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Maximum Likelihood Estimation Of The Markov Switching Garch Model
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Book Synopsis Maximum Likelihood Estimation of the Markov-Switching GARCH Model by : Maciej Augustyniak
Download or read book Maximum Likelihood Estimation of the Markov-Switching GARCH Model written by Maciej Augustyniak and published by . This book was released on 2016 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Markov-switching GARCH model offers rich dynamics to model financial data. Estimating this path dependent model is a challenging task because exact computation of the likelihood is infeasible in practice. This difficulty led to estimation procedures either based on a simplification of the model or not dependent on the likelihood. There is no method available to obtain the maximum likelihood estimator without resorting to a modification of the model. A novel approach is developed based on both the Monte Carlo expectation-maximization algorithm and importance sampling to calculate the maximum likelihood estimator and asymptotic variance-covariance matrix of the Markov-switching GARCH model. Practical implementation of the proposed algorithm is discussed and its effectiveness is demonstrated in simulation and empirical studies.
Book Synopsis Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure by : Maciej Augustyniak
Download or read book Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure written by Maciej Augustyniak and published by . This book was released on 2017 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Markov-switching GARCH model allows for a GARCH structure with time-varying parameters. This flexibility is unfortunately undermined by a path dependence problem which complicates the parameter estimation process. This problem led to the development of computationally intensive estimation methods and to simpler techniques based on an approximation of the model, known as collapsing procedures. This article develops an original algorithm to conduct maximum likelihood inference in the Markov-switching GARCH model, generalizing and improving previously proposed collapsing approaches. A new relationship between particle filtering and collapsing procedures is established which reveals that this algorithm corresponds to a deterministic particle filter. Simulation and empirical studies show that the proposed method allows for a fast and accurate estimation of the model.
Book Synopsis Maximum Likelihood Estimation and Forecasting for GARCH, Markov Switching, and Locally Stationary Wavelet Processes by : Yingfu Xie
Download or read book Maximum Likelihood Estimation and Forecasting for GARCH, Markov Switching, and Locally Stationary Wavelet Processes written by Yingfu Xie and published by . This book was released on 2007 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Analytical Derivatives for Markov Switching Models by : Jeff Gable
Download or read book Analytical Derivatives for Markov Switching Models written by Jeff Gable and published by . This book was released on 2008 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper derives analytical gradients for a broad class of regime-switching models with Markovian state-transition probabilities. Such models are usually estimated by maximum likelihood methods, which require the derivatives of the likelihood function with respect to the parameter vector. These gradients are usually calculated by means of numerical techniques. The paper shows that analytical gradients considerably speed up maximum-likelihood estimation with no loss in accuracy. A sample program listing is included.
Book Synopsis Markov Switching Models for Volatility by : Monica Billio
Download or read book Markov Switching Models for Volatility written by Monica Billio and published by . This book was released on 2013 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is devoted to show duality in the estimation of Markov Switching (MS) processes for volatility. It is well-known that MS-GARCH models suffer of path dependence which makes the estimation step unfeasible with usual Maximum Likelihood procedure. However, by rewriting the MS-GARCH model in a suitable linear State Space representation, we are able to give a unique framework to reconcile the estimation obtained by the Kalman Filter and with some auxiliary models proposed in the literature. Reasoning in the same way, we present a linear Filter for MS-Stochastic Volatility (MS-SV) models on which different conditioning sets yield more flexibility in the estimation. Estimation on simulated data and on short-term interest rates shows the feasibility of the proposed approach.
Book Synopsis Consistency of Quasi-maximum Likelihood Estimators for the Regime-switching GARCH Models by : Yingfu Xie
Download or read book Consistency of Quasi-maximum Likelihood Estimators for the Regime-switching GARCH Models written by Yingfu Xie and published by . This book was released on 2005 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Basic Recursion for Markov Switching Models by : Lung-Fei Lee
Download or read book A Basic Recursion for Markov Switching Models written by Lung-Fei Lee and published by . This book was released on 1995 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Financial Risk Management with Bayesian Estimation of GARCH Models by : David Ardia
Download or read book Financial Risk Management with Bayesian Estimation of GARCH Models written by David Ardia and published by Springer Science & Business Media. This book was released on 2008-05-08 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.
