A0374
Title: Infinite sparse factor stochastic volatility model
Authors: Martina Zaharieva - CUNEF Universidad (Spain) [presenting]
Abstract: A sparse factor multivariate stochastic volatility model is proposed, in which the sparsity of the loading matrix is achieved by introducing the Indian buffet process, a Bayesian nonparametric, prior to defining a distribution over infinite binary matrices. The benefit of the infinite-dimensional latent process is twofold. First, inducing sparsity prior reduces the dimensionality of the problem, and second, the number of active factors is determined by the data itself and a priori set to infinity. Both the diagonal elements of the covariance matrix of the idiosyncratic term and the active factors follow univariate stochastic volatility processes. Each latent volatility is sampled independently and in parallel by means of a particle filtering and smoothing technique based on a simulated likelihood. The model is applied to a cross-section of five international stock market indices.
