Non-Linear Time Series Models in Empirical Finance

by Philip Hans Franses , Dick van Dijk

• ISBN13: 9780521779654
• ISBN10: 0521779650
• Format: Paperback
• Copyright: 2000-09-04
• Publisher: Cambridge University Press

Summary

Although many of the models commonly used in empirical finance are linear, the nature of financial data suggests that non-linear models are more appropriate for forecasting and accurately describing returns and volatility. The enormous number of non-linear time series models appropriate for modeling and forecasting economic time series models makes choosing the best model for a particular application daunting. This classroom-tested advanced undergraduate and graduate textbook - the most up to-date and accessible guide available - provides a rigorous treatment of recently developed non-linear models, including regime-switching and artificial neural networks. The focus is on the potential applicability for describing and forecasting financial asset returns and their associated volatility. The models are analysed in detail and are not treated as 'black boxes'. Illustrated using a wide range of financial data, drawn from sources including the financial markets of Tokyo, London and Frankfurt.

Table of Contents

List of figures	ix
List of tables	xi
Preface	xv
Introduction	1	(19)
Introduction and outline of the book	1	(4)
Typical features of financial time series	5	(15)
Some concepts in time series analysis	20	(49)
Preliminaries	20	(7)
Empirical specification strategy	27	(17)
Forecasting returns with linear models	44	(7)
Unit roots and seasonality	51	(10)
Aberrant observations	61	(8)
Regime-switching models for returns	69	(66)
Representation	71	(12)
Estimation	83	(17)
Testing for regime-switching nonlinearity	100	(8)
Diagnostic checking	108	(9)
Forecasting	117	(8)
Impulse response functions	125	(7)
On multivariate regime-switching models	132	(3)
Regime-switching models for volatility	135	(71)
Representation	136	(21)
Testing for GARCH	157	(13)
Estimation	170	(12)
Diagnostic checking	182	(5)
Forecasting	187	(10)
Impulse response functions	197	(3)
On multivariate GARCH models	200	(6)
Artificial neural networks for returns	206	(45)
Representation	207	(8)
Estimation	215	(7)
Model evaluation and model selection	222	(12)
Forecasting	234	(3)
ANNs and other regime-switching models	237	(8)
Testing for nonlinearity using ANNs	245	(6)
Conclusions	251	(3)
Bibliography	254	(18)
Author index	272	(5)
Subject index	277