Abstract
A Multivariate Latent Stochastic Volatility Factor Model is introduced for the estimation of volatility and optimal allocation of stocks portfolio in a Markowitz type portfolio. Returns on a set of 5 banks among the best capitalized banks’ stocks traded on the Italian stock market (BIT) between 1 January 1986 and 31 August 2011 are modeled. Computational complexities arising in the estimation step are dealt by simulation-based methods, introducing a Griddy Gibbs sampler. The association structure among time-series is captured via a factor model, which reduces the computational burden required in the estimation step.
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