Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/102621
Authors: 
McCloskey, Adam
Year of Publication: 
2012
Series/Report no.: 
Working Paper, Brown University, Department of Economics 2012-17
Abstract: 
I 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.
Subjects: 
stochastic volatility
frequency domain estimation
robust estimation
spurious persistence
long-memory
level shifts
structural change
deterministic trends
JEL: 
C58
C22
C13
C18
Document Type: 
Working Paper

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