{"title":"针对具有德里赫特过程混合物的随机波动模型的贝叶斯折叠吉布斯采样","authors":"Frank C. Z. Wu","doi":"10.1002/jae.3040","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>This paper replicates the results of the stochastic volatility–Dirichlet process mixture (SV-DPM) models proposed in Jensen and Maheu (2010) in both a narrow and a wide sense. By using a normal-Wishart prior and the collapsed Gibbs sampling method, our algorithm can be applied for more general settings, and it is more efficient for sampling the Dirichlet process mixture. For the stochastic volatility component, we adopt the method in Chan (2017) to further increase the overall efficiency of our algorithm. Using the same dataset, we obtain mixed results. Some of the results have significant differences. If we use recent time period dataset, which includes the COVID-19 pandemic period, the log market portfolio volatility seems to increase in terms of the number of clusters and size of magnitude.</p>\n </div>","PeriodicalId":48363,"journal":{"name":"Journal of Applied Econometrics","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian collapsed Gibbs sampling for a stochastic volatility model with a Dirichlet process mixture\",\"authors\":\"Frank C. Z. Wu\",\"doi\":\"10.1002/jae.3040\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>This paper replicates the results of the stochastic volatility–Dirichlet process mixture (SV-DPM) models proposed in Jensen and Maheu (2010) in both a narrow and a wide sense. By using a normal-Wishart prior and the collapsed Gibbs sampling method, our algorithm can be applied for more general settings, and it is more efficient for sampling the Dirichlet process mixture. For the stochastic volatility component, we adopt the method in Chan (2017) to further increase the overall efficiency of our algorithm. Using the same dataset, we obtain mixed results. Some of the results have significant differences. If we use recent time period dataset, which includes the COVID-19 pandemic period, the log market portfolio volatility seems to increase in terms of the number of clusters and size of magnitude.</p>\\n </div>\",\"PeriodicalId\":48363,\"journal\":{\"name\":\"Journal of Applied Econometrics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Econometrics\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jae.3040\",\"RegionNum\":3,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Econometrics","FirstCategoryId":"96","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jae.3040","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
Bayesian collapsed Gibbs sampling for a stochastic volatility model with a Dirichlet process mixture
This paper replicates the results of the stochastic volatility–Dirichlet process mixture (SV-DPM) models proposed in Jensen and Maheu (2010) in both a narrow and a wide sense. By using a normal-Wishart prior and the collapsed Gibbs sampling method, our algorithm can be applied for more general settings, and it is more efficient for sampling the Dirichlet process mixture. For the stochastic volatility component, we adopt the method in Chan (2017) to further increase the overall efficiency of our algorithm. Using the same dataset, we obtain mixed results. Some of the results have significant differences. If we use recent time period dataset, which includes the COVID-19 pandemic period, the log market portfolio volatility seems to increase in terms of the number of clusters and size of magnitude.
期刊介绍：
The Journal of Applied Econometrics is an international journal published bi-monthly, plus 1 additional issue (total 7 issues). It aims to publish articles of high quality dealing with the application of existing as well as new econometric techniques to a wide variety of problems in economics and related subjects, covering topics in measurement, estimation, testing, forecasting, and policy analysis. The emphasis is on the careful and rigorous application of econometric techniques and the appropriate interpretation of the results. The economic content of the articles is stressed. A special feature of the Journal is its emphasis on the replicability of results by other researchers. To achieve this aim, authors are expected to make available a complete set of the data used as well as any specialised computer programs employed through a readily accessible medium, preferably in a machine-readable form. The use of microcomputers in applied research and transferability of data is emphasised. The Journal also features occasional sections of short papers re-evaluating previously published papers. The intention of the Journal of Applied Econometrics is to provide an outlet for innovative, quantitative research in economics which cuts across areas of specialisation, involves transferable techniques, and is easily replicable by other researchers. Contributions that introduce statistical methods that are applicable to a variety of economic problems are actively encouraged. The Journal also aims to publish review and survey articles that make recent developments in the field of theoretical and applied econometrics more readily accessible to applied economists in general.
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