Autocorrelation plots for the sampled values of the parameters {w i , a i , b i } and {a c , b c } of the DCC model estimated via MCMC in the simulated four dimensional example. . . . . . . 4.14 Ergodic Means for parameters {φ i },{σ iη },{h oi } and {a c , b c } of the DCC-SV model estimated via MCMC in the simulated four dimensional example.
We introduce a multivariate stochastic volatility model for asset returns that imposes no restrictions to the structure of the volatility matrix and treats all its elements as functions of latent stochastic processes.When the number of assets is prohibitively large, we propose a factor multivariate stochastic volatility model in which the variances and correlations of the factors evolve stochastically over time. Inference is achieved via a carefully designed feasible and scalable Markov chain Monte Carlo algorithm that combines two computationally important ingredients: it utilizes invariant to the prior Metropolis proposal densities for simultaneously updating all latent paths and has quadratic, rather than cubic, computational
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