18 citations · 36 across the 14 of their papers we have counts for
7 papers · 1 filter
Stochastic Volatility in Mean Models with Heavy Tails: A Fast Approximate Bayesian Inference Using Hidden Markov Models
Bruno E. Holtz, Carlos A. Abanto-Valle, Ricardo S. Ehlers +1
This paper extends the approximate Bayesian estimation framework for Stochastic Volatility in Mean (SVM) models to accommodate heavy-tailed distributions from the Scale Mixture of…
A Conway-Maxwell-Poisson GARMA Model for Count Data
Ricardo S Ehlers
We propose a flexible model for count time series which has potential uses for both underdispersed and overdispersed data. The model is based on the Conway-Maxwell-Poisson (COM-Poi…
Zero Variance and Hamiltonian Monte Carlo Methods in GARCH Models
Rafael S. Paixão, Ricardo S. Ehlers
In this paper, we develop Bayesian Hamiltonian Monte Carlo methods for inference in asymmetric GARCH models under different distributions for the error term. We implemented Zero-va…
BDSAR: a new package on Bregman divergence for Bayesian simultaneous autoregressive models
Ian M Danilevicz, Ricardo S Ehlers
BDSAR is an R package which estimates distances between probability distributions and facilitates a dynamic and powerful analysis of diagnostics for Bayesian models from the class…
Riemann Manifold Langevin Methods on Stochastic Volatility Estimation
Mauricio Zevallos, Loretta Gasco, Ricardo Ehlers
In this paper we perform Bayesian estimation of stochastic volatility models with heavy tail distributions using Metropolis adjusted Langevin (MALA) and Riemman manifold Langevin (…
BayesDccGarch - An Implementation of Multivariate GARCH DCC Models
Jose A. Fioruci, Ricardo S. Ehlers, Francisco Louzada
Multivariate GARCH models are important tools to describe the dynamics of multivariate times series of financial returns. Nevertheless, these models have been much less used in pra…
Bayesian Inference for Generalized Extreme Value Distributions via Hamiltonian Monte Carlo
Marcelo Hartmann, Ricardo Ehlers
In this paper we propose to evaluate and compare Markov chain Monte Carlo (MCMC) methods to estimate the parameters in a generalized extreme value model. We employed the Bayesian a…