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Leverage, asymmetry and heavy tails in the high-dimensional factor stochastic volatility model

Mengheng Li () and Marcel Scharth
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Marcel Scharth: School of Economics,University of Sydney, Sydney, https://sydney.edu.au/arts/schools/school-of-economics.html

No 49, Working Paper Series from Economics Discipline Group, UTS Business School, University of Technology, Sydney

Abstract: We develop a flexible modeling and estimation framework for a high-dimensional factor stochastic volatility (SV) model. Our specification allows for leverage effects, asymmetry and heavy tails across all systematic and idiosyncratic components of the model. This framework accounts for well-documented features of univariate financial time series, while introducing a flexible dependence structure that incorporates tail dependence and asymmetries such as stronger correlations following downturns. We develop an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior simulation based on the particle Gibbs, ancestor sampling, and particle efficient importance sampling methods. We build computationally efficient model selection into our estimation framework to obtain parsimonious specifications in practice. We validate the performance of our proposed estimation method via extensive simulation studies for univariate and multivariate simulated datasets. An empirical study shows that the model outperforms other multivariate models in terms of value-at-risk evaluation and portfolio selection performance for a sample of US and Australian stocks.

Keywords: Generalised hyperbolic skew Student’s t-distribution; Metropolis-Hastings algorithm; Importance sampling; Particle filter; Particle Gibbs; State space model; Time-varying covariance matrix; Factor model (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 C55 G32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-ore and nep-rmg
Date: 2018-08-24
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