Download or read book Asymptotic Theory for M estimators in General Autoregressive Conditional Heteroscedastic Time Series Models written by Fabian Tinkl and published by . This book was released on 2013 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Asymptotic Theory for M estimators in General Autoregressive Conditional Heteroscedasticity Models written by Fabian Tinkl and published by . This book was released on 2013 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Asymptotic Theory of General Multivariate GARCH Models written by Weibin Jiang and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalized autoregressive conditional heteroscedasticity (GARCH) models are widely used in financial markets. Parameters of GARCH models are usually estimated by the quasi-maximum likelihood estimator (QMLE). In recent years, economic theory often implies equilibrium between the levels of time series, which makes the application of multivariate models a necessity. Unfortunately the asymptotic theory of the multivariate GARCH models is far from coherent since many algorithms on the univariate case do not extend to multivariate models naturally. This thesis studies the asymptotic theory of the QMLE under mild conditions. We give some counterexamples for the parameter identifiability result in Jeantheau [1998] and provide a better necessary and sufficient condition. We prove the ergodicity of the conditional variance process on an application of theorems by Meyn and Tweedie [2009]. Under those conditions, the consistency and asymptotic normality of the QMLE can be proved by the standard compactness argument and Taylor expansion of the score function. We also give numeric example on verifying the assumptions and the scaling issue when estimating GARCH parameters in S+ FinMetrics.

Download or read book Asymptotic Theory for GARCH in mean Models written by Weiwei Liu and published by . This book was released on 2013 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: The GARCH-in-mean process is an important extension of the standard GARCH (generalized autoregressive conditional heteroscedastic) process and it has wide applications in economics and finance. The parameter estimation of GARCH type models usually involves the quasi-maximum likelihood (QML) technique as it produces consistent and asymptotically Gaussian distributed estimators under certain regularity conditions. For a pure GARCH model, such conditions were already found with asymptotic properties of its QML estimator well understood. However, when it comes to GARCH-in-mean models those properties are still largely unknown. The focus of this work is to establish a set of conditions under which the QML estimator of GARCH-in-mean models will have the desired asymptotic properties. Some general Markov model tools are applied to derive the result.

Download or read book Asymptotic Theory in Financial Time Series Models with Conditional Heteroscedasticity written by Theis Lange and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book On Asymptotic Theory for Arch Models written by Christian Hafner and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autoregressive conditional heteroskedasticity (ARCH)() models nest a wide range of ARCH and generalized ARCH models including models with long memory in volatility. Existing work assumes the existence of second moments. However, the fractionally integrated generalized ARCH model, one version of a long memory in volatility model, does not have finite second moments and rarely satisfies the moment conditions of the existing literature. This article weakens the moment assumptions of a general ARCH() class of models and develops the theory for consistency and asymptotic normality of the quasi-maximum likelihood estimator.

Download or read book Asymptotic Theory of M estimators in General Statistical Models written by R. J. Chitashvili and published by . This book was released on 1990 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Asymptotics for the Conditional Sum Of Squares Estimator in Multivariate Fractional Time Series Models written by Morten Ørregaard Nielsen and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This article proves consistency and asymptotic normality for the conditional-sum-of-squares estimator, which is equivalent to the conditional maximum likelihood estimator, in multivariate fractional time-series models. The model is parametric and quite general and, in particular, encompasses the multivariate non-cointegrated fractional autoregressive integrated moving average (ARIMA) model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge uniformly in probability, thus making the proof much more challenging than usual. The neighbourhood around the critical point where uniform convergence fails is handled using a truncation argument.

Download or read book Asymptotic Theory of M estimators in General Statistical Models written by R. J. Chitashvili and published by . This book was released on 1990 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Higher Order Asymptotic Theory for Time Series Analysis written by Masanobu Taniguchi and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: The initial basis of this book was a series of my research papers, that I listed in References. I have many people to thank for the book's existence. Regarding higher order asymptotic efficiency I thank Professors Kei Takeuchi and M. Akahira for their many comments. I used their concept of efficiency for time series analysis. During the summer of 1983, I had an opportunity to visit The Australian National University, and could elucidate the third-order asymptotics of some estimators. I express my sincere thanks to Professor E.J. Hannan for his warmest encouragement and kindness. Multivariate time series analysis seems an important topic. In 1986 I visited Center for Mul tivariate Analysis, University of Pittsburgh. I received a lot of impact from multivariate analysis, and applied many multivariate methods to the higher order asymptotic theory of vector time series. I am very grateful to the late Professor P.R. Krishnaiah for his cooperation and kindness. In Japan my research was mainly performed in Hiroshima University. There is a research group of statisticians who are interested in the asymptotic expansions in statistics. Throughout this book I often used the asymptotic expansion techniques. I thank all the members of this group, especially Professors Y. Fujikoshi and K. Maekawa foItheir helpful discussion. When I was a student of Osaka University I learned multivariate analysis and time series analysis from Professors Masashi Okamoto and T. Nagai, respectively. It is a pleasure to thank them for giving me much of research background.

Download or read book Asymptotic Theory of M estimators in General Statistical Models written by R. J. Chitashvili and published by . This book was released on 1990 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Empirical Processes in M Estimation written by Sara A. van de Geer and published by Cambridge University Press. This book was released on 2009-11-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theory of empirical processes provides valuable tools for the development of asymptotic theory in (nonparametric) statistical models, and makes it possible to give a unified treatment of various models. This book reveals the relation between the asymptotic behavior of M-estimators and the complexity of parameter space, using entropy as a measure of complexity, presenting tools and methods to analyze nonparametric, and in some cases, semiparametric methods. Graduate students and professionals in statistics, as well as those interested in applications, e.g. to econometrics, medical statistics, etc., will welcome this treatment.

Download or read book Estimation in Conditionally Heteroscedastic Time Series Models written by Daniel Straumann and published by Springer Science & Business Media. This book was released on 2006-01-27 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: In his seminal 1982 paper, Robert F. Engle described a time series model with a time-varying volatility. Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic). This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data. This includes the classical statistical issues of consistency and limiting distribution of estimators. Particular attention is addressed to (quasi) maximum likelihood estimation and misspecified models, along to phenomena due to heavy-tailed innovations. The used methods are based on techniques applied to the analysis of stochastic recurrence equations. Proofs and arguments are given wherever possible in full mathematical rigour. Moreover, the theory is illustrated by examples and simulation studies.

Download or read book Time Series Analysis Methods and Applications written by Tata Subba Rao and published by Elsevier. This book was released on 2012-06-26 with total page 778 pages. Available in PDF, EPUB and Kindle. Book excerpt: 'Handbook of Statistics' is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with volume 30 dealing with time series.

Download or read book Time Series Analysis Methods and Applications written by and published by Elsevier. This book was released on 2012-05-18 with total page 777 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments.The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respective areas

Download or read book Parameter Estimation in Non linear Time Series written by Lianfen Qian and published by . This book was released on 1996 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: