# A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approach

@article{Santos2018ABG, title={A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approach}, author={Thiago Rezende Dos Santos}, journal={arXiv: Statistical Finance}, year={2018} }

Several studies explore inferences based on stochastic volatility (SV) models, taking into account the stylized facts of return data. The common problem is that the latent parameters of many volatility models are high-dimensional and analytically intractable, which means inferences require approximations using, for example, the Markov Chain Monte Carlo or Laplace methods. Some SV models are expressed as a linear Gaussian state-space model that leads to a marginal likelihood, reducing the… Expand

#### Figures and Tables from this paper

#### References

SHOWING 1-10 OF 58 REFERENCES

Bayesian analysis of stochastic volatility models with fat-tails and correlated errors

- Mathematics, Economics
- 2004

Abstract The basic univariate stochastic volatility model specifies that conditional volatility follows a log-normal auto-regressive model with innovations assumed to be independent of the… Expand

Markov chain Monte Carlo methods for stochastic volatility models

- Mathematics
- 2002

This paper is concerned with simulation-based inference in generalized models of stochastic volatility defined by heavy-tailed Student-t distributions (with unknown degrees of freedom) and exogenous… Expand

Mcmc Bayesian Estimation of a Skew-Ged Stochastic Volatility Model

- Mathematics
- 2003

In this paper we present a stochastic volatility model assuming that the return shock has a Skew-GED distribution. This allows a parsimonious yet flexible treatment of asymmetry and heavy tails in… Expand

Bayesian Analysis of Stochastic Volatility Models

- Mathematics
- 1994

New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to… Expand

Modelling stochastic volatility using generalized t distribution

- Mathematics
- 2013

In modelling financial return time series and time-varying volatility, the Gaussian and the Student-t distributions are widely used in stochastic volatility (SV) models. However, other distributions… Expand

Stochastic volatility with leverage: Fast and efficient likelihood inference

- Mathematics
- 2007

This paper is concerned with the Bayesian analysis of stochastic volatility (SV) models with leverage. Specifically, the paper shows how the often used Kim et al. [1998. Stochastic volatility:… Expand

Stochastic Volatility: Likelihood Inference And Comparison With Arch Models

- Mathematics, Economics
- 1994

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective… Expand

A maximum likelihood approach for non-Gaussian stochastic volatility models

- Economics
- 1998

A maximum likelihood approach for the analysis of stochastic volatility models is developed. The method uses a recursive numerical integration procedure that directly calculates the marginal… Expand

A Stochastic Volatility Model With Conditional Skewness

- Economics
- 2012

We develop a discrete-time affine stochastic volatility model with time-varying conditional skewness (SVS). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness… Expand

Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

- Mathematics
- 2009

Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive… Expand