國立虎尾科技大學 |

Nonlinear time series models in empirical finance /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/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:	Time-series analysis. -
電子資源:	https://doi.org/10.1017/CBO9780511754067
ISBN:	9780511754067 (ebook)

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:

528412
Time-series analysis.

LC Class. No.: HG106 / .F73 2000

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

Nonlinear time series models in empirical finance /
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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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