• Sign In / Register
  • Help
  • Basket0
ARCH Models and Financial Applications - Springer Series in Statistics (Paperback)
  • ARCH Models and Financial Applications - Springer Series in Statistics (Paperback)
zoom

ARCH Models and Financial Applications - Springer Series in Statistics (Paperback)

(author)
£123.00
Paperback 229 Pages / Published: 06/10/2012
  • We can order this

Usually despatched within 3 weeks

  • This item has been added to your basket
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.

Publisher: Springer-Verlag New York Inc.
ISBN: 9781461273141
Number of pages: 229
Weight: 379 g
Dimensions: 235 x 155 x 13 mm
Edition: Softcover reprint of the original 1st ed. 199


MEDIA REVIEWS

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."

You may also be interested in...

Cartoon Guide to Statistics
Added to basket
Introduction to Probability
Added to basket
Statistics Essentials for Dummies
Added to basket
Psychology Statistics For Dummies
Added to basket
Time Series Analysis
Added to basket
Statistics without Tears
Added to basket
Principles of Statistics
Added to basket
Statistics II for Dummies
Added to basket
The Signal and the Noise
Added to basket
£10.99   £8.99
Paperback
The R Primer
Added to basket
£34.99
Paperback
Probability
Added to basket
£24.99
Paperback
Risk
Added to basket
£12.99
Paperback

Reviews

Please sign in to write a review

Your review has been submitted successfully.