Modeling Multivariate Positive-Valued Time Series with Financial Applications
Conference
64th ISI World Statistics Congress
Format: IPS Abstract
Session: IPS 433 - High-Dimensional Financial Time Series
Tuesday 18 July 10 a.m. - noon (Canada/Eastern)
Abstract
We describe an approach for Bayesian analysis of vector positive-valued time series, with application to analyzing financial data streams. The approach consists of a flexible level correlated model (LCM) framework for building hierarchical models. The LCM framework allows us to combine univariate gamma distributions for each of the positive-valued component responses, while accounting for association among the components via an unobserved random vector. We employ the integrated nested Laplace approximation (INLA) for fast approximate Bayesian modeling via the R-INLA package, building custom functions to handle a latent vector AR model. We use the proposed method to model interdependencies between intraday volatility measures from several stock indexes. This is joint work with Chiranjit Dutta (eBay) and Sumanta Basu (Cornell University).