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Author: Marc S. Paolella
Publisher:
ISBN: 9783980599313
Category :
Languages : en
Pages : 117
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Book Description
Author: Marc S. Paolella
Publisher:
ISBN: 9783980599313
Category :
Languages : en
Pages : 117
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Book Description
Author: Daniel Straumann
Publisher: Springer Science & Business Media
ISBN: 9783540211358
Category : Business & Economics
Languages : en
Pages : 254
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Book Description
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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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.
Author: Daniel Straumann
Publisher: Springer Science & Business Media
ISBN: 3540269789
Category : Business & Economics
Languages : en
Pages : 239
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Book Description
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 Nowadays ARCH has been replaced by more general and more sophisticated models, such as GARCH (generalized autoregressive heteroscedastic).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.
Author: Guodong Li
Publisher: Open Dissertation Press
ISBN: 9781374672758
Category :
Languages : en
Pages :
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Book Description
This dissertation, "On Some Nonlinear Time Series Models and the Least Absolute Deviation Estimation" by Guodong, Li, 李國棟, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled ON SOME NONLINEAR TIME SERIES MODELS AND THE LEAST ABSOLUTE DEVIATION ESTIMATION Submitted by LI GUODONG for the degree of Doctor of Philosophy at The University of Hong Kong in June 2007 This study investigated some testing and estimating problems for time series models with conditional heteroscedasticity. Some new statistical tools were de- velopedwhichmightprovidenewinsightsintotheunderstandingofnancialand economic time series. Empirical evidences showed that many nancial and economic data may be heavy-tailed and, as a robust statistical approach, the least absolute deviation estimation had recently become popular in the modeling of time series exhibiting this phenomenon. Two useful diagnostic tools, based on the asymptotic distribu- tions of absolute residual autocorrelations and squared residual autocorrelations, weredevelopedinthisthesistocheckwhetherageneralizedautoregressivecondi- tional heteroscedastic (GARCH) model estimated by the least absolute deviationmethod was adequate or not. Secondly, as the long memory property was known tobepresentinsomeabsolutereturnsequencesinnanceandeconomics, besides heavy tails and time varying conditional variance, a least absolute deviation ap- proachwasdevelopedtoestimatethisphenomenonbasedonthefractionallyinte- grated autoregressive moving average models with conditional heteroscedasticity. Statisticalpropertiesfortheestimatorssuchaslocalasymptoticnormalitieswere derived. Thirdly, as the phenomena of unit roots and heavy tails usually coexist in the same time series, it was clearly necessary to construct a powerful test to identify the presence of unit roots under heavy tails. A least absolute deviation estimation was considered for the unit root processes with GARCH errors, and severalrobustunitroottestswerederivedbasedonthisestimation. Fourthly, the threshold model has become a standard class of nonlinear time series models. An important problem in this literature was to test whether a threshold time series model provided a better t to the real data than a model without a threshold. A quasi-likelihood ratio test was therefore designed to check for the existence of the threshold structure in moving average models under changing conditional variance. MonteCarloexperimentswereconductedtodemonstratetheusefulnessofthe theoriesandmethodsdevelopedabove. ApplicationstotheHangSengIndex, the Dow Jones Industrial Average Index, the S&P 500 index and the exchange rate of Japanese Yen and US dollar provided some new insights into these time series. DOI: 10.5353/th_b3878239 Subjects: Heteroscedasticity Time series analysis
Author: S.T Rachev
Publisher: Elsevier
ISBN: 0080557732
Category : Business & Economics
Languages : en
Pages : 707
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Book Description
The Handbooks in Finance are intended to be a definitive source for comprehensive and accessible information in the field of finance. Each individual volume in the series should present an accurate self-contained survey of a sub-field of finance, suitable for use by finance and economics professors and lecturers, professional researchers, graduate students and as a teaching supplement. The goal is to have a broad group of outstanding volumes in various areas of finance. The Handbook of Heavy Tailed Distributions in Finance is the first handbook to be published in this series. This volume presents current research focusing on heavy tailed distributions in finance. The contributions cover methodological issues, i.e., probabilistic, statistical and econometric modelling under non- Gaussian assumptions, as well as the applications of the stable and other non -Gaussian models in finance and risk management.
Author: 黃香
Publisher:
ISBN: 9781374775442
Category :
Languages : en
Pages :
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Book Description
This dissertation, "Topics in Conditional Heteroscedastic Time Series Modelling" by 黃香, Heung, Wong, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. DOI: 10.5353/th_b3123451 Subjects: Autoregression (Statistics) Heteroscedasticity Time-series analysis
Author: Yao Zheng
Publisher:
ISBN:
Category :
Languages : en
Pages : 39
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Book Description
The estimation for time series models with heavy-tailed innovations has been widely discussed in the literature, while the corresponding goodness-of-fit tests have attracted less attention. This is mainly because the commonly used autocorrelation function in constructing goodness-of-fit tests necessarily imposes certain moment conditions on the innovations. In the light of the fact that a bounded random variable has finite moments of all orders, we address this problem by first transforming the residuals with a bounded and increasing function. Specifically, this paper considers the autocorrelation function of the transformed absolute residuals from a fitted GARCH model. With the corresponding residual empirical distribution function naturally employed as the transformation, a robust goodness-of-fit test is constructed. The asymptotic null distribution of the test statistic is derived, and simulation experiments are conducted to assess its finite-sample performance. A real data example is analyzed to further illustrate its usefulness.
Author: Daniel Peña
Publisher: John Wiley & Sons
ISBN: 1118031229
Category : Mathematics
Languages : en
Pages : 494
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Book Description
New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: Contributions from eleven of the worldâ??s leading figures in time series Shared balance between theory and application Exercise series sets Many real data examples Consistent style and clear, common notation in all contributions 60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.
Author:
Publisher:
ISBN:
Category : Industrialization
Languages : en
Pages : 694
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Book Description
Author: Heung Wong
Publisher:
ISBN:
Category : Autoregression (Statistics)
Languages : en
Pages : 246
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Book Description
