# Skew selection for factor stochastic volatility models

## TL;DR

This paper introduces factor stochastic volatility models with skew error distributions, employing Bayesian sparsity to efficiently model skewness in high-dimensional financial data, improving prediction and portfolio performance.

## Contribution

It develops a novel Bayesian sparse skew structure for factor stochastic volatility models using the generalized hyperbolic skew t-distribution.

## Key findings

- Skewness is significant in common-factor processes.
- Sparse skew structure enhances predictive accuracy.
- Improves portfolio performance.

## Abstract

This paper proposes factor stochastic volatility models with skew error distributions. The generalized hyperbolic skew t-distribution is employed for common-factor processes and idiosyncratic shocks. Using a Bayesian sparsity modeling strategy for the skewness parameter provides a parsimonious skew structure for possibly high-dimensional stochastic volatility models. Analyses of daily stock returns are provided. Empirical results show that the skewness is important for common-factor processes but less for idiosyncratic shocks. The sparse skew structure improves prediction and portfolio performance.