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The use of factor stochastic volatility models requires choosing the numberof latent factors used to describe the dynamics of the financial returnsprocess; however, empirical evidence suggests that the number and makeup ofpertinent factors is time-varying and economically situational. We present anovel factor stochastic volatility model that allows for random subsets ofassets to have their members experience non-market-wide panics. Theseparticipating assets will experience an increase in their variances andwithin-group covariances. We also give an estimation algorithm for this modelthat takes advantage of recent results on Particle Markov chain Monte Carlotechniques.

A Factor Stochastic Volatility Model with Markov-Switching Panic Regimes