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Nonlinear time series models in empi...

Dijk, Dick van,

Nonlinear time series models in empirical finance /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	332/.01/5118
書名/作者:	Nonlinear time series models in empirical finance // Philip Hans Franses, Dick van Dijk.
作者:	Franses, Philip Hans,
其他作者:	Dijk, Dick van,
面頁冊數:	1 online resource (xvi, 280 pages) : : digital, PDF file(s).
附註:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
標題:	Finance - Mathematical models.
標題:	Time-series analysis.
ISBN:	9780511754067 (ebook)
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1017/CBO9780511754067

Nonlinear time series models in empirical finance /
Franses, Philip Hans,1963-

Nonlinear time series models in empirical finance /Philip Hans Franses, Dick van Dijk. - 1 online resource (xvi, 280 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

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.

ISBN: 9780511754067 (ebook)Subjects--Topical Terms:

342179
Finance
--Mathematical models.

LC Class. No.: HG106 / .F73 2000

Dewey Class. No.: 332/.01/5118

Nonlinear time series models in empirical finance /
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500 $a Title from publisher's bibliographic system (viewed on 05 Oct 2015).
520 $a 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.
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