000			01864nam a22002417a 4500
003			ZW-GwMSU
005			20240402142202.0
008			240402b |||||||| |||| 00| 0 eng d
040			_aMSU _bEnglish _cMSU _erda
050			_aHB139.T52 ECO
100	1		_aHalunga Andreea G _eauthor
245	1	0	_aFirst-order asymptotic theory for parametric misspecification tests of garch models _cby Andreea G. Halunga and Chris D. Orme
264	1		_aCambridge : _bCambridge University Press _c2009
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_aEconomic theory _vVolume 25, number 2
520			_aThis paper develops a framework for the construction and analysis of parametric misspecification tests for generalized autoregressive conditional heteroskedastic (GARCH) models, based on first-order asymptotic theory. The principal finding is that estimation effects from the correct specification of the conditional mean (regression) function can be asymptotically nonnegligible. This implies that certain procedures, such as the asymmetry tests of Engle and Ng (1993, Journal of Finance 48, 1749–1777) and the nonlinearity test of Lundbergh and Teräsvirta (2002, Journal of Econometrics 110, 417–435), are asymptotically invalid. A second contribution is the proposed use of alternative tests for asymmetry and/or nonlinearity that, it is conjectured, should enjoy improved power properties. A Monte Carlo study supports the principal theoretical findings and also suggests that the new tests have fairly good size and very good power properties when compared with the Engle and Ng (1993) and Lundbergh and Teräsvirta (2002) procedures.
650			_aParametric misspecification tests
700			_aOrme, Chris D. _eco-author
856			_u10.1017/S0266466608090129
942			_2lcc _cJA
999			_c164615 _d164615