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Title: High-dimensional sparse multivariate stochastic volatility models Authors:  Benjamin Poignard - Osaka University (Japan) [presenting]
Manabu Asai - Soka University (Japan)
Abstract: Although multivariate stochastic volatility models usually produce more accurate forecasts compared to multivariate GARCH models, their estimation techniques such as Bayesian Markov Chain Monte Carlo are computationally demanding and thus suffer from the curse of dimensionality. We propose a fast estimation approach for MSV models based on a penalised ordinary least squares framework. We propose a two-step penalised procedure for estimating the latter using a broad range of potentially non-convex penalty functions. This two-step procedure relies on OLS based loss functions and thus easily accommodates high-dimensional vectors. We provide the large sample properties of the two-step estimator together with the oracle property of the first step estimator with a diverging number of parameters. The empirical performances of our method are illustrated through in-sample simulations and out-of-sample forecasts.