Simulating a Class of Stationary Gaussian Processes Using the Davies-Harte Algorithm, with Application to Long Memory Processes

We demonstrate that the fast and exact Davies-Harte algorithm is valid for simulating a certain class of stationary Gaussian processes - those with a negative auto-covariance sequence for all non-zero lags. The result applies to well known classes of long memory processes: Gaussian fractionally differenced (FD) processes, fractional Gaussian noise (fGn) and the nonstationary fractional Brownian Motion (fBm)

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Year of publication:	2004
Authors:	Craigmile, Peter F.
Publisher:	[S.l.] : SSRN
Subject:	Stochastischer Prozess \| Stochastic process \| Simulation \| Zeitreihenanalyse \| Time series analysis \| Theorie \| Theory \| ARMA-Modell \| ARMA model \| Algorithmus \| Algorithm

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Extent:	1 Online-Ressource (7 p)
Type of publication:	Book / Working Paper
Language:	English
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014075123