Inferência bayesiana em modelos de volatilidade estocástica usando métodos de Monte Carlo Hamiltoniano
Abstract
This paper presents a study using Bayesian approach in stochastic volatility models for modeling
financial time series, using Hamiltonian Monte Carlo methods (HMC). We propose the use
of other distributions for the errors of the equation at stochastic volatiliy model, besides the
Gaussian distribution, to treat the problem as heavy tails and asymmetry in the returns. Moreover,
we use recently developed information criteria WAIC and LOO that approximate the crossvalidation
methodology, to perform the selection of models. Throughout this work, we study
the quality of the HMC methods through examples, simulation study and application to dataset.
In addition, we evaluated the performance of the proposed models and methods by calculating
estimates for Value at Risk (VaR) for multiple time horizons.