Nonlinear time series models in empirical finance:

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...

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Bibliographic Details
Main Author: Franses, Philip Hans 1963- (Author)
Format: Electronic eBook
Language:English
Published: Cambridge Cambridge University Press 2000
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Online Access:BSB01
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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, 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
Item Description:Title from publisher's bibliographic system (viewed on 05 Oct 2015)
Physical Description:1 online resource (xvi, 280 pages)
ISBN:9780511754067
DOI:10.1017/CBO9780511754067

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