Download or read book GARCH Models written by Christian Francq and published by John Wiley & Sons. This book was released on 2019-03-19 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation, and tests. The book also provides new coverage of several extensions such as multivariate models, looks at financial applications, and explores the very validation of the models used. GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter titled Alternative Models for the Conditional Variance contains a section on Stochastic Recurrence Equations and additional material on EGARCH, Log-GARCH, GAS, MIDAS, and intraday volatility models, among others. The book is also updated with a more complete discussion of multivariate GARCH; a new section on Cholesky GARCH; a larger emphasis on the inference of multivariate GARCH models; a new set of corrected problems available online; and an up-to-date list of references. Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH models Covers significant developments in the field, especially in multivariate models Contains completely renewed chapters with new topics and results Handles both theoretical and applied aspects Applies to researchers in different fields (time series, econometrics, finance) Includes numerous illustrations and applications to real financial series Presents a large collection of exercises with corrections Supplemented by a supporting website featuring R codes, Fortran programs, data sets and Problems with corrections GARCH Models, 2nd Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
Book Synopsis Consistency of Quasi-maximum Likelihood Estimators for the Reduced Regime-switching GARCH Models by : Yingfu Xie
Download or read book Consistency of Quasi-maximum Likelihood Estimators for the Reduced Regime-switching GARCH Models written by Yingfu Xie and published by . This book was released on 2005 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Marginal Likelihood for Markov-switching and Change-point GARCH Models by : Luc Bauwens
Download or read book Marginal Likelihood for Markov-switching and Change-point GARCH Models written by Luc Bauwens and published by . This book was released on 2011 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Analysis of the Likelihood Function for Markov-Switching VAR(CH) Models by : Maddalena Cavicchioli
Download or read book Analysis of the Likelihood Function for Markov-Switching VAR(CH) Models written by Maddalena Cavicchioli and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, we give simple matrix formulae for maximum likelihood estimates of parameters in a broad class of vector autoregressions subject to Markovian changes in regime. This allows us to determine explicitly the asymptotic variance-covariance matrix of the estimators, giving a concrete possibility for the use of the classical testing procedures. In the context of multivariate autoregressive conditional heteroskedastic models with changes in regime, we provide formulae for the analytic derivatives of the log likelihood. Then we prove the consistency of some maximum likelihood estimators and give some formulae for the asymptotic variance of the different estimators.
Book Synopsis Markov-Switching GARCH Models in R by : David Ardia
Download or read book Markov-Switching GARCH Models in R written by David Ardia and published by . This book was released on 2019 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: We describe the package MSGARCH, which implements Markov-switching GARCH models in R with efficient C++ object-oriented programming. Markov-switching GARCH models have become popular methods to account for regime changes in the conditional variance dynamics of time series. The package MSGARCH allows the user to perform simulations as well as Maximum Likelihood and MCMC/Bayesian estimations of a very large class of Markov-switching GARCH-type models. The package also provides methods to make single-step and multi-step ahead forecasts of the complete conditional density of the variable of interest. Risk management tools to estimate conditional volatility, Value-at-Risk, and Expected-Shortfall are also available. We illustrate the broad functionality of the MSGARCH package using exchange rate and stock market return data.
Book Synopsis Inference in Hidden Markov Models by : Olivier Cappé
Download or read book Inference in Hidden Markov Models written by Olivier Cappé and published by Springer Science & Business Media. This book was released on 2006-04-12 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view.
Book Synopsis Finite-sample Properties of the Maximum Likelihood Estimator in Autoaggressive Models with Markov Switching by : Zacharias G. Psaradakis
Download or read book Finite-sample Properties of the Maximum Likelihood Estimator in Autoaggressive Models with Markov Switching written by Zacharias G. Psaradakis and published by . This book was released on 1995 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Finite-sample Properties of the Maximum Likelihood Estimator in Autoregressive Models with Markov Switching by : Zacharias Psaradakis
Download or read book Finite-sample Properties of the Maximum Likelihood Estimator in Autoregressive Models with Markov Switching written by Zacharias Psaradakis and published by . This book was released on 1995 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book GARCH Models written by Christian Francq and published by John Wiley & Sons. This book was released on 2011-06-24 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation and tests. The book also provides coverage of several extensions such as asymmetric and multivariate models and looks at financial applications. Key features: Provides up-to-date coverage of the current research in the probability, statistics and econometric theory of GARCH models. Numerous illustrations and applications to real financial series are provided. Supporting website featuring R codes, Fortran programs and data sets. Presents a large collection of problems and exercises. This authoritative, state-of-the-art reference is ideal for graduate students, researchers and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
