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Book Quasi Maximum Likelihood Estimation of Semi Strong GARCH Models

Download or read book Quasi Maximum Likelihood Estimation of Semi Strong GARCH Models written by Juan Carlos Escanciano and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This note proves the consistency and asymptotic normality of the Quasi-Maximum Likelihood Estimator (QMLE) of the parameters of a GARCH model with martingale difference centered squared innovations. The results are obtained under mild conditions and generalize and improve those in Lee and Hansen (1994) for the local QMLE in semi-strong GARCH(1,1) models. In particular, no restrictions on the conditional mean are imposed. Our proofs closely follow those in Francq and Zakoian (2004) for independent and identically distributed innovations.

Book GARCH Models

    Book Details:
  • Author : Christian Francq
  • Publisher : John Wiley & Sons
  • Release : 2019-06-10
  • ISBN : 1119313570
  • Pages : 517 pages

Download or read book GARCH Models written by Christian Francq and published by John Wiley & Sons. This book was released on 2019-06-10 with total page 517 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 Quasi Maximum Likelihood Estimation of GARCH Models with Heavy Tailed Likelihoods

Download or read book Quasi Maximum Likelihood Estimation of GARCH Models with Heavy Tailed Likelihoods written by Jianqing Fan and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The non-Gaussian maximum likelihood estimator is frequently used in GARCH models with the intention of capturing the heavy-tailed returns. However, unless the parametric likelihood family contains the true likelihood, the estimator is inconsistent due to density misspecification. To correct this bias, we identify an unknown scale parameter that is critical to the identification, and propose a two-step quasi maximum likelihood procedure with non-Gaussian likelihood functions. This novel approach is consistent and asymptotically normal under weak moment conditions. Moreover, it achieves better efficiency than the Gaussian alternative, particularly when the innovation error has heavy tails. We also summarize and compare the values of the scale parameter and the asymptotic efficiency for estimators based on different choices of likelihood functions with an increasing level of heaviness in the innovation tails. Numerical studies confirm the advantages of the proposed approach.

Book Handbook of Financial Time Series

Download or read book Handbook of Financial Time Series written by Torben Gustav Andersen and published by Springer Science & Business Media. This book was released on 2009-04-21 with total page 1045 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Financial Time Series gives an up-to-date overview of the field and covers all relevant topics both from a statistical and an econometrical point of view. There are many fine contributions, and a preamble by Nobel Prize winner Robert F. Engle.

Book On Quasi likelihood Estimation

Download or read book On Quasi likelihood Estimation written by Youyi Chen and published by . This book was released on 1991 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Quasi Likelihood And Its Application

Download or read book Quasi Likelihood And Its Application written by Christopher C. Heyde and published by Springer Science & Business Media. This book was released on 2008-01-08 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first account in book form of all the essential features of the quasi-likelihood methodology, stressing its value as a general purpose inferential tool. The treatment is rather informal, emphasizing essential principles rather than detailed proofs, and readers are assumed to have a firm grounding in probability and statistics at the graduate level. Many examples of the use of the methods in both classical statistical and stochastic process contexts are provided.

Book When Simplicity Offers a Benefit  Not a Cost

Download or read book When Simplicity Offers a Benefit Not a Cost written by Todd Prono and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Robust M Estimation of Multivariate GARCH Models

Download or read book Robust M Estimation of Multivariate GARCH Models written by Kris Boudt and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In empirical work on multivariate financial time series, it is common to postulate a Multivariate GARCH model. We show that the popular Gaussian quasi-maximum likelihood estimator of MGARCH models is very sensitive to outliers in the data. We propose to use robust M-estimators and provide asymptotic theory for M-estimators of MGARCH models. The Monte Carlo study and empirical application document the good robustness properties of the M-estimator with a fat-tailed Student t loss function and volatility models with the property of bounded innovation propagation.

Book QUASI MAXIMUM LIKELIHOOD ESTIMATION OF DYNAMIC MODELS WITH TIME VARYNG COVARIANCES

Download or read book QUASI MAXIMUM LIKELIHOOD ESTIMATION OF DYNAMIC MODELS WITH TIME VARYNG COVARIANCES written by Tim BOLLERSLEV and published by . This book was released on 1988 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book On the Three Step Non Gaussian Quasi Maximum Likelihood Estimation of Heavy Tailed Double Autoregressive Models

Download or read book On the Three Step Non Gaussian Quasi Maximum Likelihood Estimation of Heavy Tailed Double Autoregressive Models written by Dong Li and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This note considers a three-step non-Gaussian quasi-maximum likelihood estimation (TS-NGQMLE) of the double autoregressive model with its asymptotics, which improves efficiency of the GQMLE and circumvents inconsistency of the NGQMLE when the innovation is heavy-tailed. Under mild conditions, the estimator not only can achieve consistency and asymptotic normality regardless of density misspecification of the innovation, but also outperforms the existing estimators, such as the GQMLE and the (weighted) least absolute deviation estimator, when the innovation is indeed heavy-tailed.

Book Semiparametric Maximum Likelihood Estimation of GARCH Models

Download or read book Semiparametric Maximum Likelihood Estimation of GARCH Models written by Jian Yang and published by London : Department of Economics, University of Western Ontario. This book was released on 1998 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Quasi maximum Likelihood Estimation of Dynamic Models with Time Varying Covariances

Download or read book Quasi maximum Likelihood Estimation of Dynamic Models with Time Varying Covariances written by Tim Bollerslev and published by . This book was released on 1988 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Book Quasi Maximum Likelihood Estimation for Conditional Expectiles

Download or read book Quasi Maximum Likelihood Estimation for Conditional Expectiles written by Collin Philipps and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We characterize the quasi-likelihood functions that may elicit expectiles and find that the family has a unique representation under standard conditions for linear regression. The only distribution that elicits expectiles as its quasi-maximum likelihood estimator under general conditions is an asymmetric normal distribution. Next, we analyze the quasi maximum likelihood estimator and give conditions for consistency, asymptotic normality, and efficiency. The estimator is unique up to the choice of weights on individual observations and nests the usual GLS estimator. We give the asymptotic MVUE and a uniform Cramer-Rao theorem for expectile regression.