Estimating Persistence in the Volatility of Asset Returns with Signal Plus Noise Models
18 Pages Posted: 14 Jul 2010
Date Written: May 1, 2010
This paper examines the degree of persistence in the volatility of financial time series using a Long Memory Stochastic Volatility (LMSV) model. Specifically, it employs a Gaussian semiparametric (or local Whittle) estimator of the memory parameter, based on the frequency domain, proposed by Robinson (1995a), and shown by Arteche (2004) to be consistent and asymptotically normal in the context of signal plus noise models. Daily data on the NASDAQ index are analysed. The results suggest that volatility has a component of long- memory behaviour, the order of integration ranging between 0.3 and 0.5, the series being therefore stationary and mean-reverting.
Keywords: Fractional Integration, Long Memory, Stochastic Volatility, Asset Returns
JEL Classification: C13, C22
Suggested Citation: Suggested Citation