Bayesian collapsed Gibbs sampling for a stochastic volatility model with a Dirichlet process mixture
Summary 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 gener...
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| Veröffentlicht in: | Journal of applied econometrics (Chichester, England) England), 2024-06, Vol.39 (4), p.697-704 |
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| container_title | Journal of applied econometrics (Chichester, England) |
| container_volume | 39 |
| creator | Wu, Frank C. Z. |
| description | Summary
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. |
| doi_str_mv | 10.1002/jae.3040 |
| format | Article |
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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.</description><identifier>ISSN: 0883-7252</identifier><identifier>EISSN: 1099-1255</identifier><identifier>DOI: 10.1002/jae.3040</identifier><language>eng</language><publisher>Chichester: Wiley Periodicals Inc</publisher><subject>Algorithms ; Bayesian analysis ; Bayesian nonparametrics ; collapsed Gibbs sampling ; COVID-19 ; Datasets ; Dirichlet problem ; Dirichlet process mixture prior ; Econometrics ; Markov chain Monte Carlo ; mixture models ; Mixtures ; Pandemics ; Sampling ; Sampling methods ; Stochastic models ; stochastic volatility ; Volatility</subject><ispartof>Journal of applied econometrics (Chichester, England), 2024-06, Vol.39 (4), p.697-704</ispartof><rights>2024 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3440-13079bc2d1f2abc617dc693840faaaa39dab6ab70026a161ec4d74dabc180c743</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjae.3040$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjae.3040$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids></links><search><creatorcontrib>Wu, Frank C. Z.</creatorcontrib><title>Bayesian collapsed Gibbs sampling for a stochastic volatility model with a Dirichlet process mixture</title><title>Journal of applied econometrics (Chichester, England)</title><description>Summary
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.</description><subject>Algorithms</subject><subject>Bayesian analysis</subject><subject>Bayesian nonparametrics</subject><subject>collapsed Gibbs sampling</subject><subject>COVID-19</subject><subject>Datasets</subject><subject>Dirichlet problem</subject><subject>Dirichlet process mixture prior</subject><subject>Econometrics</subject><subject>Markov chain Monte Carlo</subject><subject>mixture models</subject><subject>Mixtures</subject><subject>Pandemics</subject><subject>Sampling</subject><subject>Sampling methods</subject><subject>Stochastic models</subject><subject>stochastic volatility</subject><subject>Volatility</subject><issn>0883-7252</issn><issn>1099-1255</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp10EFLwzAUB_AgCs4p-BECXrx0vjRt0x6nzqkMvOg5pGnqMtKmJpmz397MCZ7M5UH48X-8P0KXBGYEIL3ZCDWjkMERmhCoqoSkeX6MJlCWNGFpnp6iM-83AFAAsAlqbsWovBY9ltYYMXjV4KWua4-96Aaj-3fcWocF9sHKtfBBS_xpjQja6DDizjbK4J0O60jutdNybVTAg7NSeY87_RW2Tp2jk1YYry5-5xS9PSxe7x6T1cvy6W6-SiTNMkgIBVbVMm1Im4paFoQ1sqhomUEr4qNVI-pC1CyeWQhSECWzhmXxU5ISJMvoFF0dcuP-j63ygW_s1vVxJadQMBJvphDV9UFJZ713quWD051wIyfA9x3y2CHfdxgpPlAlba_9HyyrlOWEsDKS5EB22qjx3yj-PF_8RH4DCwV9ww</recordid><startdate>202406</startdate><enddate>202406</enddate><creator>Wu, Frank C. Z.</creator><general>Wiley Periodicals Inc</general><scope>OQ6</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8BJ</scope><scope>FQK</scope><scope>JBE</scope><scope>JQ2</scope></search><sort><creationdate>202406</creationdate><title>Bayesian collapsed Gibbs sampling for a stochastic volatility model with a Dirichlet process mixture</title><author>Wu, Frank C. Z.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3440-13079bc2d1f2abc617dc693840faaaa39dab6ab70026a161ec4d74dabc180c743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Bayesian analysis</topic><topic>Bayesian nonparametrics</topic><topic>collapsed Gibbs sampling</topic><topic>COVID-19</topic><topic>Datasets</topic><topic>Dirichlet problem</topic><topic>Dirichlet process mixture prior</topic><topic>Econometrics</topic><topic>Markov chain Monte Carlo</topic><topic>mixture models</topic><topic>Mixtures</topic><topic>Pandemics</topic><topic>Sampling</topic><topic>Sampling methods</topic><topic>Stochastic models</topic><topic>stochastic volatility</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wu, Frank C. Z.</creatorcontrib><collection>ECONIS</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><jtitle>Journal of applied econometrics (Chichester, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Frank C. Z.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bayesian collapsed Gibbs sampling for a stochastic volatility model with a Dirichlet process mixture</atitle><jtitle>Journal of applied econometrics (Chichester, England)</jtitle><date>2024-06</date><risdate>2024</risdate><volume>39</volume><issue>4</issue><spage>697</spage><epage>704</epage><pages>697-704</pages><issn>0883-7252</issn><eissn>1099-1255</eissn><abstract>Summary
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.</abstract><cop>Chichester</cop><pub>Wiley Periodicals Inc</pub><doi>10.1002/jae.3040</doi><tpages>8</tpages></addata></record> |
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| ispartof | Journal of applied econometrics (Chichester, England), 2024-06, Vol.39 (4), p.697-704 |
| issn | 0883-7252 1099-1255 |
| language | eng |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms Bayesian analysis Bayesian nonparametrics collapsed Gibbs sampling COVID-19 Datasets Dirichlet problem Dirichlet process mixture prior Econometrics Markov chain Monte Carlo mixture models Mixtures Pandemics Sampling Sampling methods Stochastic models stochastic volatility Volatility |
| title | Bayesian collapsed Gibbs sampling for a stochastic volatility model with a Dirichlet process mixture |
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