  The use of factor stochastic volatility models requires choosing the number
of latent factors used to describe the dynamics of the financial returns
process; however, empirical evidence suggests that the number and makeup of
pertinent factors is time-varying and economically situational. We present a
novel factor stochastic volatility model that allows for random subsets of
assets to have their members experience non-market-wide panics. These
participating assets will experience an increase in their variances and
within-group covariances. We also give an estimation algorithm for this model
that takes advantage of recent results on Particle Markov chain Monte Carlo
techniques.
