Extreme-Dimensional Sparse Multivariate Stochastic Volatility Fashions
Authors: Benjamin Poignard, Manabu Asai
Abstract: Although multivariate stochastic volatility fashions usually produce additional appropriate forecasts compared with the MGARCH fashions, their estimation strategies akin to Bayesian MCMC typically endure from the curse of dimensionality. We recommend a fast and setting pleasant estimation technique for MSV primarily based totally on a penalized OLS framework. Specifying the MSV model as a multivariate state home model, we function out a two-step penalized course of. We provide the asymptotic properties of the two-step estimator and the oracle property of the first-step estimator when the number of parameters diverges. The performances of our methodology are illustrated by the use of simulations and financial info