Propagation of Memory Parameter from Durations to Counts

Abstract

We establish sufficient conditions on durations that are stationary with finite variance and memory parameter d 2 [0; 1=2) to ensure that the corresponding counting process N(t) satisfies VarN(t) » Ct2d+1 (C > 0) as t ! 1, with the same memory parameter d 2 [0; 1=2) that was assumed for the durations. Thus, these conditions ensure that the memory in durations propagates to the same memory parameter in counts and therefore in realized volatility. We then show that any Autoregressive Conditional Duration ACD(1,1) model with a sufficient number of finite moments yields short memory in counts, while any Long Memory Stochastic Duration model with d > 0 and all finite moments yields long memory in counts, with the same d. Finally, we present a result implying that the onlyway for a series of counts aggregated over a long time period to have nontrivial autocorrelation is for the short-term counts to have long memory. In other words, aggregation ultimately destroys allautocorrelation in counts, if and only if the counts have short memory.

Keywords: Long Memory Stochastic Duration, Autoregressive Conditional Duration, Rosenthal type Inequality