Non-Linear Time Series Models

Descripción

This item is printed on demand - it takes 3-4 days longer - Neuware -In contrast to the traditional time series analysis, which focuses on the modeling based on the first two moments, the nonlinear GARCH models specifically take the effect of the higher moments into modeling consideration. This helps to explain and model volatility especially in financial time series. The GARCH models are able to capture financial characteristics such as volatility clustering, heavy tails and asymmetry. In much of the literature available for the GARCH models, the methods of estimating parameters include the MLE,GMM and LSE which have distributional and optimality limitations. In this book, the Optimal Estimating Function(EF) based techniques are derived for the GARCH models. The EF incorporate the Skewness and the Kurtosis moments which are common in financial data. It is shown using simulations that the Estimating Function (EF) method competes reasonably well with the MLE method especially for the non-normal data and hence provides an alternative estimation technique.Financial analysts, Econometricians and Time series scholars will find this book important in teaching and in risk computation. 120 pp. Englisch.

N° de ref. del artículo 9783659302015

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Detalles bibliográficos

Título

Non-Linear Time Series Models

Editor

LAP LAMBERT Academic Publishing Nov 2012

Fecha de publicación

2012

Idioma

Inglés

ISBN 10

3659302015

ISBN 13

9783659302015

Encuadernación

Taschenbuch

Estado

Neu

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