000			02199nam a22003498i 4500
001			CR9780511754067
003			UkCbUP
005			20200124160251.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			100422s2000||||enk o ||1 0|eng|d
020			_a9780511754067 (ebook)
020			_z9780521770415 (hardback)
020			_z9780521779654 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aHG106 _b.F73 2000
082	0	0	_a332/.01/5118 _221
100	1		_aFranses, Philip Hans, _d1963- _eauthor.
245	1	0	_aNonlinear time series models in empirical finance / _cPhilip Hans Franses, Dick van Dijk.
264		1	_aCambridge : _bCambridge University Press, _c2000.
300			_a1 online resource (xvi, 280 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520			_aAlthough 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.
650		0	_aFinance _xMathematical models.
650		0	_aTime-series analysis.
700	1		_aDijk, Dick van, _eauthor.
776	0	8	_iPrint version: _z9780521770415
856	4	0	_uhttps://doi.org/10.1017/CBO9780511754067
999			_c519502 _d519500