Bayesian Stochastic Volatility Models
Bayesian Stochastic Volatility Models is a class of latent variable models in econometrics that incorporates Bayesian inference to estimate time-varying volatility through a stochastic process, distinguishing itself by explicitly modeling the volatility as a latent state governed by a random dynamic evolution and using prior distributions to parameterize uncertainty in the volatility process.
118
Publications
4.7K
Citations
187
Authors
171
Institutions
Publications per year
2017–2026
41
Parents
118
Bayesian Vector Autoregressions with Stochastic Volatility
Harald Uhlig · Econometrica · 1997 · 169 citations
Business, Finance, Econometrics +16
1–5 of 118
187
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Hong Kong University of Science and Technology, Hong Kong SAR China
4 publications · 169 citations
4
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4
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1–5 of 187
171
University of Kent
Canterbury, United Kingdom
5 h-index
7 publications
186 citations

UNSW Sydney
Sydney, Australia
5 h-index
6 publications
169 citations

Universidade Estadual de Campinas (UNICAMP)
Campinas, Brazil
5 h-index
5 publications
206 citations
Federal Reserve Bank of Cleveland
Cleveland, United States
4 h-index
3 publications
451 citations

The University of Sydney
Sydney, Australia
4 h-index
9 publications
290 citations

Bocconi University
Milan, Italy
4 h-index
3 publications
451 citations

Federal Reserve Bank of Atlanta
Georgiana, United States
3 h-index
2 publications
156 citations

National Bureau of Economic Research
Cambridge, United States
3 h-index
5 publications
90 citations
1–8 of 171
Repository · 10 publications · 289 citations
10
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JBES · 8 publications · 366 citations
8
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8 publications · 260 citations
8
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5 publications · 1K citations
6
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CSDA · 5 publications · 250 citations
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