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Activity Number:
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415
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #303485 |
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Title:
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Statistical Inference for Volatility Component Models
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Author(s):
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Fangfang Wang*+ and Eric Ghysels
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Companies:
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The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
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Address:
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Dept of Statistics and Operations Research, Chapel Hill, NC, 27599,
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Keywords:
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volatility component model ; GARCH ; MIDAS ; stationarity ; ergodicity
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Abstract:
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The volatility component models have received much attention recently, not only because of their ability to capture complex dynamics via a parsimonious parameter structure, but also because it is believed that they can well handle structural breaks or nonstationarities in asset price volatility. The paper studies the distributional properties of various volatility component models. Sufficient conditions for the existence or/and uniqueness of (strictly) stationary (ergodic) solutions with mixing property to the volatility component models are derived. Hence, the paper revisits the component models from a statistical perspective and attempts to explore the stationarity and mixing properties of the underlying processes. We also look into the sampling behavior of the maximum likelihood estimates and establish their local consistency and asymptotic normality.
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