Download or read book Tail Index Estimation for a Filtered Dependent Time Series written by Jonathan B. Hill and published by . This book was released on 2015 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt: We prove Hill's (1975) tail index estimator is asymptotically normal where the employed data are generated by a stationary parametric process {x(t)}. We assume x(t) is an unobservable function of a parameter q that is estimable. Natural applications include regression residuals and GARCH filters. Our main result extends Resnick and Stărică's (1997) theory for estimated AR i.i.d. errors and Ling and Peng's (2004) theory for estimated ARMA i.i.d. errors to a wide range of filtered time series since we do not require x(t) to be i.i.d., nor generated by a linear process with geometric dependence. We assume x(t) is b-mixing with possibly hyperbolic dependence, covering ARMA-GARCH filters, ARMA filters with heteroscedastic errors of unknown form, nonlinear filters like threshold autoregressions, and filters based on mis-specified models, as well as i.i.d. errors in an ARMA model. Finally, as opposed to existing results we do not require the plug-in for q to be super-n1/2-convergent when x(t) has an infinite variance allowing a far greater variety of plug-ins including those that are slower than n1/2 , like QML-type estimators for GARCH models.
Download or read book Statistical Methodologies written by Jan Peter Hessling and published by BoD – Books on Demand. This book was released on 2020-02-26 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical practices have recently been questioned by numerous independent authors, to the extent that a significant fraction of accepted research findings can be questioned. This suggests that statistical methodologies may have gone too far into an engineering practice, with minimal concern for their foundation, interpretation, assumptions, and limitations, which may be jeopardized in the current context. Disguised by overwhelming data sets, advanced processing, and stunning presentations, the basic approach is often intractable to anyone but the analyst. The hierarchical nature of statistical inference, exemplified by Bayesian aggregation of prior and derived knowledge, may also be challenging. Conceptual simplified studies of the kind presented in this book could therefore provide valuable guidance when developing statistical methodologies, but also applying state of the art with greater confidence.
Download or read book Tail Estimation and Conditional Modeling of Heteroscedastic Time Series written by Marc S. Paolella and published by . This book was released on 1999 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Adaptive Optimal Estimate of the Tail Index for MA 1 Time Series written by J. L. Geluk and published by . This book was released on 1999 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Heavy Tailed Distributions and Robustness in Economics and Finance written by Marat Ibragimov and published by Springer. This book was released on 2015-05-23 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on general frameworks for modeling heavy-tailed distributions in economics, finance, econometrics, statistics, risk management and insurance. A central theme is that of (non-)robustness, i.e., the fact that the presence of heavy tails can either reinforce or reverse the implications of a number of models in these fields, depending on the degree of heavy-tailed ness. These results motivate the development and applications of robust inference approaches under heavy tails, heterogeneity and dependence in observations. Several recently developed robust inference approaches are discussed and illustrated, together with applications.
Download or read book Weak Dependence With Examples and Applications written by Jérome Dedecker and published by Springer Science & Business Media. This book was released on 2007-07-29 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops Doukhan/Louhichi's 1999 idea to measure asymptotic independence of a random process. The authors, who helped develop this theory, propose examples of models fitting such conditions: stable Markov chains, dynamical systems or more complicated models, nonlinear, non-Markovian, and heteroskedastic models with infinite memory. Applications are still needed to develop a method of analysis for nonlinear times series, and this book provides a strong basis for additional studies.
Download or read book Prediction and Nonparametric Estimation for Time Series with Heavy Tails written by Peter Hall and published by . This book was released on 2004 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated by prediction problems for time series with heavy-tailed marginal distributions, we consider methods based on 'local least absolute deviations' for estimating a regression median from dependent data. Unlike more conventional 'local median' methods, which are in effect based on locally fitting a polynomial of degree 0, techniques founded on local least absolute deviations have quadratic bias right up to the boundary of the design interval. Also in contrast to local least-squares methods based on linear fits, the order of magnitude of variance does not depend on tail-weight of the error distribution. To make these points clear, we develop theory describing local applications to time series of both least-squares and least-absolute-deviations methods, showing for example that, in the case of heavy-tailed data, the conventional local-linear least-squares estimator suffers from an additional bias term as well as increased variance.
Download or read book Tail Index and Quantile Estimation with Very High Frequency Data written by Casper G. de Vries and published by . This book was released on 1996 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Theory and Applications of Time Series Analysis written by Olga Valenzuela and published by Springer Nature. This book was released on 2020-11-20 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a selection of peer-reviewed contributions on the latest advances in time series analysis, presented at the International Conference on Time Series and Forecasting (ITISE 2019), held in Granada, Spain, on September 25-27, 2019. The first two parts of the book present theoretical contributions on statistical and advanced mathematical methods, and on econometric models, financial forecasting and risk analysis. The remaining four parts include practical contributions on time series analysis in energy; complex/big data time series and forecasting; time series analysis with computational intelligence; and time series analysis and prediction for other real-world problems. Given this mix of topics, readers will acquire a more comprehensive perspective on the field of time series analysis and forecasting. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics, statistics and econometrics.
