A0679
Title: Dynamic sparsity in factor stochastic volatility models
Authors: Luis Gruber - University of Klagenfurt (Austria) [presenting]
Florian Huber - University of Salzburg (Austria)
Gregor Kastner - WU Vienna University of Economics and Business (Austria)
Abstract: Appropriately selecting the number of factors in a factor model is a challenging task, and even more so if the number of factors changes over time. A factor stochastic volatility (FSV) model is estimated through Markov chain Monte Carlo (MCMC) methods and then post-process the draws from the posterior to achieve sparsity in the factor loadings matrix. Recasting the FSV model as a homoskedastic factor model with time-varying loadings enables us to sparsify the loadings for each point in time and across MCMC draws. This enables backing out the posterior distribution of the number of factors over time. It is illustrated in simulations that the techniques accurately detect the true number of factors and apply the model to US stock market returns.
