Bayesian Analysis

Dynamic Financial Index Models: Modeling Conditional Dependencies via Graphs

Hao Wang, Craig Reeson, and Carlos M. Carvalho

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We discuss the development and application of dynamic graphical models for multivariate financial time series in the context of Financial Index Models. The use of graphs generalizes the independence residual variation assumption of index models with a more complex yet still parsimonious alternative. Working with the dynamic matrix-variate graphical model framework, we develop general time-varying index models that are analytically tractable. In terms of methodology, we carefully explore strategies to deal with graph uncertainty and discuss the implementation of a novel computational tool to sequentially learn about the conditional independence relationships defining the model. Additionally, motivated by our applied context, we extend the DGM framework to accommodate random regressors. Finally, in a case study involving 100 stocks, we show that our proposed methodology is able to generate improvements in covariance forecasting and portfolio optimization problems.

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Bayesian Anal., Volume 6, Number 4 (2011), 639-664.

First available in Project Euclid: 13 June 2012

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Bayesian forecasting Covariance matrix forecasting Dynamic matrix-variate graphical models Index models Factor models Gaussian graphical models Portfolio selection


Wang, Hao; Reeson, Craig; Carvalho, Carlos M. Dynamic Financial Index Models: Modeling Conditional Dependencies via Graphs. Bayesian Anal. 6 (2011), no. 4, 639--664. doi:10.1214/11-BA624.

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