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EBookClubs

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Book Bayesian Inference for Stochastic Volatility Models

Download or read book Bayesian Inference for Stochastic Volatility Models written by Zhongxian Men and published by . This book was released on 2012 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic volatility (SV) models provide a natural framework for a representation of time series for financial asset returns. As a result, they have become increasingly popular in the finance literature, although they have also been applied in other fields such as signal processing, telecommunications, engineering, biology, and other areas. In working with the SV models, an important issue arises as how to estimate their parameters efficiently and to assess how well they fit real data. In the literature, commonly used estimation methods for the SV models include general methods of moments, simulated maximum likelihood methods, quasi Maximum likelihood method, and Markov Chain Monte Carlo (MCMC) methods. Among these approaches, MCMC methods are most flexible in dealing with complicated structure of the models. However, due to the difficulty in the selection of the proposal distribution for Metropolis-Hastings methods, in general they are not easy to implement and in some cases we may also encounter convergence problems in the implementation stage. In the light of these concerns, we propose in this thesis new estimation methods for univariate and multivariate SV models.

Book Bayesian Stochastic Volatility Models

Download or read book Bayesian Stochastic Volatility Models written by Stefanos Giakoumatos and published by LAP Lambert Academic Publishing. This book was released on 2010-08 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Book Bayesian Inference in Spatial Stochastic Volatility Models

Download or read book Bayesian Inference in Spatial Stochastic Volatility Models written by Suleyman Taspinar and published by . This book was released on 2019 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, we propose a spatial stochastic volatility model in which the latent log-volatility terms follow a spatial autoregressive process. Though there is no spatial correlation in the outcome equation (the mean equation), the spatial autoregressive process defined for the log-volatility terms introduces spatial dependence in the outcome equation. To introduce a Bayesian Markov chain Monte Carlo (MCMC) estimation algorithm, we transform the model so that the outcome equation takes the form of log-squared terms. We approximate the distribution of the log-squared error terms in the outcome equation with a finite mixture of normal distributions so that the transformed model turns into a linear Gaussian state-space model. Our simulation results indicate that the Bayesian estimator has satisfactory finite sample properties. We investigate the practical usefulness of our proposed model and estimation method by using the price returns of residential properties in the broader Chicago Metropolitan area.

Book Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models

Download or read book Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models written by Gregor Kastner and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book On Efficient Bayesian Inference for Models with Stochastic Volatility

Download or read book On Efficient Bayesian Inference for Models with Stochastic Volatility written by Bill Sakaria and published by . This book was released on 2016 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: An efficient method for Bayesian inference in stochastic volatility models uses a linear state space representation to define a Gibbs sampler in which the volatilities are jointly updated. This method involves the choice of an offset parameter and we illustrate how its choice can have an important effect on the posterior inference. A Metropolis-Hastings algorithm is developed to robustify this approach to choice of the offset parameter. The method is illustrated on both simulated data with known parameters and the daily log returns of the Eurostoxx index.

Book Bayesian Inference for Generalised Markov Switching Stochastic Volatility Models

Download or read book Bayesian Inference for Generalised Markov Switching Stochastic Volatility Models written by Roberto Casarin and published by . This book was released on 2006 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study a Markov switching stochastic volatility model with heavy tail innovations in the observable process. Due to the economic interpretation of the hidden volatility regimes, these models have many financial applications like asset allocation, option pricing and risk management. The Markov switching process is able to capture clustering effects and jumps in volatility. Heavy tail innovations account for extreme variations in the observed process. Accurate modelling of the tails is important when estimating quantiles is the major interest like in risk management applications. Moreover we follow a Bayesian approach to filtering and estimation, focusing on recently developed simulation based filtering techniques, called Particle Filters. Simulation based filters are recursive techniques, which are useful when assuming non-linear and non-Gaussian latent variable models and when processing data sequentially. They allow to update parameter estimates and state filtering as new observations become available.

Book Bugs for a Bayesian Analysis of Stochastic Volatility Models

Download or read book Bugs for a Bayesian Analysis of Stochastic Volatility Models written by Renate Meyer and published by . This book was released on 2013 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Book Semiparametric Bayesian Inference of Long Memory Stochastic Volatility Models

Download or read book Semiparametric Bayesian Inference of Long Memory Stochastic Volatility Models written by Mark J. Jensen and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, a semiparametric, Bayesian estimator of the long-memory stochastic volatility model's fractional order of integration is presented. This new estimator relies on a highly efficient, Markov chain Monte Carlo (MCMC) sampler of the model's posterior distribution. The MCMC algorithm is set forth in the time-scale domain of the stochastic volatility model's wavelet representation. The key to and centerpiece of this new algorithm is the quick and efficient multi-state sampler of the latent volatility's wavelet coefficients. A multi-state sampler of the latent wavelet coefficients is only possible because of the near-independent multivariate distribution of the long-memory process's wavelet coefficients. Using simulated and empirical stock return data, we find that our algorithm produces uncorrelated draws of the posterior distribution and point estimates that rival existing long-memory stochastic volatility estimators.

