ARCH Models and Financial Applications:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
1997
|
| Schriftenreihe: | Springer Series in Statistics
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 1.1 The DevelopmentofARCH Models Time series models have been initially introduced either for descriptive purposes like prediction and seasonal correction or for dynamic control. In the 1970s, the researchfocusedonaspecificclassoftimeseriesmodels,theso-calledautoregres sive moving average processes (ARMA), which were very easy to implement. In thesemodels,thecurrentvalueoftheseriesofinterestiswrittenasalinearfunction ofits own laggedvalues andcurrentandpastvaluesofsomenoiseprocess, which can be interpreted as innovations to the system. However, this approach has two major drawbacks: 1) it is essentially a linear setup, which automatically restricts the type of dynamics to be approximated; 2) it is generally applied without im posing a priori constraintson the autoregressive and moving average parameters, which is inadequatefor structural interpretations. Among the field ofapplications where standard ARMA fit is poorare financial and monetary problems. The financial time series features various forms ofnon lineardynamics,the crucialone being the strongdependenceofthe instantaneous variabilityoftheseriesonitsownpast. Moreover,financial theoriesbasedoncon ceptslikeequilibriumorrationalbehavioroftheinvestorswouldnaturallysuggest including and testing some structural constraints on the parameters. In this con text, ARCH (Autoregressive Conditionally Heteroscedastic) models, introduced by Engle (1982), arise as an appropriate framework for studying these problems. Currently, there existmorethan onehundredpapers and some dozenPh.D. theses on this topic, which reflects the importance ofthis approach for statistical theory, finance and empirical work. 2 1. Introduction From the viewpoint ofstatistical theory, the ARCH models may be considered as some specific nonlinear time series models, which allow for aquite exhaustive studyoftheunderlyingdynamics.Itisthereforepossibletoreexamineanumberof classicalquestions like the random walkhypothesis, prediction intervals building, presenceoflatentvariables [factors] etc., and to test the validity ofthe previously established results |
| Beschreibung: | 1 Online-Ressource (IX, 229 p) |
| ISBN: | 9781461218609 9781461273141 |
| ISSN: | 0172-7397 |
| DOI: | 10.1007/978-1-4612-1860-9 |
Internformat
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| 500 | |a 1.1 The DevelopmentofARCH Models Time series models have been initially introduced either for descriptive purposes like prediction and seasonal correction or for dynamic control. In the 1970s, the researchfocusedonaspecificclassoftimeseriesmodels,theso-calledautoregres sive moving average processes (ARMA), which were very easy to implement. In thesemodels,thecurrentvalueoftheseriesofinterestiswrittenasalinearfunction ofits own laggedvalues andcurrentandpastvaluesofsomenoiseprocess, which can be interpreted as innovations to the system. However, this approach has two major drawbacks: 1) it is essentially a linear setup, which automatically restricts the type of dynamics to be approximated; 2) it is generally applied without im posing a priori constraintson the autoregressive and moving average parameters, which is inadequatefor structural interpretations. Among the field ofapplications where standard ARMA fit is poorare financial and monetary problems. | ||
| 500 | |a The financial time series features various forms ofnon lineardynamics,the crucialone being the strongdependenceofthe instantaneous variabilityoftheseriesonitsownpast. Moreover,financial theoriesbasedoncon ceptslikeequilibriumorrationalbehavioroftheinvestorswouldnaturallysuggest including and testing some structural constraints on the parameters. In this con text, ARCH (Autoregressive Conditionally Heteroscedastic) models, introduced by Engle (1982), arise as an appropriate framework for studying these problems. Currently, there existmorethan onehundredpapers and some dozenPh.D. theses on this topic, which reflects the importance ofthis approach for statistical theory, finance and empirical work. 2 1. | ||
| 500 | |a Introduction From the viewpoint ofstatistical theory, the ARCH models may be considered as some specific nonlinear time series models, which allow for aquite exhaustive studyoftheunderlyingdynamics.Itisthereforepossibletoreexamineanumberof classicalquestions like the random walkhypothesis, prediction intervals building, presenceoflatentvariables [factors] etc., and to test the validity ofthe previously established results | ||
