Bayesian Analysis of Stochastic Volatility Models PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Bayesian Analysis of Stochastic Volatility Models PDF full book. Access full book title Bayesian Analysis of Stochastic Volatility Models by Joanne Jia Jia Wang. Download full books in PDF and EPUB format.
Author: Joanne Jia Jia Wang
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 468
Get Book Here
Book Description
Author: Joanne Jia Jia Wang
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 468
Get Book Here
Book Description
Author: Asma Graja
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Time varying volatility is a characteristic of many financial series. An alternative to the popular ARCH framework is a Stochastic Volatility model which is harder to estimate than the ARCH family. In this paper we estimate and compare two classes of Stochastic Volatility models proposed in financial literature: the Log normal autoregressive model with some extensions and the Heston model. The basic univariate Stochastic Volatility model is extended to allow for the quot;leverage effectquot; via correlation between the volatility and the mean innovations and for fat tails in the mean equation innovation.A Bayesian Markov Chain Monte Carlo algorithm developed in Jacquier, Polson and Rossi 2004 is analyzed and applied to a large data base of the French financial market. Moreover, explicit expression for the parameter's estimators is found via Monte Carlo technique.
Author: Eric Jacquier
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 41
Get Book Here
Book Description
Author: Renate Meyer
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 20
Get Book Here
Book Description
Author: Renate Meyer
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
Get Book Here
Book Description
This paper reviews the general Bayesian approach to parameter estimation in stochastic volatility models with posterior computations performed by Gibbs sampling. The main purpose is to illustrate the ease with which the Bayesian stochastic volatility model can now be studied routinely via BUGS (Bayesian Inference Using Gibbs Sampling), a recently developed, user-friendly, and freely available software package. It is an ideal software tool for the exploratory phase of model building as any modifications of a model including changes of priors and sampling error distributions are readily realized with only minor changes of the code. BUGS automates the calculation of the full conditional posterior distributions using a model representation by directed acyclic graphs. It contains an expert system for choosing an efficient sampling method for each full conditional. Furthermore, software for convergence diagnostics and statistical summaries is available for the BUGS output. The BUGS implementation of a stochastic volatility model is illustrated using a time series of daily Pound/Dollar exchange rates.
Author: Pawel J. Szerszen
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Stefanos Giakoumatos
Publisher: LAP Lambert Academic Publishing
ISBN: 9783838386331
Category :
Languages : en
Pages : 240
Get Book Here
Book Description
The phenomenon of changing variance and covariance is often encountered in financial time series. As a result, during the last years researchers focused on the time-varying volatility models. These models are able to describe the main characteristics of the financial data such as the volatility clustering. In addition, the development of the Markov Chain Monte Carlo Techniques (MCMC) provides a powerful tool for the estimation of the parameters of the time-varying volatility models, in the context of Bayesian analysis. In this thesis, we adopt the Bayesian inference and we propose easy-to-apply MCMC algorithms for a variety of time-varying volatility models. We use a recent development in the context of the MCMC techniques, the Auxiliary variable sampler. This technique enables us to construct MCMC algorithms, which only consist of Gibbs steps. We propose new MCMC algorithms for many univariate and multivariate models. Furthermore, we apply the proposed MCMC algorithms to real data and compare the above models based on their predictive distribution
Author: Daniel B. Nelson
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Toshiaki Watanabe
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 64
Get Book Here
Book Description
Author: Stefanos Dimitrakopoulos
Publisher:
ISBN:
Category :
Languages : en
Pages : 28
Get Book Here
Book Description
We propose a moving average stochastic volatility in mean model and a moving average stochastic volatility model with leverage. For parameter estimation, we develop efficient Markov chain Monte Carlo algorithms and illustrate our methods, using simulated data and a real data set. We compare the proposed specifications against several competing stochastic volatility models, using marginal likelihoods and the observed-data Deviance information criterion. We find that the moving average stochastic volatility model with leverage has better fit to our daily return series than various standard benchmarks.
