Dynamic Skewness in Stochastic Volatility Models: A Penalized Prior Approach

TL;DR

This paper introduces a flexible stochastic volatility model with dynamic skewness and heavy tails, employing penalized priors for robustness, and demonstrates improved performance through simulation and cryptocurrency data analysis.

Contribution

It develops a novel dynamic skewness stochastic volatility model within the SMSN family using penalized priors, enhancing robustness and flexibility in financial time series modeling.

Findings

Penalized priors outperform classical priors in simulations.

Models with heavy tails and dynamic skewness fit cryptocurrency returns better.

Bayesian estimation via Hamiltonian Monte Carlo is effective.

Abstract

Financial time series often exhibit skewness and heavy tails, making it essential to use models that incorporate these characteristics to ensure greater reliability in the results. Furthermore, allowing temporal variation in the skewness parameter can bring significant gains in the analysis of this type of series. However, for more robustness, it is crucial to develop models that balance flexibility and parsimony. In this paper, we propose dynamic skewness stochastic volatility models in the SMSN family (DynSSV-SMSN), using priors that penalize model complexity. Parameter estimation was carried out using the Hamiltonian Monte Carlo (HMC) method via the \texttt{RStan} package. Simulation results demonstrated that penalizing priors present superior performance in several scenarios compared to the classical choices. In the empirical application to returns of cryptocurrencies, models with heavy tails and dynamic skewness provided a better fit to the data according to the DIC, WAIC, and LOO-CV information criteria.