent variable. Simulation results show that the proposed inference procedure performs satisfactorily. An application of the proposed method to a financial study of the Chinese stock market is presented.
期刊介绍:
Econometrics and Statistics is the official journal of the networks Computational and Financial Econometrics and Computational and Methodological Statistics. It publishes research papers in all aspects of econometrics and statistics and comprises of the two sections Part A: Econometrics and Part B: Statistics.