Bayesian Estimation of Stochastic Volatility Models
| Author | : Jens Jungkunz |
| Publisher | : |
| Total Pages | : 80 |
| Release | : 2010 |
| Genre | : |
| ISBN | : |
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Bayesian Estimation of Stochastic Volatility Models with Fat Tails and Correlated Errors Applied to the South African Financial Market
| Author | : Richard Savanhu |
| Publisher | : |
| Total Pages | : 80 |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
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Bayesian Estimation of a Stochastic Volatility Model Using Option and Spot Prices
| Author | : Catherine S. Forbes |
| Publisher | : |
| Total Pages | : 40 |
| Release | : 2002 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : 9780732610937 |
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Bayesian Statistics and New Generations
| Author | : Raffaele Argiento |
| Publisher | : Springer Nature |
| Total Pages | : 184 |
| Release | : 2019-11-21 |
| Genre | : Mathematics |
| ISBN | : 3030306119 |
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This book presents a selection of peer-reviewed contributions to the fourth Bayesian Young Statisticians Meeting, BAYSM 2018, held at the University of Warwick on 2-3 July 2018. The meeting provided a valuable opportunity for young researchers, MSc students, PhD students, and postdocs interested in Bayesian statistics to connect with the broader Bayesian community. The proceedings offer cutting-edge papers on a wide range of topics in Bayesian statistics, identify important challenges and investigate promising methodological approaches, while also assessing current methods and stimulating applications. The book is intended for a broad audience of statisticians, and demonstrates how theoretical, methodological, and computational aspects are often combined in the Bayesian framework to successfully tackle complex problems.
Bayesian Analysis of Stochastic Volatility Models
| Author | : Asma Graja |
| Publisher | : |
| Total Pages | : |
| Release | : 2009 |
| Genre | : |
| ISBN | : |
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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.
Multivariate Stochastic Volatility Models
| Author | : Jun Yu |
| Publisher | : |
| Total Pages | : 29 |
| Release | : 2004 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
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Shows that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called WinBUGS.
Bayesian Stochastic Volatility Models
| Author | : Stefanos Giakoumatos |
| Publisher | : LAP Lambert Academic Publishing |
| Total Pages | : 240 |
| Release | : 2010-08 |
| Genre | : |
| ISBN | : 9783838386331 |
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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
Bugs for a Bayesian Analysis of Stochastic Volatility Models
| Author | : Renate Meyer |
| Publisher | : |
| Total Pages | : 17 |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
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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.
Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing
| Author | : Achal Awasthi |
| Publisher | : |
| Total Pages | : 150 |
| Release | : 2018 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
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In this thesis, we propose a generalized Heston model as a tool to estimate volatility. We have used Approximate Bayesian Computing to estimate the parameters of the generalized Heston model. This model was used to examine the daily closing prices of the Shanghai Stock Exchange and the NIKKEI 225 indices. We found that this model was a good fit for shorter time periods around financial crisis. For longer time periods, this model failed to capture the volatility in detail.
Stochastic Volatility Models with Heavy-tailed Distributions
| Author | : Toshiaki Watanabe |
| Publisher | : |
| Total Pages | : 64 |
| Release | : 2001 |
| Genre | : Bayesian statistical decision theory |
| ISBN | : |
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