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ON THE LOG PERIODOGRAM REGRESSION ESTIMATOR OF THE MEMORY PARAMETER IN LONG MEMORY STOCHASTIC VOLATILITY MODELS

机译:关于长记忆随机波动模型中记忆参数的对数周期回归估计

摘要

We consider semiparametric estimation of the memory parameter in a long memorystochastic volatility model. We study the estimator based on a log periodogramregression as originally proposed by Geweke and Porter-Hudak (1983,Journal of Time Series Analysis 4, 221 238). Expressions for the asymptotic biasand variance of the estimator are obtained, and the asymptotic distribution is shownto be the same as that obtained in recent literature for a Gaussian long memoryseries. The theoretical result does not require omission of a block of frequenciesnear the origin. We show that this ability to use the lowest frequencies is particularlydesirable in the context of the long memory stochastic volatility model.
机译:我们考虑了长记忆随机波动率模型中记忆参数的半参数估计。我们研究了基于对数周期图回归的估计量,该对数最初由Geweke和Porter-Hudak提出(1983年,时间序列分析杂志4,221 238)。得到了估计量的渐近偏差和方差的表达式,并且渐近分布与高斯长记忆级数的最新文献表明是相同的。理论结果不需要在原点附近省略一个频率块。我们表明,在长记忆随机波动率模型的背景下,使用最低频率的能力特别理想。

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