Sparsity modelling in large-scale dynamic models for portfolio analysis
Duration: 1 hour 8 mins 12 secs
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| Description: |
West, M (Duke)
Wednesday 09 January 2008, 14:00-15:00 Contemporary Frontiers in High-Dimensional Statistical Data Analysis |
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| Created: | 2008-01-21 12:58 | ||
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| Collection: | Statistical Theory and Methods for Complex, High-Dimensional Data | ||
| Publisher: | Isaac Newton Institute | ||
| Copyright: | West, M | ||
| Language: | eng (English) | ||
| Distribution: |
World
(downloadable)
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| Credits: |
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| Explicit content: | No | ||
| Aspect Ratio: | 4:3 | ||
| Screencast: | No | ||
| Bumper: | UCS Default | ||
| Trailer: | UCS Default | ||
| Abstract: | I will discuss some of our recent work in dynamic modelling for multivariate time series that combines stochastic volatility and graphical modelling ideas. I will describe the modelling ideas and resulting matrix-variate, dynamic graphical models, and aspects of Bayesian methodology and computation for model fitting and structure search. Practical implications of the framework when applied to financial time series for predictive portfolio analysis will highlight some of the reasons for interest in sparsely structured, conditional independence models of volatility matrices. |
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