TY  - BOOK
AU  - Deo,Rohit
AU  - Hurvich,Clifford M.
AU  - Soulier,Philippe
AU  - Wang,Yi
TI  - Conditions for the propagation of memory parameter from durations to counts and realized volatility
SN  - 02664666
AV  - HB139.T52 ECO 
PY  - 2009///
CY  - Cambridge
PB  - Cambridge University Press
KW  - Time series analysis
KW  - Estimation theory
KW  - Volatility
KW  - Duration analysis
N2  - We 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
UR  - https://doi.org/10.1017/S0266466608090294
ER  -