000			02283nam a22003975i 4500
001			978-3-8348-9778-7
003			DE-He213
005			20140220084553.0
007			cr nn 008mamaa
008			101031s2010 gw | s |||| 0|eng d
020			_a9783834897787 _9978-3-8348-9778-7
024	7		_a10.1007/978-3-8348-9778-7 _2doi
050		4	_aQA1-939
072		7	_aPB _2bicssc
072		7	_aMAT000000 _2bisacsh
082	0	4	_a510 _223
100	1		_aShimizu, Kenichi. _eauthor.
245	1	0	_aBootstrapping Stationary ARMA-GARCH Models _h[electronic resource] / _cby Kenichi Shimizu.
264		1	_aWiesbaden : _bVieweg+Teubner, _c2010.
300			_a148 p. 12 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aBootstrap Does not Always Work -- Parametric AR(p)-ARCH(q) Models -- Parametric ARMA(p, q)- GARCH(r, s) Models -- Semiparametric AR(p)-ARCH(1) Models.
520			_aBootstrap technique is a useful tool for assessing uncertainty in statistical estimation and thus it is widely applied for risk management. Bootstrap is without doubt a promising technique, however, it is not applicable to all time series models. A wrong application could lead to a false decision to take too much risk. Kenichi Shimizu investigates the limit of the two standard bootstrap techniques, the residual and the wild bootstrap, when these are applied to the conditionally heteroscedastic models, such as the ARCH and GARCH models. The author shows that the wild bootstrap usually does not work well when one estimates conditional heteroscedasticity of Engle’s ARCH or Bollerslev’s GARCH models while the residual bootstrap works without problems. Simulation studies from the application of the proposed bootstrap methods are demonstrated together with the theoretical investigation.
650		0	_aMathematics.
650	1	4	_aMathematics.
650	2	4	_aMathematics, general.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783834809926
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-8348-9778-7
912			_aZDB-2-SMA
999			_c113032 _d113032