Non-Linear Time Series Models in Empirical Finance

Philip Hans Franses

ISBN 10: 0521770416 / ISBN 13: 9780521770415

Editore: Cambridge University Press, 2000

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - 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, first published in 2000, 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.

Codice articolo 9780521770415

Dati bibliografici

• Titolo: Non-Linear Time Series Models in Empirical Finance
• Editore: Cambridge University Press
• Data di pubblicazione: 2000
• Lingua: Inglese
• ISBN 10: 0521770416
• ISBN 13: 9780521770415
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 260 L x 183 A x 21 P (millimetri) 260 millimetri di larghezza per 183 millimetri di altezza per 21 millimetri di profondità
• Peso: 759 grammi

Descrizione articolo

Riassunto

This 2000 volume reviews non-linear time series models, and their applications to financial markets.

Descrizione del libro

An accessible guide to one of the fastest growing areas in financial analysis by one of Europes's leading teaching and researching teams, first published in 2000. This classroom-tested advanced undergraduate and graduate textbook provides an in-depth treatment of non-linear models, including regime-switching and artificial neural networks.