Download or read book Time Series and Panel Data Econometrics written by M. Hashem Pesaran and published by Oxford University Press. This book was released on 2015 with total page 1095 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is concerned with recent developments in time series and panel data techniques for the analysis of macroeconomic and financial data. It provides a rigorous, nevertheless user-friendly, account of the time series techniques dealing with univariate and multivariate time series models, as well as panel data models. It is distinct from other time series texts in the sense that it also covers panel data models and attempts at a more coherent integration of time series, multivariate analysis, and panel data models. It builds on the author's extensive research in the areas of time series and panel data analysis and covers a wide variety of topics in one volume. Different parts of the book can be used as teaching material for a variety of courses in econometrics. It can also be used as reference manual. It begins with an overview of basic econometric and statistical techniques, and provides an account of stochastic processes, univariate and multivariate time series, tests for unit roots, cointegration, impulse response analysis, autoregressive conditional heteroskedasticity models, simultaneous equation models, vector autoregressions, causality, forecasting, multivariate volatility models, panel data models, aggregation and global vector autoregressive models (GVAR). The techniques are illustrated using Microfit 5 (Pesaran and Pesaran, 2009, OUP) with applications to real output, inflation, interest rates, exchange rates, and stock prices.
Download or read book Non Linear Time Series written by Kamil Feridun Turkman and published by Springer. This book was released on 2014-09-29 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a useful combination of probabilistic and statistical tools for analyzing nonlinear time series. Key features of the book include a study of the extremal behavior of nonlinear time series and a comprehensive list of nonlinear models that address different aspects of nonlinearity. Several inferential methods, including quasi likelihood methods, sequential Markov Chain Monte Carlo Methods and particle filters, are also included so as to provide an overall view of the available tools for parameter estimation for nonlinear models. A chapter on integer time series models based on several thinning operations, which brings together all recent advances made in this area, is also included. Readers should have attended a prior course on linear time series, and a good grasp of simulation-based inferential methods is recommended. This book offers a valuable resource for second-year graduate students and researchers in statistics and other scientific areas who need a basic understanding of nonlinear time series.
Download or read book Financial Econometrics written by Oliver Linton and published by Cambridge University Press. This book was released on 2019-02-21 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a thorough exploration of the models and methods of financial econometrics by one of the world's leading financial econometricians and is for students in economics, finance, statistics, mathematics, and engineering who are interested in financial applications. Based on courses taught around the world, the up-to-date content covers developments in econometrics and finance over the last twenty years while ensuring a solid grounding in the fundamental principles of the field. Care has been taken to link theory and application to provide real-world context for students. Worked exercises and empirical examples have also been included to make sure complicated concepts are solidly explained and understood.
Download or read book Tail Index Estimation written by Jón Daníelsson and published by . This book was released on 2019 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The selection of upper order statistics in tail estimation is notoriously difficult. Methods that are based on asymptotic arguments, like minimizing the asymptotic MSE, do not perform well in finite samples. Here, we advance a data-driven method that minimizes the maximum distance between the fitted Pareto type tail and the observed quantile. To analyze the finite sample properties of the metric, we perform rigorous simulation studies. In most cases, the finite sample-based methods perform best. To demonstrate the economic relevance of choosing the proper methodology, we use daily equity return data from the CRSP database and find economically relevant variation between the tail index estimates"--Abstract.
Download or read book Statistica Sinica written by and published by . This book was released on 2003 with total page 748 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Asymmetric Tail Dependence in Asian Developed Futures Markets written by Qing Xu and published by . This book was released on 2007 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper employs three two-parameter Archimedean copulas (BB1, BB4, and BB7) to investigate dynamic asymmetric tail dependence in Asian developed futures markets over the post-crisis period. The estimation is consistent and asymptotic with a careful implementation of the two-stage method. Unlike previous empirical research, we first let each marginal model follow a conditional skewed-t distribution. Based on robust inference for dynamic marginal models, it is found that higher moments of each filtered index return series are significantly time-dependent. We then extend those three two-parameter copulas incorporating time-varying tail dependence to capture dynamic asymmetries. The estimation results of the copulas provide strong evidence of asymmetric tail dependence in Asian developed futures markets. Moreover, based on the goodness-of-fit tests, we find that the model BB7 is the optimal one. The model's results suggest that the probability of dependence in bear markets is higher than in bull markets in the post-crisis period. This further confirms downside dependent risk in Asian developed futures markets. Our empirical findings provide a basis for hedging downside dependent risk, and thus make a contribution to the literature of financial risk management.
Download or read book Introductory Econometrics for Finance written by Chris Brooks and published by Cambridge University Press. This book was released on 2019-03-28 with total page 729 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers econometrics for finance students with no prior knowledge of the field. Includes case studies, examples and extensive online support.
Download or read book Nonlinear Time Series written by Jianqing Fan and published by Springer Science & Business Media. This book was released on 2008-09-11 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book that integrates useful parametric and nonparametric techniques with time series modeling and prediction, the two important goals of time series analysis. Such a book will benefit researchers and practitioners in various fields such as econometricians, meteorologists, biologists, among others who wish to learn useful time series methods within a short period of time. The book also intends to serve as a reference or text book for graduate students in statistics and econometrics.
