DYNAMIC MODELS FOR VOLATILITY AND HEAVY TAILS by Andrew C. Harvey ( 9781107034723 )

Item Details

Title:

DYNAMIC MODELS FOR VOLATILITY AND HEAVY TAILS
WITH APPLICATIONS TO FINANCIAL AND ECONOMIC TIME SERIES

By:

Andrew C. Harvey

Format:

Hardback

List price:

£95.00

Our price:

£85.50

Discount:

10% off

You save:

£9.50

ISBN 10:

1107034728

ISBN 13:

9781107034723

Availability:

Usually dispatched within 1-3 weeks.

Delivery rates

Stock:

Currently 0 available

Publisher:

CAMBRIDGE UNIVERSITY PRESS

Pub. date:

22 April, 2013

Series:

Econometric Society Monographs 52

Pages:

282

Description:

The book presents a statistical theory for a class of nonlinear time-series models. It will be of interest to econometricians and statisticians.

Synopsis:

The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling.

Illustrations:

43 b/w illus. 14 tables

Publication:

Imprint:

Cambridge University Press

Returns:

Returnable

Some other items by this author:

DYNAMIC MODELS FOR VOLATILITY AND HEAVY TAILS (PB)

FORECASTING, STRUCTURAL TIME SERIES MODELS AND THE KALMAN FILTER (HB)

FORECASTING, STRUCTURAL TIME SERIES MODELS AND THE KALMAN FILTER (PB)

STRUCTURAL TIME SERIES ANALYSER, MODELLER AND PREDICTOR (PB)