Estimation of the Long-Memory Stochastic Volatility Model Parameters that is Robust to Level Shifts and Deterministic Trends
AbstractI provide conditions under which the trimmed FDQML estimator, advanced by McCloskey (2010) in the context of fully parametric short-memory models, can be used to estimate the long-memory stochastic volatility model parameters in the presence of additive low-frequency contamination in log-squared returns. The types of lowfrequency contamination covered include level shifts as well as deterministic trends. I establish consistency and asymptotic normality in the presence or absence of such lowfrequency contamination under certain conditions on the growth rate of the trimming parameter. I also provide theoretical guidance on the choice of trimming parameter by heuristically obtaining its asymptotic MSE-optimal rate under certain types of lowfrequency contamination. A simulation study examines the finite sample properties of the robust estimator, showing substantial gains from its use in the presence of level shifts. The finite sample analysis also explores how different levels of trimming affect the parameter estimates in the presence and absence of low-frequency contamination and long-memory.
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Bibliographic InfoPaper provided by Brown University, Department of Economics in its series Working Papers with number 2012-17.
Date of creation: 2012
Date of revision:
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Postal: Department of Economics, Brown University, Providence, RI 02912
stochastic volatility; frequency domain estimation; robust estimation; spurious persistence; long-memory; level shifts; structural change; deterministic trends;
Other versions of this item:
- Adam McCloskey, 2013. "Estimation of the long-memory stochastic volatility model parameters that is robust to level shifts and deterministic trends," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 285-301, 05.
- NEP-ALL-2012-11-17 (All new papers)
- NEP-ECM-2012-11-17 (Econometrics)
- NEP-ETS-2012-11-17 (Econometric Time Series)
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