000			01922nam a22002897a 4500
003			ZW-GwMSU
005			20240325100936.0
008			240325b |||||||| |||| 00| 0 eng d
022			_a02664666
040			_aMSU _bEnglish _cMSU _erda
050	0	0	_aHB139.T52 ECO
100	1		_aDeo, Rohit _eauthor
245	1	0	_aConditions for the propagation of memory parameter from durations to counts and realized volatility _ccreated by Rohit Deo , Clifford M. Hurvich , Philippe Soulier and Yi Wang
264	1		_aCambridge: _bCambridge University Press, _c2009.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_aEconometric theory _vVolume 25, number 3,
520	3		_aWe establish sufficient conditions on durations that are stationary with finite variance and memory parameter to ensure that the corresponding counting process N(t) satisfies Var N(t) ~ Ct2d+1 (C > 0) as t → ∞, with the same memory parameter that was assumed for the durations. Thus, these conditions ensure that the memory parameter in durations propagates to the same memory parameter in the counts. We then show that any autoregressive conditional duration ACD(1,1) model with a sufficient number of finite moments yields short memory in counts, whereas any long memory stochastic duration model with d > 0 and all finite moments yields long memory in counts, with the same d. Finally, we provide some results about the propagation of long memory to the empirically relevant case of realized variance estimates affected by market microstructure noise contamination.
650			_aTime series analysis _vEstimation theory _xVolatility
650			_aDuration analysis
700	1		_aHurvich, Clifford M. _eco author
700	1		_aSoulier, Philippe _eco author
700	1		_aWang, Yi _eco author
856			_uhttps://doi.org/10.1017/S0266466608090294
942			_2lcc _cJA
999			_c164531 _d164531