000			01617nam a22002775i 4500
001			66681
005			20160907153628.0
010			_a978-3-8348-9778-7 _dcompra
090			_a66681
100			_a20150401d2010 k||y0pory50 ba
101			_aeng
102			_aDE
200			_aBootstrapping stationary ARMA-GARCH models _bE-book _fKenichi Shimizu
210			_aWiesbaden _cVieweg+Teubner _d2010
215			_a148 p. _cil.
300			_aColocação: Online
303			_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.
606			_aBootstrap (Estatística) _98276
680			_aQA276.8
700			_aShimizu _bKenichi _98280
801			_aPT _gRPC
856			_uhttp://dx.doi.org/10.1007/978-3-8348-9778-7
942			_2lcc _cF _n0