LEADER 03292nam 22006372 450 001 9910454677903321 005 20151005020621.0 010 $a1-107-11898-0 010 $a1-280-15463-2 010 $a0-511-11827-9 010 $a0-511-15217-5 010 $a0-511-32333-6 010 $a0-511-75406-X 010 $a0-511-04932-3 035 $a(CKB)111056485651480 035 $a(EBL)201447 035 $a(OCoLC)437063063 035 $a(SSID)ssj0000211968 035 $a(PQKBManifestationID)11169168 035 $a(PQKBTitleCode)TC0000211968 035 $a(PQKBWorkID)10136541 035 $a(PQKB)10633966 035 $a(UkCbUP)CR9780511754067 035 $a(MiAaPQ)EBC201447 035 $a(Au-PeEL)EBL201447 035 $a(CaPaEBR)ebr2000895 035 $a(CaONFJC)MIL15463 035 $a(EXLCZ)99111056485651480 100 $a20100422d2000|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNonlinear time series models in empirical finance /$fPhilip Hans Franses, Dick van Dijk$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2000. 215 $a1 online resource (xvi, 280 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a0-521-77965-0 311 $a0-521-77041-6 320 $aIncludes bibliographical references (p. 254-271) and index. 327 $aCover; Half-title; Title; Copyright; Dedication; Contents; Figures; Tables; Preface; 1 Introduction; 2 Some concepts in time series analysis; 3 Regime-switching models for returns; 4 Regime-switching models for volatility; 5 Artificial neural networks for returns; 6 Conclusions; Bibliography; Author index; Subject index 330 $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. 606 $aFinance$xMathematical models 606 $aTime-series analysis 615 0$aFinance$xMathematical models. 615 0$aTime-series analysis. 676 $a332/.01/5118 700 $aFranses$b Philip Hans$f1963-$0252841 702 $aDijk$b Dick van 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910454677903321 996 $aNonlinear time series models in empirical finance$92489428 997 $aUNINA