ARCH Models and Financial Applications

Language: English

Published by Springer New York Apr 1997, 1997

0387948767 / 9780387948768

Series: Book 35 of 160 - Springer Series in Statistics

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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,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. 244 pp. Englisch.…

Seller Inventory # 9780387948768

Title
ARCH Models and Financial Applications
Author
Christian Gourieroux
Publisher
Springer New York Apr 1997
Publication year
1997
Condition
Neu
Binding
Buch
Language
English
ISBN 10
0387948767
ISBN 13
9780387948768
Item weight
535 grams
Dimensions
241x160x19 mm
Series
Book 35 of 160: Springer Series in Statistics

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

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BuchWeltWeit Ludwig Meier e.K.

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