Book Novel Techniques for Bayesian Inference in Univariate and Multivariate Stochastic Volatility Models

Download or read book Novel Techniques for Bayesian Inference in Univariate and Multivariate Stochastic Volatility Models written by Mike G. Tsionas and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we exploit properties of the likelihood function of the stochastic volatility model to show that it can be approximated accurately and efficiently using a response surface methodology. The approximation is across the plausible range of parameter values and all possible data and is found to be highly accurate. The methods extend easily to multivariate models and are applied to artificial data as well as ten exchange rates and all stocks of FTSE100 using daily data. Formal comparisons with multivariate GARCH models are undertaken using a special prior for the GARCH parameters. The comparisons are based on marginal likelihood and the Bayes factors.

Book Bayesian Inference of Long Memory Stochastic Volatility Via Wavelets

Download or read book Bayesian Inference of Long Memory Stochastic Volatility Via Wavelets written by Mark J. Jensen and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we are concerned with estimating the fractional order of integration associated with a long-memory stochastic volatility model. We develop a new Bayesian estimator based on the Markov chain Monte Carlo sampler and the wavelet representation of the log-squared returns to draw values of the fractional order of integration and latent volatilities from their joint posterior distribution. Unlike short-memory stochastic volatility models, long-memory stochastic volatility models do not have a state-space representation, and thus their sampler cannot employ the Kalman filters simulation smoother to update the chain of latent volatilities. Instead, we design a simulator where the latent long-memory volatilities are drawn quickly and efficiently from the near independent multivariate distribution of the long-memory volatility's wavelet coefficients. We find that sampling volatility in the wavelet domain, rather than in the time domain, leads to a fast and simulation-efficient sampler of the posterior distribution for the volatility's long-memory parameter and serves as a promising alternative estimator to the existing frequentist based estimators of long-memory volatility.

Book Bayesian Inference in a Stochastic Volatility Nelson Siegel Model

Download or read book Bayesian Inference in a Stochastic Volatility Nelson Siegel Model written by Nikolaus Hautsch and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Bayesian Inference for Stochastic Processes

Download or read book Bayesian Inference for Stochastic Processes written by Lyle D. Broemeling and published by CRC Press. This book was released on 2017-12-12 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book designed to introduce Bayesian inference procedures for stochastic processes. There are clear advantages to the Bayesian approach (including the optimal use of prior information). Initially, the book begins with a brief review of Bayesian inference and uses many examples relevant to the analysis of stochastic processes, including the four major types, namely those with discrete time and discrete state space and continuous time and continuous state space. The elements necessary to understanding stochastic processes are then introduced, followed by chapters devoted to the Bayesian analysis of such processes. It is important that a chapter devoted to the fundamental concepts in stochastic processes is included. Bayesian inference (estimation, testing hypotheses, and prediction) for discrete time Markov chains, for Markov jump processes, for normal processes (e.g. Brownian motion and the Ornstein–Uhlenbeck process), for traditional time series, and, lastly, for point and spatial processes are described in detail. Heavy emphasis is placed on many examples taken from biology and other scientific disciplines. In order analyses of stochastic processes, it will use R and WinBUGS. Features: Uses the Bayesian approach to make statistical Inferences about stochastic processes The R package is used to simulate realizations from different types of processes Based on realizations from stochastic processes, the WinBUGS package will provide the Bayesian analysis (estimation, testing hypotheses, and prediction) for the unknown parameters of stochastic processes To illustrate the Bayesian inference, many examples taken from biology, economics, and astronomy will reinforce the basic concepts of the subject A practical approach is implemented by considering realistic examples of interest to the scientific community WinBUGS and R code are provided in the text, allowing the reader to easily verify the results of the inferential procedures found in the many examples of the book Readers with a good background in two areas, probability theory and statistical inference, should be able to master the essential ideas of this book.

Book Bayesian Analysis of Stochastic Volatility Models

Download or read book Bayesian Analysis of Stochastic Volatility Models written by Asma Graja and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.