Go back

Arch Models And Financial Applications(1997th Edition)

Authors:

Christian Gourieroux

Free arch models and financial applications 1997th edition christian gourieroux 0387948767, 978-0387948768
15 ratings
Cover Type:Hardcover
Condition:Used

In Stock

Shipment time

Expected shipping within 2 Days
Access to 30 Million+ solutions Free
Ask 50 Questions from expert AI-Powered Answers
7 days-trial

Total Price:

$0

List Price: $29.49 Savings: $29.49(100%)
Access to 30 Million+ solutions
Ask 50 Questions from expert AI-Powered Answers 24/7 Tutor Help Detailed solutions for Arch Models And Financial Applications

Price:

$9.99

/month

Book details

ISBN: 0387948767, 978-0387948768

Book publisher: Springer

Get your hands on the best-selling book Arch Models And Financial Applications 1997th Edition for free. Feed your curiosity and let your imagination soar with the best stories coming out to you without hefty price tags. Browse SolutionInn to discover a treasure trove of fiction and non-fiction books where every page leads the reader to an undiscovered world. Start your literary adventure right away and also enjoy free shipping of these complimentary books to your door.

Book Summary: 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.