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Semiparametric Estimation of Long-Memory Volatility Dependencies: The Role of High-Frequency Data.

机译:长内存波动依赖性的半参数估计:高频数据的作用。

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Recent empirical studies have argued that the temporal dependencies in financial market volatility are best characterized by long memory, or fractionally integrated, time series models. Meanwhile, little is known about the properties of the semiparametric inference procedures underlying much of this empirical evidence. The simulations reported in the present paper demonstrate that, in contrast to log-periodogram regression estimates for the degree of fractional integration in the mean (where the span of the data is crucially important), the quality of the inference concerning long-memory dependencies in the conditional variance is intimately related to the sampling frequency of the data. Some new estimators that succinctly aggregate the information in higher frequency returns are also proposed. The theoretical findings are illustrated through the analysis of a ten-year time series consisting of more than half-a-million intradaily observations on the Japanese Yen-U.S. Dollar exchange rate.
机译:最近的经验研究认为,金融市场波动的时间依赖性最好以长期记忆或部分整合的时间序列模型为特征。同时,关于半参数推理过程的性质了解甚少,而这些实证证据是其中的大部分。本文报道的模拟结果表明,与均值(其中数据的跨度至关重要)的对数积分回归估计的对数-周期图回归估计相反,有关长内存依赖项的推理质量条件方差与数据的采样频率密切相关。还提出了一些新的估算器,这些估算器以较高的频率回报率简要汇总了信息。通过对十年时间序列的分析来说明理论发现,该时间序列包括对日元/日元的超过50万次日内观察。美元汇率。

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