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• W1977776321 abstract "The Bayesian analysis of a state-space model includes computing the posterior distribution of the system’s parameters as well as its latent states. When the latent states wander around Rn there are several well-known modeling components and computational tools that may be profitably combined to achieve this task. When the latent states are constrained to a strict subset of Rn these models and tools are either impaired or break down completely. State-space models whose latent states are covariance matrices arise in finance and exemplify the challenge of devising tractable models in the constrained setting. To that end, we present a state-space model whose observations and latent states take values on the manifold of symmetric positive-definite matrices and for which one may easily compute the posterior distribution of the latent states and the system’s parameters as well as filtered distributions and one-step ahead predictions. Employing the model within the context of finance, we show how one can use realized covariance matrices as data to predict latent time-varying covariance matrices. This approach out-performs factor stochastic volatility." @default.
• W1977776321 created "2016-06-24" @default.
• W1977776321 creator A5008199122 @default.
• W1977776321 creator A5045139315 @default.
• W1977776321 date "2014-12-01" @default.
• W1977776321 modified "2023-10-10" @default.
• W1977776321 title "A Tractable State-Space Model for Symmetric Positive-Definite Matrices" @default.
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• W1977776321 doi "https://doi.org/10.1214/14-ba888" @default.
• W1977776321 hasPublicationYear "2014" @default.
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