Estimating Persistence in the Volatility of Asset Returns with Signal Plus Noise Models
AbstractThis 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.
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Bibliographic InfoPaper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 1006.
Length: 15 p.
Date of creation: 2010
Date of revision:
Fractional integration; long memory; stochastic volatility; asset returns;
Other versions of this item:
- Guglielmo Maria Caporale & Luis A. Gil‐Alana, 2012. "Estimating persistence in the volatility of asset returns with signal plus noise models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 23-30, 01.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-06-04 (All new papers)
- NEP-ECM-2010-06-04 (Econometrics)
- NEP-ETS-2010-06-04 (Econometric Time Series)
- NEP-MST-2010-06-04 (Market Microstructure)
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