B1887
Title: Student-t stochastic volatility model with composite likelihood EM-algorithm
Authors: Raanju Sundararajan - Southern Methodist University (United States) [presenting]
Abstract: A new robust stochastic volatility (SV) model having Student-$t$ marginals is proposed. The process is defined through a linear normal regression model driven by a latent gamma process that controls temporal dependence. This gamma process is strategically chosen to enable us to find an explicit expression for the pairwise joint density function of the Student-$t$ response process. With this at hand, a composite likelihood (CL) is proposed based on inference for the model, which can be straightforwardly implemented with a low computational cost. This is a remarkable feature of the Student-$t$ process over existing SV models in the literature that involve computationally heavy algorithms for estimating parameters. Aiming at a precise estimation of the parameters related to the latent process, a CL expectation-maximization algorithm is proposed and a bootstrap approach is discussed to obtain standard errors. The finite-sample performance of the composite likelihood methods is assessed through Monte Carlo simulations. The methodology is motivated by an empirical application in the financial market. The relationship is analyzed, across multiple time periods, between various US sector exchange-traded funds returns and individual companies' stock price returns based on the novel Student-$t$ model. This relationship is further utilized in selecting optimal financial portfolios.
