Analytical factor stochastic volatility modeling for portfolio allocation

This paper proposes a computationally efficient approach to estimation of factor stochastic volatility models using analytical formulae. Following the maximum likelihood principle there are obtained formulae for the evaluation of the parameters of the dynamic factor model, as well as for the parameters of the ingredient stochastic volatility processes. The approach uses the Hull-White stochastic volatility model to represent the common factors and the idiosyncratic factors. Empirical investigations show that this analytical factor stochastic volatility modeling generates plausible forecasts which are useful for portfolio selection.