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• W2046357557 abstract "New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to construct a Markov-chain simulation tool. Simulations from this Markov chain coverage in distribution to draws from the posterior distribution enabling exact finite-sample inference. The exact solution to the filtering/smoothing problem of inferring about the unobserved variance states is a by-product of our Markov-chain method. In addition, multistep-ahead predictive densities can be constructed that reflect both inherent model variability and parameter uncertainty. We illustrate our method by analyzing both daily and weekly data on stock returns and exchange rates. Sampling experiments are conducted to compare the performance of Bayes estimators to method of moments and quasi-maximum likelihood estimators proposed in the literature. In both parameter estimation and filtering, the Bayes estimators outperform these other approaches." @default.
• W2046357557 created "2016-06-24" @default.
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• W2046357557 date "2002-01-01" @default.
• W2046357557 modified "2023-10-16" @default.
• W2046357557 title "Bayesian Analysis of Stochastic Volatility Models" @default.
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• W2046357557 doi "https://doi.org/10.1198/073500102753410408" @default.
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