Dynamic models for volatility and heavy tails: with applications to financial and economic time series
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 ex...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2013
|
| Schriftenreihe: | Econometric Society monographs
52 |
| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 UBG01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (xviii, 261 pages) |
| ISBN: | 9781139540933 |
| DOI: | 10.1017/CBO9781139540933 |
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| 520 | |a 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 | ||
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Datensatz im Suchindex
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| dewey-ones | 330 - Economics |
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| discipline | Wirtschaftswissenschaften |
| doi_str_mv | 10.1017/CBO9781139540933 |
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| id | DE-604.BV043695589 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:32:44Z |
| institution | BVB |
| isbn | 9781139540933 |
| language | English |
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| spelling | Harvey, Andrew C. 1947- Verfasser (DE-588)121875032 aut Dynamic models for volatility and heavy tails with applications to financial and economic time series Andrew C. Harvey Dynamic Models for Volatility & Heavy Tails Cambridge Cambridge University Press 2013 1 online resource (xviii, 261 pages) txt rdacontent c rdamedia cr rdacarrier Econometric Society monographs 52 Title from publisher's bibliographic system (viewed on 05 Oct 2015) 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 Mathematisches Modell Econometrics Finance / Mathematical models Time-series analysis Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd rswk-swf Dynamisches Modell (DE-588)4150932-8 gnd rswk-swf Nichtlineare Zeitreihenanalyse (DE-588)4276267-4 gnd rswk-swf Nichtlineare Zeitreihenanalyse (DE-588)4276267-4 s Wahrscheinlichkeitsverteilung (DE-588)4121894-2 s Dynamisches Modell (DE-588)4150932-8 s 1\p DE-604 Erscheint auch als Druckausgabe 978-1-107-03472-3 Erscheint auch als Druckausgabe 978-1-107-63002-4 https://doi.org/10.1017/CBO9781139540933 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Harvey, Andrew C. 1947- Dynamic models for volatility and heavy tails with applications to financial and economic time series Mathematisches Modell Econometrics Finance / Mathematical models Time-series analysis Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd Dynamisches Modell (DE-588)4150932-8 gnd Nichtlineare Zeitreihenanalyse (DE-588)4276267-4 gnd |
| subject_GND | (DE-588)4121894-2 (DE-588)4150932-8 (DE-588)4276267-4 |
| title | Dynamic models for volatility and heavy tails with applications to financial and economic time series |
| title_alt | Dynamic Models for Volatility & Heavy Tails |
| title_auth | Dynamic models for volatility and heavy tails with applications to financial and economic time series |
| title_exact_search | Dynamic models for volatility and heavy tails with applications to financial and economic time series |
| title_full | Dynamic models for volatility and heavy tails with applications to financial and economic time series Andrew C. Harvey |
| title_fullStr | Dynamic models for volatility and heavy tails with applications to financial and economic time series Andrew C. Harvey |
| title_full_unstemmed | Dynamic models for volatility and heavy tails with applications to financial and economic time series Andrew C. Harvey |
| title_short | Dynamic models for volatility and heavy tails |
| title_sort | dynamic models for volatility and heavy tails with applications to financial and economic time series |
| title_sub | with applications to financial and economic time series |
| topic | Mathematisches Modell Econometrics Finance / Mathematical models Time-series analysis Wahrscheinlichkeitsverteilung (DE-588)4121894-2 gnd Dynamisches Modell (DE-588)4150932-8 gnd Nichtlineare Zeitreihenanalyse (DE-588)4276267-4 gnd |
| topic_facet | Mathematisches Modell Econometrics Finance / Mathematical models Time-series analysis Wahrscheinlichkeitsverteilung Dynamisches Modell Nichtlineare Zeitreihenanalyse |
| url | https://doi.org/10.1017/CBO9781139540933 |
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