Download or read book On the Construction and Estimation of Asymmetric GARCH Models and the Minimum Volume Sets for Time Series written by Jianing Di and published by . This book was released on 2008 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The first part of the dissertation considers the modeling of financial volatility under a GARCH-type setup. The Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model has earned popularity due to its ability to represent the features of financial returns based on simple model structures. However, new evidence suggests that certain stylized features, particularly the asymmetry of the financial returns, are not captured well by the regular GARCH model. This dissertation introduces two generalizations of the GARCH model that incorporate asymmetry novelly. The first approach is based on time-dependent coefficients of GARCH model that rely on smooth estimates of the local cross-correlation function, and is referred to as the Local Self-Adjusting Volatility (LSAV) model. This model generates stationary and ergodic return processes, and has close connection with the regime switching model. The other approach is based on generalization of the model via flexible semiparametric setup that does not require a parametric specification of the innovation distribution. Several semiparametric estimators are introduced. The proposed two-step estimator is shown to be consistent and asymptotically normal. The limiting distribution contains a vanishing bias term, and a variance-covariance matrix identical to that of the true MLE. The proposed one-step estimator follows the same type of limiting distribution, but with a different vanishing bias and a larger asymptotic variance-covariance matrix. This aspect of the model provides important insights into the efficiencies of the general class of semiparametric estimators of GARCH models. Numerical experiments are carried out to compare different estimators. The second part considers the construction of a minimum volume (MV) set of a multivariate stationary stochastic process. MIT sets provide a natural notion of the 'central mass' of a distribution and have recently become popular as a tool for the detection of anomalies in multivariate data. The proposed method is based on the concept of complexity-penalized estimation and has both desirable theoretical properties and a practical implementation. In particular, for a large class of processes, choice of the penalty reduces to the selection of a single tuning parameter. A data-dependent method for selecting this parameter is introduced. Numerical investigations are based on simulated data and real traffics of the Abilene network.
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2008 with total page 1006 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Time Series Analysis Modeling and Applications written by Witold Pedrycz and published by Springer Science & Business Media. This book was released on 2012-11-29 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological and algorithmic approaches and case studies. This volume is aimed at a broad audience of researchers and practitioners engaged in various branches of operations research, management, social sciences, engineering, and economics. Owing to the nature of the material being covered and a way it has been arranged, it establishes a comprehensive and timely picture of the ongoing pursuits in the area and fosters further developments.
Download or read book Models for Dependent Time Series written by Granville Tunnicliffe Wilson and published by CRC Press. This book was released on 2015-07-29 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vect
Download or read book Non Linear Time Series Models written by Jesse Mwangi and published by LAP Lambert Academic Publishing. This book was released on 2012 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: In contrast to the traditional time series analysis, which focuses on the modeling based on the first two moments, the nonlinear GARCH models specifically take the effect of the higher moments into modeling consideration. This helps to explain and model volatility especially in financial time series. The GARCH models are able to capture financial characteristics such as volatility clustering, heavy tails and asymmetry. In much of the literature available for the GARCH models, the methods of estimating parameters include the MLE, GMM and LSE which have distributional and optimality limitations. In this book, the Optimal Estimating Function(EF) based techniques are derived for the GARCH models. The EF incorporate the Skewness and the Kurtosis moments which are common in financial data. It is shown using simulations that the Estimating Function (EF) method competes reasonably well with the MLE method especially for the non-normal data and hence provides an alternative estimation technique.Financial analysts, Econometricians and Time series scholars will find this book important in teaching and in risk computation
Download or read book Time Series Analysis written by George E. P. Box and published by . This book was released on 1976 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction and summary; Stochastic models and their forecasting; The autocorrelation function and spectrum; Linear stationary models; Linear nonstationary models; Forecasting; Stochastic model building; Model identification; Model estimation; Model diagnostic checking; Seasonal models; Transfer function models; Identification fitting, and checking of transfer function models.
Download or read book Time Series Analysis With Matlab written by Perez M. and published by Createspace Independent Publishing Platform. This book was released on 2016-06-23 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Econometrics Toolbox(TM) provides functions for modeling economic data. You can select and calibrate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis. This book focuses on conditional variance models. Conditional variance models attempt to address volatility clustering in univariate time series models to improve parameter estimates and forecast accuracy. To model volatility, Econometrics Toolbox(TM) supports the standard generalized autoregressive conditional heteroscedastic (ARCH/GARCH) model, the exponential GARCH (EGARCH) model, and the Glosten, Jagannathan, and Runkle (GJR) model.
Download or read book Advances in Time Series Forecasting written by Cagdas Hakan Aladag and published by Bentham Science Publishers. This book was released on 2012 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Time series analysis is applicable in a variety of disciplines such as business administration, economics, public finances, engineering, statistics, econometrics, mathematics and actuarial sciences. Forecasting the future assists in critical organizationa"
Download or read book Time Series Models written by Manfred Deistler and published by Springer Nature. This book was released on 2022-10-21 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a self-contained presentation of the theory and models of time series analysis. Putting an emphasis on weakly stationary processes and linear dynamic models, it describes the basic concepts, ideas, methods and results in a mathematically well-founded form and includes numerous examples and exercises. The first part presents the theory of weakly stationary processes in time and frequency domain, including prediction and filtering. The second part deals with multivariate AR, ARMA and state space models, which are the most important model classes for stationary processes, and addresses the structure of AR, ARMA and state space systems, Yule-Walker equations, factorization of rational spectral densities and Kalman filtering. Finally, there is a discussion of Granger causality, linear dynamic factor models and (G)ARCH models. The book provides a solid basis for advanced mathematics students and researchers in fields such as data-driven modeling, forecasting and filtering, which are important in statistics, control engineering, financial mathematics, econometrics and signal processing, among other subjects.
