×

Uh-oh, it looks like your Internet Explorer is out of date.

For a better shopping experience, please upgrade now.

Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series
     

Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series

by Andrew C. Harvey
 

See All Formats & Editions

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

Overview

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, such as those arising from data on the range of returns and the time between trades. Furthermore, 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. As such, there are applications not only to financial data but also to macroeconomic time series and to time series in other disciplines. 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. The practical value of the proposed models is illustrated by fitting them to real data sets.

Editorial Reviews

From the Publisher
"It offers a comprehensive view of DCS models and is self-contained in that it includes the necessary statistical theory for understanding and applying them. Empirical examples help the reader appreciate the potential of these models."
Journal of Economic Literature

"Besides being invaluable to researchers in time series, the book will be of immense help to practitioners, particularly in the fields of econometrics and finance."
Sugata Sen Roy, Mathematical Reviews

Product Details

ISBN-13:
9781107630024
Publisher:
Cambridge University Press
Publication date:
05/31/2013
Series:
Econometric Society Monographs Series , #52
Pages:
280
Product dimensions:
5.90(w) x 8.90(h) x 0.80(d)

Meet the Author

Andrew Harvey is Professor of Econometrics at the University of Cambridge and a Fellow of Corpus Christi College. He is a Fellow of the Econometric Society and of the British Academy. He has published more than one hundred articles in journals and edited volumes and is the author of three books, The Econometric Analysis of Time Series, Time Series Models, and Forecasting and Structural Time Series Models and the Kalman Filter (Cambridge University Press, 1989). He is one of the developers of the STAMP computer package.

Customer Reviews

Average Review:

Post to your social network

     

Most Helpful Customer Reviews

See all customer reviews