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Activity Number:
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109
- Time Series and Forecasting
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2018 : 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 #327177
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Title:
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Goodness of Fit Statistics Based on Quantile Periodogram for Time Series with Nonlinear Dynamic Volatility
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Author(s):
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Ta-Hsin Li*
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Companies:
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IBM T. J. Watson Research Center
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Keywords:
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time series;
goodness of fit;
quantile regression;
spectral analysis;
volatility;
detection
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Abstract:
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Nonlinear dynamic volatility has been observed in many financial and econometric time series. The recently proposed quantile periodogram offers an alternative way to examine this phenomena in the frequency domain. The quantile periodogram is constructed by applying the powerful technique of quantile regression with harmonic regressors to time series data. This paper presents a number of goodness of fit statistics based on the quantile periodogram for detection and modeling of such nonlinear dynamic volatility. The usefulness of these statistics are demonstrated with real financial data.
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Authors who are presenting talks have a * after their name.
