Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series - Rilegato
Libro 14 di 25: Econometric Society Monographs
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The book presents a statistical theory for a class of nonlinear time-series models. It will be of interest to econometricians and statisticians.
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Informazioni sull?autore
Andrew Harvey is Professor of Econometrics at the University of Cambridge and a Fellow of Corpus Christi College. He is a Fellow of the Econometric Society and of the British Academy. He has published more than one hundred articles in journals and edited volumes and is the author of three books, The Econometric Analysis of Time Series, Time Series Models, and Forecasting and Structural Time Series Models and the Kalman Filter (Cambridge University Press, 1989). He is one of the developers of the STAMP computer package.
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Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series (Econometric Society Monographs, Series Number 52)
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Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series (Econometric Society Monographs, Series Number 52)
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Dynamic Models for Volatility and Heavy Tails : With Applications to Financial and Economic Time Series
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Cambridge University Press, 2013
ISBN 10: 1107034728
ISBN 13: 9781107034723
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Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series
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Dynamic Models for Volatility and Heavy Tails (Hardcover)
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Cambridge University Press, Cambridge, 2013
ISBN 10: 1107034728
ISBN 13: 9781107034723
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Hardcover. Condizione: new. Hardcover. The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling. This book presents a statistical theory for a class of nonlinear time-series models. It has particular relevance for the modeling of volatility in financial time series but the overall approach will be of interest to econometricians and statisticians in a variety of disciplines. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781107034723
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Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series
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Dynamic Models for Volatility and Heavy Tails : With Applications to Financial and Economic Time Series
Editore:
Cambridge University Press, 2013
ISBN 10: 1107034728
ISBN 13: 9781107034723
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Dynamic Models for Volatility and Heavy Tails (Hardcover)
Editore:
Cambridge University Press, Cambridge, 2013
ISBN 10: 1107034728
ISBN 13: 9781107034723
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Hardcover. Condizione: new. Hardcover. The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling. This book presents a statistical theory for a class of nonlinear time-series models. It has particular relevance for the modeling of volatility in financial time series but the overall approach will be of interest to econometricians and statisticians in a variety of disciplines. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9781107034723
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