Dynamic Skewness in Stochastic Volatility Models: A Penalized Prior Approach

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 scale mixture of skew normal (SMSN) family (DynSSV‐SMSN), using priors that penalize model complexity. Parameter estimation is carried out using the Hamiltonian Monte Carlo (HMC) method via the 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, as well as an improvement in the accuracy of expected shortfall forecasts for Bitcoin applications.