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A new robust stochastic volatility (SV) model having Student-t marginals isproposed. Our process is defined through a linear normal regression modeldriven by a latent gamma process that controls temporal dependence. This gammaprocess is strategically chosen to enable us to find an explicit expression forthe pairwise joint density function of the Student-t response process. Withthis at hand, we propose a composite likelihood (CL) based inference for ourmodel, which can be straightforwardly implemented with a low computationalcost. This is a remarkable feature of our Student-t SV process over existing SVmodels in the literature that involve computationally heavy algorithms forestimating parameters. Aiming at a precise estimation of the parameters relatedto the latent process, we propose a CL Expectation-Maximization algorithm anddiscuss a bootstrap approach to obtain standard errors. The finite-sampleperformance of our composite likelihood methods is assessed through Monte Carlosimulations. The methodology is motivated by an empirical application in thefinancial market. We analyze the relationship, across multiple time periods,between various US sector Exchange-Traded Funds returns and individualcompanies' stock price returns based on our novel Student-t model. Thisrelationship is further utilized in selecting optimal financial portfolios.

Student-t Stochastic Volatility Model With Composite Likelihood EM-Algorithm