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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. AUXILIARY VARIABLE METHODS FOR STOCHASTIC VOLATILITY AND OTHER TIME-VARYING VOLATILITY MODELS

автор Stefanos G. издательство Книга по Требованию Книги
Cтраниц 240        мягкая обложка       
Аннотация:Furthermore, we apply the proposed MCMC algorithms to real data and compare the above models based on their predictive distribution As a result, during the last years researchers focused on the time-varying volatility models. 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. The phenomenon of changing variance and covariance is often encountered in financial time series. 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. We use a recent development in the context of the MCMC techniques, the Auxiliary variable sampler. In this thesis, we adopt the Bayesian inference and we propose easy-to-apply MCMC algorithms for a variety of time-varying volatility models.
Автор: Stefanos G.
Издательство: Книга по Требованию
Год:2010
Цена:
3050 руб
Нет в наличии
Поиск: BAYESIAN STOCHASTIC VOLATILITY MODELS. AUXILIARY VARIABLE METHODS FOR STOCHASTIC VOLATILITY AND OTHER TIME-VARYING VOLATILITY MODELS
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