Leverage, asymmetry and heavy tails in the high-dimensional factor stochastic volatility model
Mengheng Li () and
Additional contact information
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
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
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:uts:ecowps:49
Access Statistics for this paper
More papers in Working Paper Series from Economics Discipline Group, UTS Business School, University of Technology, Sydney PO Box 123, Broadway, NSW 2007, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Duncan Ford ().