03323nam 22006492 450 991082324010332120151005020621.01-107-11898-01-280-15463-20-511-11827-90-511-15217-50-511-32333-60-511-75406-X0-511-04932-3(CKB)111056485651480(EBL)201447(OCoLC)437063063(SSID)ssj0000211968(PQKBManifestationID)11169168(PQKBTitleCode)TC0000211968(PQKBWorkID)10136541(PQKB)10633966(UkCbUP)CR9780511754067(Au-PeEL)EBL201447(CaPaEBR)ebr2000895(CaONFJC)MIL15463(MiAaPQ)EBC201447(PPN)26136233X(EXLCZ)9911105648565148020100422d2000|||| uy| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierNonlinear time series models in empirical finance /Philip Hans Franses, Dick van Dijk[electronic resource]Cambridge :Cambridge University Press,2000.1 online resource (xvi, 280 pages) digital, PDF file(s)Title from publisher's bibliographic system (viewed on 05 Oct 2015).0-521-77965-0 0-521-77041-6 Includes bibliographical references (p. 254-271) and index.Cover; 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 indexAlthough 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.FinanceMathematical modelsTime-series analysisFinanceMathematical models.Time-series analysis.332/.01/5118Franses Philip Hans1963-252841Dijk Dick vanUkCbUPUkCbUPBOOK9910823240103321Nonlinear time series models in empirical finance4094523UNINA