Download or read book Applied Time Series Analysis II written by David F. Findley and published by Academic Press. This book was released on 2014-05-10 with total page 811 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Time Series Analysis II contains the proceedings of the Second Applied Time Series Symposium Held in Tulsa, Oklahoma, on March 3-5, 1980. The symposium provided a forum for discussing significant advances in time series analysis and signal processing. Effective alternatives to the familiar least-square and maximum likelihood procedures are described, along with maximum likelihood procedures for modeling irregularly sampled series and for classifying non-stationary series. Comprised of 22 chapters, this volume begins with an introduction to the multidimensional filtering theory and presents specific case histories related to the multidimensional recursive filter stability problem; the least squares inverse problem; realization of filters; and spectral estimation. The unique properties of the three-dimensional wave equation are also considered. Subsequent chapters focus on high-resolution spectral estimators; time series analysis of geophysical inverse scattering problems; minimum entropy deconvolution; and fitting of a continuous time autoregression to discrete data. This monograph will appeal to students and practitioners in the fields of mathematics and statistics, electrical and electronics engineering, and information and computer sciences.
Download or read book New Introduction to Multiple Time Series Analysis written by Helmut Lütkepohl and published by Springer Science & Business Media. This book was released on 2007-07-26 with total page 792 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the new and totally revised edition of Lütkepohl’s classic 1991 work. It provides a detailed introduction to the main steps of analyzing multiple time series, model specification, estimation, model checking, and for using the models for economic analysis and forecasting. The book now includes new chapters on cointegration analysis, structural vector autoregressions, cointegrated VARMA processes and multivariate ARCH models. The book bridges the gap to the difficult technical literature on the topic. It is accessible to graduate students in business and economics. In addition, multiple time series courses in other fields such as statistics and engineering may be based on it.
Download or read book Extracting Knowledge From Time Series written by Boris P. Bezruchko and published by Springer Science & Business Media. This book was released on 2010-09-03 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical modelling is ubiquitous. Almost every book in exact science touches on mathematical models of a certain class of phenomena, on more or less speci?c approaches to construction and investigation of models, on their applications, etc. As many textbooks with similar titles, Part I of our book is devoted to general qu- tions of modelling. Part II re?ects our professional interests as physicists who spent much time to investigations in the ?eld of non-linear dynamics and mathematical modelling from discrete sequences of experimental measurements (time series). The latter direction of research is known for a long time as “system identi?cation” in the framework of mathematical statistics and automatic control theory. It has its roots in the problem of approximating experimental data points on a plane with a smooth curve. Currently, researchers aim at the description of complex behaviour (irregular, chaotic, non-stationary and noise-corrupted signals which are typical of real-world objects and phenomena) with relatively simple non-linear differential or difference model equations rather than with cumbersome explicit functions of time. In the second half of the twentieth century, it has become clear that such equations of a s- ?ciently low order can exhibit non-trivial solutions that promise suf?ciently simple modelling of complex processes; according to the concepts of non-linear dynamics, chaotic regimes can be demonstrated already by a third-order non-linear ordinary differential equation, while complex behaviour in a linear model can be induced either by random in?uence (noise) or by a very high order of equations.
Download or read book Time Series Analysis and Applications to Geophysical Systems written by David Brillinger and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Part of a two volume set based on a recent IMA program of the same name. The goal of the program and these books is to develop a community of statistical and other scientists kept up-to-date on developments in this quickly evolving and interdisciplinary field. Consequently, these books present recent material by distinguished researchers. Topics discussed in Part I include nonlinear and non- Gaussian models and processes (higher order moments and spectra, nonlinear systems, applications in astronomy, geophysics, engineering, and simulation) and the interaction of time series analysis and statistics (information model identification, categorical valued time series, nonparametric and semiparametric methods). Self-similar processes and long-range dependence (time series with long memory, fractals, 1/f noise, stable noise) and time series research common to engineers and economists (modeling of multivariate and possibly non-stationary time series, state space and adaptive methods) are discussed in Part II.
Download or read book Forecasting Structural Time Series Models and the Kalman Filter written by Andrew C. Harvey and published by Cambridge University Press. This book was released on 1990-02-22 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.
Download or read book Periodic Time Series Models written by Philip Hans Franses and published by OUP Oxford. This book was released on 2004-03-25 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book considers periodic time series models for seasonal data, characterized by parameters that differ across the seasons, and focuses on their usefulness for out-of-sample forecasting. Providing an up-to-date survey of the recent developments in periodic time series, the book presents a large number of empirical results. The first part of the book deals with model selection, diagnostic checking and forecasting of univariate periodic autoregressive models. Tests for periodic integration, are discussed, and an extensive discussion of the role of deterministic regressors in testing for periodic integration and in forecasting is provided. The second part discusses multivariate periodic autoregressive models. It provides an overview of periodic cointegration models, as these are the most relevant. This overview contains single-equation type tests and a full-system approach based on generalized method of moments. All methods are illustrated with extensive examples, and the book will be of interest to advanced graduate students and researchers in econometrics, as well as practitioners looking for an understanding of how to approach seasonal data.
Download or read book State Space Modeling of Time Series written by Masanao Aoki and published by Springer. This book was released on 1990 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Time Series Models written by Andrew C. Harvey and published by . This book was released on 1981 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stationary stochastic process and their properties in the time domain; The frequency domain; State space models and the kalman filter; Estimation of autoregressive moving average models; Model building and prediction; Selected topics in time series regression.
