000 02341nam a22003617a 4500
001 sulb-eb0015603
003 BD-SySUS
005 20160405134439.0
008 120627s2013||||enk o ||1 0|eng|d
020 _a9781139540933 (ebook)
020 _z9781107034723 (hardback)
020 _z9781107630024 (paperback)
040 _aUkCbUP
_beng
_erda
_cUkCbUP
050 0 0 _aHB139
_b.H369 2013
082 0 0 _a330.01/5195
_223
100 1 _aHarvey, Andrew C.,
_eauthor.
245 1 0 _aDynamic Models for Volatility and Heavy Tails :
_bWith Applications to Financial and Economic Time Series /
_cAndrew C. Harvey.
246 3 _aDynamic Models for Volatility & Heavy Tails
264 1 _aCambridge :
_bCambridge University Press,
_c2013.
300 _a1 online resource (282 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 0 _aEconometric Society Monographs ;
_v52
500 _aTitle from publisher's bibliographic system (viewed on 04 Apr 2016).
520 _aThe volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling.
650 0 _aEconometrics
650 0 _aTime-series analysis
776 0 8 _iPrint version:
_z9781107034723
830 0 _aEconometric Society Monographs ;
_v52.
856 4 0 _uhttp://dx.doi.org/10.1017/CBO9781139540933
942 _2Dewey Decimal Classification
_ceBooks
999 _c37447
_d37447