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BAYESIAN STOCHASTIC VOLATILITY MODELS: AUXILIARY VARIABLE METHODS FOR STOCHASTIC VOLATILITY AND OTHER TIME-VARYING VOLATILITY MODELS - Brossura
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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
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L'autore
Stefanos Giakoumatos is Associate Professor of Statistics and Quantitative Methods in the Technological Educational Institute of Kalamata Greece. He was born in Athens in 1972 and he was studied Statistics in the Athens University of Economics and Business (Ph.D, M.Sc., B.Sc). His scientific interests include MCMC methods, Time-series Analysis.
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Bayesian Stochastic Volatility Models
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BAYESIAN STOCHASTIC VOLATILITY MODELS AUXILIARY VARIABLE METHODS FOR STOCHASTIC VOLATILITY AND OTHER TIMEVARYING VOLATILITY MODELS
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BAYESIAN STOCHASTIC VOLATILITY MODELS: AUXILIARY VARIABLE METHODS FOR STOCHASTIC VOLATILITY AND OTHER TIME-VARYING VOLATILITY MODELS
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BAYESIAN STOCHASTIC VOLATILITY MODELS
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 240 pp. Englisch. Codice articolo 9783838386331
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BAYESIAN STOCHASTIC VOLATILITY MODELS
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Giakoumatos StefanosStefanos Giakoumatos is Associate Professor of Statistics and Quantitative Methods in the Technological Educational Institute of Kalamata Greece. He was born in Athens in 1972 and he was studied Statistics in the . Codice articolo 5418881
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BAYESIAN STOCHASTIC VOLATILITY MODELS : AUXILIARY VARIABLE METHODS FOR STOCHASTIC VOLATILITY AND OTHER TIME-VARYING VOLATILITY MODELS
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Codice articolo 9783838386331
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Bayesian Stochastic Volatility Models
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BAYESIAN STOCHASTIC VOLATILITY MODELS | AUXILIARY VARIABLE METHODS FOR STOCHASTIC VOLATILITY AND OTHER TIME-VARYING VOLATILITY MODELS
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Taschenbuch. Condizione: Neu. BAYESIAN STOCHASTIC VOLATILITY MODELS | AUXILIARY VARIABLE METHODS FOR STOCHASTIC VOLATILITY AND OTHER TIME-VARYING VOLATILITY MODELS | Stefanos Giakoumatos | Taschenbuch | 240 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838386331 | Verantwortliche Person für die EU: OmniScriptum GmbH & Co. KG, Bahnhofstr. 28, 66111 Saarbrücken, info[at]akademikerverlag[dot]de | Anbieter: preigu. Codice articolo 107353368
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BAYESIAN STOCHASTIC VOLATILITY MODELS
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LAP LAMBERT Academic Publishing Aug 2010, 2010
ISBN 10: 3838386337
ISBN 13: 9783838386331
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 distributionVDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 240 pp. Englisch. Codice articolo 9783838386331
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