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| isbn | 9781461218609 9781461273141 |
| issn | 0172-7397 |
| language | English |
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| spelling | Gouriéroux, Christian Verfasser aut ARCH Models and Financial Applications by Christian Gouriéroux New York, NY Springer New York 1997 1 Online-Ressource (IX, 229 p) txt rdacontent c rdamedia cr rdacarrier Springer Series in Statistics 0172-7397 1.1 The DevelopmentofARCH Models Time series models have been initially introduced either for descriptive purposes like prediction and seasonal correction or for dynamic control. In the 1970s, the researchfocusedonaspecificclassoftimeseriesmodels,theso-calledautoregres sive moving average processes (ARMA), which were very easy to implement. In thesemodels,thecurrentvalueoftheseriesofinterestiswrittenasalinearfunction ofits own laggedvalues andcurrentandpastvaluesofsomenoiseprocess, which can be interpreted as innovations to the system. However, this approach has two major drawbacks: 1) it is essentially a linear setup, which automatically restricts the type of dynamics to be approximated; 2) it is generally applied without im posing a priori constraintson the autoregressive and moving average parameters, which is inadequatefor structural interpretations. Among the field ofapplications where standard ARMA fit is poorare financial and monetary problems. The financial time series features various forms ofnon lineardynamics,the crucialone being the strongdependenceofthe instantaneous variabilityoftheseriesonitsownpast. Moreover,financial theoriesbasedoncon ceptslikeequilibriumorrationalbehavioroftheinvestorswouldnaturallysuggest including and testing some structural constraints on the parameters. In this con text, ARCH (Autoregressive Conditionally Heteroscedastic) models, introduced by Engle (1982), arise as an appropriate framework for studying these problems. Currently, there existmorethan onehundredpapers and some dozenPh.D. theses on this topic, which reflects the importance ofthis approach for statistical theory, finance and empirical work. 2 1. Introduction From the viewpoint ofstatistical theory, the ARCH models may be considered as some specific nonlinear time series models, which allow for aquite exhaustive studyoftheunderlyingdynamics.Itisthereforepossibletoreexamineanumberof classicalquestions like the random walkhypothesis, prediction intervals building, presenceoflatentvariables [factors] etc., and to test the validity ofthe previously established results Statistics Economics / Statistics Economics Statistics for Business/Economics/Mathematical Finance/Insurance Economic Theory Statistik Wirtschaft ARCH-Prozess (DE-588)4346437-3 gnd rswk-swf Kapitalmarkttheorie (DE-588)4137411-3 gnd rswk-swf Kapitalmarkttheorie (DE-588)4137411-3 s ARCH-Prozess (DE-588)4346437-3 s 1\p DE-604 https://doi.org/10.1007/978-1-4612-1860-9 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Gouriéroux, Christian ARCH Models and Financial Applications Statistics Economics / Statistics Economics Statistics for Business/Economics/Mathematical Finance/Insurance Economic Theory Statistik Wirtschaft ARCH-Prozess (DE-588)4346437-3 gnd Kapitalmarkttheorie (DE-588)4137411-3 gnd |
| subject_GND | (DE-588)4346437-3 (DE-588)4137411-3 |
| title | ARCH Models and Financial Applications |
| title_auth | ARCH Models and Financial Applications |
| title_exact_search | ARCH Models and Financial Applications |
| title_full | ARCH Models and Financial Applications by Christian Gouriéroux |
| title_fullStr | ARCH Models and Financial Applications by Christian Gouriéroux |
| title_full_unstemmed | ARCH Models and Financial Applications by Christian Gouriéroux |
| title_short | ARCH Models and Financial Applications |
| title_sort | arch models and financial applications |
| topic | Statistics Economics / Statistics Economics Statistics for Business/Economics/Mathematical Finance/Insurance Economic Theory Statistik Wirtschaft ARCH-Prozess (DE-588)4346437-3 gnd Kapitalmarkttheorie (DE-588)4137411-3 gnd |
| topic_facet | Statistics Economics / Statistics Economics Statistics for Business/Economics/Mathematical Finance/Insurance Economic Theory Statistik Wirtschaft ARCH-Prozess Kapitalmarkttheorie |
| url | https://doi.org/10.1007/978-1-4612-1860-9 |
| work_keys_str_mv | AT gourierouxchristian archmodelsandfinancialapplications |