Verwandte Artikel zu Arch Models and Financial Applications (Springer Series...
Inhaltsangabe
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.
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Críticas
From the reviews:
RISKBOOK.COM
"Gourieroux offers a nice balance of theory and application in this book on ARCH modeling in finance...The book is well written and has extensive references. Its focus on finance will appeal to financial engineers and financial risk managers."
Reseña del editor
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.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
Gebraucht kaufen
Zustand: BefriedigendConnecting readers with great books...
Diesen Artikel anzeigen
EUR 12,70
EUR 3,19 für den Versand innerhalb von/der USA
Versandziele, Kosten & DauerNeu kaufen
Diesen Artikel anzeigen
EUR 88,43
EUR 5,92 für den Versand innerhalb von/der USA
Versandziele, Kosten & Dauer
Suchergebnisse für Arch Models and Financial Applications (Springer Series...
Beispielbild für diese ISBN
ARCH Models and Financial Applications (Springer Series in Statistics)
Anbieter: HPB-Red, Dallas, TX, USA
Verkäuferbewertung 5 von 5 Sternen
hardcover. Zustand: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority! Bestandsnummer des Verkäufers S_363186464
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
ARCH Models and Financial Applications
Anbieter: Treehorn Books, Santa Rosa, CA, USA
Verkäuferbewertung 4 von 5 Sternen
Hardcover. Zustand: Very Good. Zustand des Schutzumschlags: No Dust Jacket. Springer Series In Statistics; 9.3 X 6.1 X 0.8 inches; 229 pages. Bestandsnummer des Verkäufers 8635
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
ARCH Models and Financial Applications
Anbieter: Anybook.com, Lincoln, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,550grams, ISBN:9780387948768. Bestandsnummer des Verkäufers 9313710
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
ARCH Models and Financial Applications (Springer Series in Statistics)
Anbieter: medimops, Berlin, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Zustand: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present. Bestandsnummer des Verkäufers M00387948767-G
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
ARCH Models and Financial Applications (Springer Series in Statistics)
Anbieter: BennettBooksLtd, San Diego, NV, USA
Verkäuferbewertung 5 von 5 Sternen
hardcover. Zustand: New. In shrink wrap. Looks like an interesting title! Bestandsnummer des Verkäufers Q-0387948767
Anzahl: 1 verfügbar
Beispielbild für diese ISBN
ARCH Models and Financial Applications (Springer Series in Statistics)
Anbieter: Best Price, Torrance, CA, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. SUPER FAST SHIPPING. Bestandsnummer des Verkäufers 9780387948768
Anzahl: 2 verfügbar
Beispielbild für diese ISBN
ARCH Models and Financial Applications (Springer Series in Statistics)
Anbieter: Lucky's Textbooks, Dallas, TX, USA
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. Bestandsnummer des Verkäufers ABLIING23Feb2215580174264
Anzahl: Mehr als 20 verfügbar
Beispielbild für diese ISBN
ARCH Models and Financial Applications (Springer Series in Statistics)
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
Verkäuferbewertung 5 von 5 Sternen
Zustand: New. In. Bestandsnummer des Verkäufers ria9780387948768_new
Anzahl: Mehr als 20 verfügbar
Foto des Verkäufers
ARCH Models and Financial Applications
Print-on-DemandAnbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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,thecurrentvalueoftheseriesofinterestiswrittenasalinea rfunction 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.Itisthereforepossibletoreexamineanumb erof classicalquestions like the random walkhypothesis, prediction intervals building, presenceoflatentvariables [factors] etc., and to test the validity ofthe previously established results. 244 pp. Englisch. Bestandsnummer des Verkäufers 9780387948768
Anzahl: 2 verfügbar
Beispielbild für diese ISBN
Arch Models and Financial Applications
Anbieter: Books Puddle, New York, NY, USA
Verkäuferbewertung 4 von 5 Sternen
Zustand: New. pp. 244. Bestandsnummer des Verkäufers 26315589
Anzahl: 1 verfügbar