Statistical Methods in Finance 2023

                                

Abstract

Modeling Multivariate Positive-Valued Financial Time Series

By Nalini Ravishanker
University of Connecticut, USA

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)

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