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Activity Number:
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146
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economics Statistics Section
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| Abstract - #309669 |
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Title:
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Long-Memory Parameter Estimation in Time Series and Its Connection to fBm
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Author(s):
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Michael Levine*+ and Frederi Viens and Soledad Torres
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Companies:
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Purdue University and Purdue University and Universidad de Valparaiso
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Address:
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Dept of Statistics, West Lafayette, IN, 47907,
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Keywords:
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Hurst parameter ; LARCH ; Conditional MLE ; Long-memory property
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Abstract:
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We investigate several possible strategies for consistently estimating the Hurst parameter H responsible for the long-memory property in a special class of nonlinear time series ARCH-type models known as LARCH, as well as in the continuous-time fractional Brownian motion (fBm). Conditional MLE estimation method and a local Whittle-type estimation procedure for this parameter are discussed. The conditional MLE is proved to be consistent and a Portmanteau-type test for model validation is established. A specially designed conditional maximum likelihood method for estimating the fBm's Hurst parameter is proposed. In keeping with the popular financial interpretation of ARCH-type models, all estimators are based only on observation of the returns of the model and not on the volatilities.
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