Add to ORKGMotivated by the fact that realized measures of volatility are affected by measurement errors, we introduce a new family of discrete-time stochastic volatility models having two measurement equations relating both the observed returns and realized measures to the latent conditional variance
Estimation of stochastic volatility (SV) models is a formidable task because the presence of the lat...
In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility mode...
Discrete-time stochastic volatility (SV) models have generated a considerable literature in financia...
Motivated by the fact that realized measures of volatility are affected by measurement errors, we in...
Based on the fact that realized measures of volatility are affected by measurement errors, we introd...
In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) ...
It has long been recognised that the return volatility of financial assets tends to vary over time w...
This paper proposes a novel stochastic volatility model that draws from the exist- ing literature on...
We introduce a multivariate stochastic volatility model that imposes no restrictions on the structur...
Modelling of the fi nancial variable evolution represents an important issue in financial econometri...
The R package stochvol provides a fully Bayesian implementation of heteroskedasticity modeling withi...
An efficient method for Bayesian inference in stochastic volatility models uses a linear state space...
In this paper we exploit some recent computational advances in Bayesian inference, coupled with data...
A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonp...
This paper extends the existing fully parametric Bayesian literature on stochastic volatility to all...
Estimation of stochastic volatility (SV) models is a formidable task because the presence of the lat...
In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility mode...
Discrete-time stochastic volatility (SV) models have generated a considerable literature in financia...
Motivated by the fact that realized measures of volatility are affected by measurement errors, we in...
Based on the fact that realized measures of volatility are affected by measurement errors, we introd...
In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) ...
It has long been recognised that the return volatility of financial assets tends to vary over time w...
This paper proposes a novel stochastic volatility model that draws from the exist- ing literature on...
We introduce a multivariate stochastic volatility model that imposes no restrictions on the structur...
Modelling of the fi nancial variable evolution represents an important issue in financial econometri...
The R package stochvol provides a fully Bayesian implementation of heteroskedasticity modeling withi...
An efficient method for Bayesian inference in stochastic volatility models uses a linear state space...
In this paper we exploit some recent computational advances in Bayesian inference, coupled with data...
A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonp...
This paper extends the existing fully parametric Bayesian literature on stochastic volatility to all...
Estimation of stochastic volatility (SV) models is a formidable task because the presence of the lat...
In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility mode...
Discrete-time stochastic volatility (SV) models have generated a considerable literature in financia...